Machine Learning with Python Training in Singapore, Singapore

Know how Statistical Modeling relates to Machine Learning

  • 48 hours of Instructor led Training
  • Comprehensive Hands-on with Python
  • Covers Unsupervised learning algorithms such as K-means clustering techniques
  • Get introduced to deep learning techniques

Description

With so many opportunities on the horizon, a career as a Machine Learning Engineer can be both satisfying and rewarding. A good workshop, such as the one offered by KnowledgeHut, can lead you on the right path towards becoming a machine learning expert.

So what is Machine Learning? Machine learning is an application of Artificial Intelligence which trains computers and machines to predict outcomes based on examples and previous experiences, without the need of explicit programming.

Our Machine learning course will help you to solve data problems using major Machine Learning algorithms, which includes Supervised Learning, Unsupervised Learning, Reinforcement Learning and Semi-supervised Learning algorithms. It will help you to understand and learn:

  • The basic concepts of the Python Programming language
  • About Python libraries (Scipy, Scikit-Learn, TensorFlow, Numpy, Pandas,)
  • The data structure of Python
  • Machine Learning Techniques
  • Basic Descriptive And Inferential Statistics before advancing to serious Machine learning development.
  • Different stages of Data Exploration/Cleaning/Preparation in Python

The Machine Learning Course with Python by KnowledgeHut is a 48 hour, instructor-led live training sessions course, with 80 hours of MCQs and assignments. It also includes 45 hours of hands-on practical session, along with 10 live projects.

Why Learn Machine Learning from Knowledgehut?

Our Machine Learning course with Python will help you get hands-on experience of the following:

  1. Learn to implement statistical operations in Excel.
  2. Get a taste of how to start work with data in Python.
  3. Understand various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.
  4. Learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies.
  5. Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Real Life Case Study on K-means Clustering.
  6. Learn about Decision Trees for regression & classification problems through a real-life case study.
  7. Get knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index, CHAID.
  8. Learn the implementation of Association Rules. You will learn to use the Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Further, you will learn what are UBCF and IBCF and how they are used in Recommender Engines.

What is Machine Learning?

Machine Learning is an application of Artificial Intelligence that allows machines and computers to learn automatically to predict outcomes from examples and experiences, without there being any need for explicit programming. As the name suggests, it gives machines and computers the ability to learn, making them similar to humans.

The concept of machine learning is quite simple. Instead of writing code, data is fed to a generic algorithm. The generic algorithm/machine will build a logic which will be based on the data provided. The provided data is termed as ‘training data’ as they are used to make decisions or predictions without any program to perform the task.

Practical Definition from Credible Sources:

1) Stanford defines Machine Learning as:

“Machine learning is the science of getting computers to act without being explicitly programmed.”

2) Nvidia defines Machine Learning as:

“Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world.”

3) McKinsey & Co. defines Machine Learning as:

“Machine learning is based on algorithms that can learn from data without relying on rules-based programming.”

4) The University of Washington defines Machine Learning as:

“Machine learning algorithms can figure out how to perform important tasks by generalizing from examples.”

5) Carnegie Mellon University defines Machine Learning as:

“The field of Machine Learning seeks to answer the question “How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?”

Origin of Machine Learning through the years

Today, algorithms of machine learning enable computers and machines to interact with humans, write and publish sport match reports, autonomously drive cars, and find terrorist suspects as well. Let’s peek through the origins of machine learning and its recent milestones.

1950:
Alan Turing created a ‘Turing Test’ in order to determine if a computer has real intelligence. A computer should fool a human into believing that it is also a human to pass the test.

1952:
The first computer learning program was written by Arthur Samuel. The program was a game of checkers. The more that the IBM computer played the game, the more it improved at the game, as it studied the winning strategies and incorporated those moves into programs.

1957:
The first neural network for computers was designed by Frank Rosenblatt. It stimulates the thought process of the human brain.

1967:
The ‘nearest neighbour’ was written. It allowed computers to use basic pattern recognition.

1981:
Explanation-Based Learning was introduced, where a computer analyses the training data and creates a general rule which it can follow by discarding the unimportant data.

1990:
The approach towards the work on machine learning changes from a knowledge-driven approach to machine-driven approach. Programs were now created for computers to analyze a large amount of data and obtain conclusions from the results.

1997:
IBM’s Deep Blue beat the world champion in a game of chess.

2006:
Geoffrey Hinton coined the term ‘deep learning’ that explained new algorithms that let the computer distinguish objects and texts in videos and images.

2010:
The Microsoft Kinect was released, which tracked 20 human features at a rate of 30 times per second. This allowed people to interact with computers via gestures and movements.

2011:
IBM’s Watson beat its human competitors at Jeopardy.

2011:
Google Brain was developed. It discovered and categorized objects similar to the way a cat does.

2012:
Google’s X Labs developed an algorithm that browsed YouTube videos and identified those videos that contained cats.

2014:
Facebook introduced DeepFace. It is an algorithm that recognizes and verifies individuals on photos.

2015:
Microsoft launched the Distributed Machine Learning Toolkit, which distributed machine learning problems across multiple computers.

2016:
An artificial intelligence algorithm by Google, AlphaGo, beat a professional player at a Chinese board game Go.

How does Machine Learning work?

The algorithm of machine learning is trained using a training data set so that a model can be created. With the introduction of any new input data to the ML algorithm, a prediction is made based on the model.

The accuracy of the prediction is checked and if the accuracy is acceptable, the ML algorithm is deployed. For cases where accuracy is not acceptable, the Machine Learning algorithm is trained again with supplementary training data set.

There are various other factors and steps involved as well. This is just an example of the process.

Advantages of Machine Learning

  1. It is used in multifold applications such as financial and banking sectors, healthcare, publishing, retail, social media, etc.
  2. Machine learning can handle multi-variety and multi-dimensional data in an uncertain or dynamic environment.
  3. Machine learning algorithms are used by Facebook and Google to push advertisements which are based on the past search behaviour of a user.
  4. In large and complex process environments, Machine Learning has made tools available which provide continuous improvement in quality.
  5. Machine learning has reduced the time cycle and has led to the efficient utilization of resources.
  6. Source programs like Rapidminer have helped increase the usability of algorithms for numerous applications.    

Industries using Machine Learning

Various industries work with Machine Learning technology and have recognized its value. It has helped and continues to help organisations to work in a more effective manner, as well as gain an advantage over their competitors.

  1. Financial services:

Machine Learning technology is used in the financial industry due to two key reasons: to prevent fraud and to identify important insights in data. This helps them in deciding on investment opportunities, that is, helps the investors with the process of trading, as well as identify clients with high-risk profiles.

  1. Government:

Machine learning has various sources of data that can be drawn used for insights. It also helps in detecting fraud and minimizes identity theft.

  1. Health Care:

Machine Learning in the health care sector has introduced wearable devices and sensors that use data to assess a patient’s health in real time, which might lead to improved treatment or diagnosis.

  1. Oil and Gas:

There are numerous use cases for the oil and gas industry, and it continues to expand. A few of the use cases are: finding new energy sources, predicting refinery sensor failure, analyzing minerals in the ground, etc.

  1. Retail:

Websites use Machine Learning to recommend items that you might like to buy based on your purchase history.

What is the future of Machine Learning?

Machine learning has transformed various sectors of industries including retail, healthcare, finance, etc. and continues to do so in other fields as well. Based on the current trends in technology, the following are a few predictions that have been made related to the future of Machine Learning.

  1. Personalization algorithms of Machine Learning offer recommendations to users and attract them to complete certain actions. In future, the personalization algorithms will become more fine-tuned, which will result in more beneficial and successful experiences.
  2. With the increase in demand and usage for Machine Learning, the usage of Robots will increase as well.
  3. Improvements in unsupervised machine learning algorithms are likely to be observed in the coming years. These advancements will help you develop better algorithms, which will result in faster and more accurate machine learning predictions.
  4. Quantum machine learning algorithms hold the potential to transform the field of machine learning. If quantum computers integrate to Machine Learning, it will lead to faster processing of data. This will accelerate the ability to draw insights and synthesize information.

What You Will Learn

PREREQUISITES

For Machine Learning, it is important to have sufficient knowledge of at least one coding language. Python being a minimalistic and intuitive coding language becomes a perfect choice for beginners.

Sign up for this comprehensive course and learn from industry experts who will handhold you through your learning journey, and earn an industry-recognized Machine Learning Certification from KnowledgeHut upon successful completion of the Machine Learning course.

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who Should Attend?

  • If you are interested in the field of machine learning and want to learn essential machine learning algorithms and implement them in real life business problem
  • If you're a Software or Data Engineer interested in learning the fundamentals of quantitative analysis and machine learning

Knowledgehut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.

Curriculum

Learning Objectives:

In this module, you will visit the basics of statistics like mean (expected value), median and mode. You will understand the distribution of data in terms of variance, standard deviation and interquartile range; and explore data and measures and simple graphics analyses.Through daily life examples, you will understand the basics of probability. Going further, you will learn about marginal probability and its importance with respect to data science. You will also get a grasp on Baye's theorem and conditional probability and learn about alternate and null hypotheses.

Topics:
  • Statistical analysis concepts
  • Descriptive statistics
  • Introduction to probability and Bayes theorem
  • Probability distributions
  • Hypothesis testing & scores
Hands-on :
Learn to implement statistical operations in Excel.
Learning Objectives:

In this module, you will get a taste of how to start work with data in Python. You will learn how to define variables, sets and conditional statements, the purpose of having functions and how to operate on files to read and write data in Python. Understand how to use Pandas, a must have package for anyone attempting data analysis in Python. Towards the end of the module, you will learn to visualization data using Python libraries like matplotlib, seaborn and ggplot.

Topics:
  • Python Overview
  • Pandas for pre-Processing and Exploratory Data Analysis
  • Numpy for Statistical Analysis
  • Matplotlib & Seaborn for Data Visualization
  • Scikit Learn

Hands-on: No hands-on

Learning Objectives :

This module will take you through real-life examples of Machine Learning and how it affects society in multiple ways. You can explore many algorithms and models like Classification, Regression, and Clustering. You will also learn about Supervised vs Unsupervised Learning, and look into how Statistical Modeling relates to Machine Learning.

Topics:
  • Machine Learning Modelling Flow
  • How to treat Data in ML
  • Types of Machine Learning
  • Performance Measures
  • Bias-Variance Trade-Off
  • Overfitting & Underfitting

Hands-on: No hands-on

Learning Objectives:

This module gives you an understanding of various optimization techniques like Batch Gradient Descent, Stochastic Gradient Descent, ADAM, RMSProp.

Topics:
  • Maxima and Minima
  • Cost Function
  • Learning Rate
  • Optimization Techniques

Hands-on: No hands-on

Learning Objectives:

In this module you will learn Linear and Logistic Regression with Stochastic Gradient Descent through real-life case studies. It covers hyper-parameters tuning like learning rate, epochs, momentum and class-balance.You will be able to grasp the concepts of Linear and Logistic Regression with real-life case studies. Through a case study on KNN Classification, you will learn how KNN can be used for a classification problem. You will further explore Naive Bayesian Classifiers through another case study, and also understand how Support Vector Machines can be used for a classification problem. The module also covers hyper-parameter tuning like regularization and a case study on SVM.

Topics:
  • Linear Regression
  • Case Study
  • Logistic Regression
  • Case Study
  • KNN Classification
  • Case Study
  • Naive Bayesian classifiers
  • Case Study
  • SVM - Support Vector Machines
  • Case Study
Hands-on:
  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.
  • This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers.
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • We receive 100s of emails & text messages everyday. Many of them are spams. We would like to classify our spam messages and send them to the spam folder. We would also not like to incorrectly classify our good messages as spam. So correctly classifying a message into spam and ham is of utmost importance. We will use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham.
  • Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set containing 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable and non-biodegradable.
Learning Objectives:

Learn about unsupervised learning technique - K-Means Clustering and Hierarchical Clustering. Real Life Case Study on K-means Clustering

Topics:
  • Clustering approaches
  • K Means clustering
  • Hierarchical clustering
  • Case Study
Hands-on :
In marketing, if you're trying to talk to everybody, you're not reaching anybody.. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns. 
Learning Objectives:

This module will teach you about Decision Trees for regression & classification problems through a real-life case study. You will get  knowledge on Entropy, Information Gain, Standard Deviation reduction, Gini Index,CHAID.The module covers basic ensemble techniques like averaging, weighted averaging & max-voting. You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting.
Going further, you will learn Random Forest with a real-life case study and learn how it helps avoid overfitting compared to decision trees.You will gain a deep understanding of the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It covers comprehensive techniques to find the optimum number of components/factors using scree plot, one-eigenvalue criterion. Finally, you will examine a case study on PCA/Factor Analysis.

Topics:
  • Decision Trees
  • Case Study
  • Introduction to Ensemble Learning
  • Different Ensemble Learning Techniques
  • Bagging
  • Boosting
  • Random Forests
  • Case Study
  • PCA (Principal Component Analysis) and Its Applications
  • Case Study
Hands-on:
  • Wine comes in various style. With the ingredient composition known, we can build a model to predict the the Wine Quality using Decision Tree (Regression Trees).
  • In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. In this case study, use AdaBoost, GBM & Random Forest on Lending Data to predict loan status. Ensemble the output and see your result perform than a single model.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights &  better modeling.
Learning Objectives: 

This module helps you to understand hands-on implementation of Association Rules. You will learn to use the Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift. Further, you will learn what are UBCF and IBCF and how they are used in Recommender Engines. The courseware covers concepts like cold-start problems.You will examine a real life case study on building a Recommendation Engine.

Topics:
  • Introduction to Recommendation Systems
  • Types of Recommendation Techniques
  • Collaborative Filtering
  • Content based Filtering
  • Hybrid RS
  • Performance measurement
  • Case Study
Hands-on:
You do not need a market research team to know what your customers are willing to buy.  Netflix is an example of this, having successfully used recommender system to recommend movies to its viewers. Netflix has estimated, that its recommendation engine is worth a yearly $1 billion.
An increasing number of online companies are using recommendation systems to increase user interaction and benefit from the same. Build Recommender System for a Retail Chain to recommend the right products to its users 

Meet your instructors

Biswanath

Biswanath Banerjee

Trainer

Provide Corporate training on Big Data and Data Science with Python, Machine Learning and Artificial Intelligence (AI) for International and India based Corporates.
Consultant for Spark projects and Machine Learning projects for several clients

View Profile

Projects

Predict Property Pricing using Linear Regression

With attributes describing various aspects of residential homes, you are required to build a regression model to predict the property prices using optimization techniques like gradient descent.

Classify good and bad customers for banks to decide on granting loans.

This dataset classifies people described by a set of attributes as good or bad credit risks. Using logistic regression, build a model to predict good or bad customers to help the bank decide on granting loans to its customers.

Classify chemicals into 2 classes, biodegradable and non-biodegradable using SVM.

Biodegradation is one of the major processes that determine the fate of chemicals in the environment. This Data set contains 41 attributes (molecular descriptors) to classify 1055 chemicals into 2 classes - biodegradable and non-biodegradable. Build Models to study the relationships between chemical structure and biodegradation of molecules and correctly classify if a chemical is biodegradable

Read More

Cluster teen student into groups for targeted marketing campaigns using Kmeans Clustering.

In marketing, if you’re trying to talk to everybody, you’re not reaching anybody. This dataset has social posts of teen students. Based on this data, use K-Means clustering to group teen students into segments for targeted marketing campaigns.

Read More

Predict quality of Wine

Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Note: These were the projects undertaken by students from previous batches.  

Learn Machine Learning

Learn Machine Learning in Singapore

From watching Netflix to managing a firm’s financial services, Machine Learning has now become an integral part of our lives. Machine learning is an application of Artificial Intelligence (AI) that focuses on the development of computer programs. Machine learning provides  systems the ability to function automatically without being programmed explicitly. It uses algorithms that can automatically learn, perform and improve the given tasks. 

Machine learning uses modules to interpret the set of exposed data and identify the hidden patterns. This involves data analysis and analytical automation. The several methods of Machine Learning are categorized as follows:

  • Supervised machine learning algorithms: These algorithms make use of past data to complete a task and apply them to the new data by making use of the labelled examples to predict the future outcomes.
    • A known dataset is fed into the system, which it is trained and it learns from. 
    • The learning algorithm is then derived from this training and  produced in the form of an inferred function that makes predictions. 
    • Such kind of algorithms provide us with results for any new inputs after they are put to sufficient learning and training.
  • Unsupervised machine learning algorithms: These algorithms make use of the information required for the training of the system. The algorithms are either not labelled or have not been classified. 
    • Unsupervised learning systems infer a function in order to describe a hidden structure from unlabelled data.
    • Such systems, while being unable to figure out the correct result, explore the available data and draw inferences from the available datasets in order to describe and identify hidden structures from unlabelled data.

Machine Learning deals with systems that use huge amounts of data to analyze and solve problems through training to get the best outcome for a task or problem. It helps humans solve the problems, without a necessity to actually know and understand what the problem is. 

  • It's easy and it works

Machines work faster than human brains. They solve problems faster than we ever can. Assuming that there are a million approaches to a problem, the machine systematically works out on it, resolves and simultaneously evaluates all the options in order to obtain the best possible outcome or result.

  • It has a wide range of applications

Machine Learning has a solution to all the practical problems that the world is looking for. Machine learning when implemented in businesses saves time, effort and money. It is more efficient, effective and appropriate. Many industries like healthcare, nursing, transport, customer service, government and financial institutions apply Machine Learning in their daily activities thereby making it an indispensable part of our society.

We are in a world always in a state of technology flux, and data is at the core of this flux, with the unique power to transform the world. Singapore is home to many leading tech companies, including AppDynamics, Paycom, Cisco Systems, Apple, NetApp, HP Inc. Expedia Group, etc. These companies are using Machine Learning to harness the immense amount of data generated every day and use it for key decisions. No matter how big or small the data is, it has the power to reshape technology and businesses. 

The National Research Foundation set up AI Singapore (AISG) to bring research institutions and start-ups in this field together back in 2017. To scale development efforts, the Singapore Government is now looking to open up access to data and AI tools so everyone can experiment with the technology. In addition to this, the government has also decided to expand public and private sectors, thereby making it business friendly.

Machine Learning in day to day life

Machine Learning is no longer confined just to a few elite scientists but has now spread far and wide in its scope and uses. Tech experts have been increasingly making use of Machine Learning over the years. Surge pricing at Uber, Wal-Mart product recommendations, Social media feeds displayed by Facebook and Instagram, Google Maps, detecting financial fraud at financial institutions etc - all these and many more functionalities are now being performed with the help of powerful Machine Learning algorithms, more particularly without human intervention. 

Every individual knowingly or unknowingly uses one or the other products of Machine Learning. In such a scenario, the idea of Machine Learning is an inevitable step that any professional, especially one in the field of Information Technology and Data Science, must know in order to become relevant. 

Benefits of learning Machine Learning in Singapore:

  1. Better job opportunities: According to a report published by Tractica, services driven by Artificial Intelligence were worth a $1.9 billion in the year 2016 and this number is expected to rise to around $19.9 billion by the end of 2025. Machine Learning is the bandwagon that every corporation in the world now looks at. With every industry in Singapore looking at Machine Learning and Artificial Intelligence, a knowledge in the same is bound to attract more and brighter career opportunities.
  2. Increased Income: The valuation of a Machine Learning expert can be compared to that of a top NFL quarterback prospect. According to a study published by SimplyHired.com, the average income of a machine learning engineer is approximately S$69,492 in Singapore.
  3. Demand for Machine Learning skills: There exists a huge gap between the demand and the availability of Machine Learning engineers. There are several companies in Singapore that are hiring Machine Learning engineer including Juvo, Micro Focus, Veracode, Tower Research Capital, Beyond Trust, SAP, Grab, Dyson, Agoda, Amazon, Biofourmis Pte Ltd., HP Inc., Dell, Salesforce, JP Morgan, Agilent Technologies, Refinitiv, etc.
  4. A shift to Machine Learning: Most of the industries around the world deal with a humongous amount of data that increases every single day. An analysis found out that companies are fast taking cognizance of this point. By gleaning insights from this data, companies look to work more efficiently, thereby gaining an edge over their competitors.

There are a number of certification courses in Singapore that will help you learn Machine Learning including:

  1. SMU Academy
  2. Tertiary courses
  3. National University of Singapore
  4. Intellipat
  5. GSTF

One of the best ways to get started with Machine Learning is to connect with other professionals. Here is a list of Machine Learning meetups in Singapore where you can connect with other Machine Learning Engineers:

  1. Next-Gen Analytics Mondays
  2. A Newbie’s Guide to Machine Learning and Data Science
  3. TensorFlow and Deep Learning Singapore
  4. Deep Learning SG
  5. Singapore Artificial Intelligence and Deep Learning

Below are some other ways to get started in Machine Learning as an absolute beginner:

  • Adjust your mindset: Don't just jump on the bandwagon. Make sure to read all related articles and adjust your mindset accordingly. 
  • Pick a process that suits you best: Choose a process that is structured and systematic and suits your way of working with problems.
  • Pick a tool: Pick a tool that best suits your level of comfort and map these onto your processes.
    • Weka Workbench is recommended for Beginners 
    • Python Ecosystem is recommended for Intermediate level learners 
    • Advanced level learners are recommended, the R Platform
  • Practice on Datasets: Choose from a host of available datasets to work on and practice the entire process of data collection and manipulation.
  • Build your own portfolio: Make use of the knowledge you have gained and demonstrate your skills in the form of a portfolio.

In Singapore, companies like Flowserve, WorldQuant, Hitachi Vantara, Hewlett Packard Enterprise, Singtel, Continental, Boston Consulting Group, PayPal, Singtel, Zendesk, Garena, Lazada, honestbee, LeadIQ, GO-JEK, Thales Group, Seek Asia, MediaTek, Google, DataRobot, EY, Procter & Gamble, Argyll Scott, etc. are looking for Machine Learning professionals with suitable experience that will help the organization make crucial marketing decisions.

Skills always play a key role in building our career. Below are some technical skills required to become a Machine Learning engineer:

  • Programming languages: Knowledge of programming languages is important to be able to grasp Machine Learning skills more thoroughly. Popular programming languages are- Python, Java, Scala etc.
  • Database skills: You should be able to analyse data available at different sources and then convert this data obtained in a format that is readable as well as compatible. So, understanding of MySQL  as well as relational databases is essential.
  • Knowledge of Machine learning frameworks: Knowledge of frameworks like TensorFlow, R, Scala, NLP, Apache Spark, etc. are important for a thorough understanding of Machine Learning concepts.
  • Mathematical skills:  It is with the help of  the mathematical algorithms and concepts that the data is processed, analyzed and used in order to form a Machine Learning model. You should have a knowledge of some of the mathematical concepts, such as Optimization, Linear algebra, Calculus of variations, Probability theory, Calculus, Bayesian Modeling, Fitting of a distribution, Probability Distributions, Hypothesis Testing, Regression and Time Series, Mathematical statistics, Statistics and Probability, Differential equations, Graph theory, etc.

Successful compilation of the project includes the following steps:

  1. Gathering data: The first step is to collect accurate data required for the project. 
  2. Cleaning and preparing data: This step involves correcting and preparing the data as raw data cannot readily be injected into our model. 
  3. Visualizing the data: This is the final step and involves exhibiting the prepared data and finding the correlation between the variables. This step helps analyse the kind of data that we have and make a good selection of model accordingly.

Algorithms are an integral part of Machine Learning. Understanding the concepts of machine learning plays a key role for any engineer.

  • Listing: Every algorithm is unique in its own way, so it is important for one to list down the related algorithms with which you wish to begin your Machine Learning journey with. Enlist all the algorithms that you wish to learn. Also list the category that the particular algorithm falls under. This particular activity will also help you build familiarity with the various classes and types of algorithms available and prepare you for what lies ahead.
  • Application: Machine Learning algorithms do not exist alone and no matter how much time you spend on theory, the best comes from the practical application and implementation of Machine Learning algorithms to data sets. Thus, apart from learning the basic concepts and theory of Machine Learning algorithms, it is also important to keep practicing Applied Machine Learning with the help of algorithms like Support Vector Machines, decision trees etc. Build confidence by applying these algorithms to various problems and data sets.
  • Description: The next logical step to be undertaken in order to gain a better understanding of Machine Learning algorithms is to explore what has already been understood about these algorithms. A thorough analysis and understanding of Machine Learning algorithms will help you build a description of these algorithms. Add more information to the descriptions through the course of your study of Machine Learning algorithms. This is quite a valuable technique to help you build up a mini algorithm encyclopedia of your own. 
  • Implementation: The implementation of Machine Learning algorithms helps you learn how it works. By implementing the algorithm yourself, you will be able to understand the micro decisions involved in the implementation of Machine Learning concepts and also understand the mathematical extensions and descriptions of the algorithm.
  • Experimentation: Once you have implemented and understood a Machine Learning algorithm, you are well trained to experiment with it. You can now use standardized data sets, control variables as well as study the functioning of algorithms in the form of a complex system in itself. Understanding the parameters at play while working on algorithms is a great way to be able to customize its working in order to suit your needs while working on a problem. Understanding the behavior of an algorithm also enables you to better scale and adapt an algorithm to suit your problem needs in the future.
  • Choosing the correct model: After visualizing the data with a good knowledge about how this data can be harvested and which model or algorithm is best suited to do so, choosing the correct model significantly determines the performance of your algorithm.
  • Train and test: We have our prepared data ready to be fed into our chosen model. The data is divided into training and testing data, we now train our model with the training data and after it is trained, we test its accuracy with the test data in which it wasn’t trained.
  • Adjust parameters: After finding how accurate the model is, we can fine tune our parameters. For example, changing the number of neurons in a neural network is an example of adjusting parameters.

Machine Learning Algorithms

The K Nearest Neighbors algorithm is a rather simplistic and uncomplicated Machine Learning algorithm. Given a totally multiclass dataset to be worked on, with the goal of predicting the class of a given data point, we can make use of the K Nearest Neighbor algorithm.

  • The primary requirement of the nearest neighbor classification is the definition of a pre-defined number, which will be stored as the value of ‘k’. This number, k, defines the number of training samples that are closest in distance to a new data point that is to be classified. 
  • The label that will be assigned to this new data point, will then, be one that has already been assigned to and defined by these neighbors. 
  • K-nearest neighbor classifiers possess a fixed user-defined constant for the number of neighbors which have to be determined.
  • These algorithms work on the concept of radius based classification. The concept behind the radius based classification is that depending on the density of the neighboring data points, all the samples are identified and classified under and inside a fixed radius. This fixed radius is a metric measure of the distances and is most popularly, the Euclidean distance between the points.
  • All these methods based on the classification of the neighbors are also known as the non-generalizing Machine Learning methods. This is majorly owing to the fact that these methods ‘remember’ all the training data that was fed into it, instead of acting on them.
  • Classification is then performed as a result of a majority vote conducted among the nearest neighbors of an unknown sample.

The K Nearest Neighbour algorithm is the simplest of all machine learning algorithms. However, in spite of its simplicity, the algorithm has proven to be very successful and useful in the solution to a huge number of regression as well as classification problems, an example of which includes character recognition as well as image analysis.

You don’t need to know any algorithms to learn Machine Learning if you just want to use the Machine Learning algorithm. There are many courses available online that helps you learn Machine Learning without having to learn any algorithm. However, if you want to use Machine Learning for innovation purpose, it is important to have basic knowledge of algorithms. 
There are various bootcamps in Singapore offering basic machine learning courses.

Machine Learning Algorithms can be classified basically into the following 3 types - 

  1. Supervised Learning: Linear Regression, Logistic Regression, Classification and Regression Trees (CART), Naïve Bayes, K-Nearest Neighbors 
  2. Unsupervised Learning: Apriori, K-Means, Principal Component Analysis (PCA) 
  3. Ensemble Learning: Bagging, Boosting

  • Supervised Learning: Using categorically classified historical data to learn the mapping function from the input variables (X) to the output variable (Y). Examples of such include:
    • Linear Regression -  The relationship between the input variable (x) and output variable (y) is expressed in the form y = a + bx
    • Logistic Regression - Logistic Regression is similar to linear regression model; the only difference is the outcome of the regression is probabilistic, rather than exact values. 
    • CART - Classification and Regression Trees (CART) is an implementation of Decision Trees. This algorithm charts the possibility of each outcome and predicts the result on the basis of defined nodes and branches. At each non-terminal node is a single input variable (x). The splitting point on that node depicts the various outcomes that can happen to that variable, and the following leaf node represents the output variable (y).
    • Naïve Bayes - This algorithm predicts the possibility of an outcome happening, given the basic value of some other variable. It works exactly on the principle of the Bayes theorem, and is considered “naive” as it makes the assumption that all variables are independent in nature. 
    • K-Nearest Neighbors - This algorithm charts the entire data set given, and after assigning a predefined value of “k” to find out the outcome for a given value of the variable, it collects “k nearest instances” of the value in the dataset and then either averages them to produce the output (for a regression model) or finds the mode of these averages (for most frequent class problem). 
  • Unsupervised Learning: In these types of problems, only the input variables are given and not the output ones. Thus, the underlying structure of the given data sets is analyzed to reveal possible associations and clusters. Examples of such algorithms include the following -
    • Apriori - This algorithm is used in various databases containing transactions to identify frequent associations of two items occurring together, and then such associations are used to predict further relationships.
    • K-Means - This algorithm groups similar data into clusters, and then associates each data point in the cluster to an “assumed” centroid of the cluster. 
    • PCA - Principal Component Analysis (PCA) makes the data space easier to visualize, by reducing the number of variables. 
  • Ensemble Learning: Groups or ensembles of learners are more likely to perform better than singular learners. These types of algorithms combine the results of each learner and then analyze them as a whole to obtain a fairly accurate representation of the actual outcome. Examples of such algorithms include the following:
    • Bagging - This algorithm is used to generate multiple datasets (based on the original one), then model the same algorithm on each to produce different outputs, which can then be compiled and performed upon to obtain the real outcome.
    • Boosting - This algorithm is similar to the above one, but it works sequentially instead of the parallel nature of bagging. Thus, each new dataset is created by learning from the previous one’s errors and miscalculations. 

The simple machine learning algorithms solve the simplest of ML problems (simple recognition). Algorithms can be selected based on the following criteria:

  • Easy to understand.
  • Easy to implement and understand the underlying principles.
  • Takes less time and resources to train and test the data as compared to high-level algorithms.

Keeping this in mind, the simplest Machine Learning algorithm for beginners is k-nearest neighbor algorithm. Below are some of the reasons why k-nearest neighbor algorithm is used extensively for solving some of the basic, but important, real-life problems:

  • It is a classification algorithm though it can be used for regression as well.
  • It classifies based on the similarity measure and is non-parametric.
  • Data set used for the training phase is labeled data (supervised learning) and the aim of the algorithm is to predict a class for an object based on its k nearest surroundings where k defines the number of neighbors.
  • Some practical and real-life examples where KNN is used are:
    • When searching in documents containing similar topics.
    • Used to detect patterns in credit card usage.
    • Vehicular number plate recognition.

Below are some ways to choose the right Machine Learning algorithm (ML):

  • Understanding the data: To find the correct algorithm, it is important to understand your data.
    • Visualize your data by plotting graphs.
    • Try to find correlation among the data which indicate strong relationships.
    • Your data is not always perfect; there can be missing data or bad data as well which can be sensitive to your model. Deal with this and clean your data.
    • Prepare your data by feature engineering to make your data ready to be injected into your model.
  • Get the intuition about the task: The next step is to understand which kind of learning will help your model complete the task at hand. There are 4 types of learning in general:
    • Supervised learning
    • Unsupervised learning
    • Semi-supervised learning
    • Reinforcement learning
  • Understand the constraints: If we don’t apply constraints to our planning while choosing algorithms, we might go ahead and choose the best tools and algorithms. But that isn’t the right approach! The best models and algorithms work on high-end machines and require high data storage and manipulation resources. Constraints can be on the hardware or software as well.
    • Data storage capacity limits the amount of data that we can store for training and testing phases.
    • Hardware constraints allow us to choose algorithms which run according to the hardware available to us. 

Greater the implementation of Machine Learning algorithms, the faster and more efficient your subsequent solutions become. Implementation of Machine Learning algorithms includes the following:

  • Select a programming language:  Select the programming language that you want to use for implementation. This decision of choosing the programming language will influence the standard libraries as well as the APIs that you are going to make use of in your implementation.
  • Select the algorithm: Once you have chosen your programming language, the next logical step is to choose the algorithm. Decide on all of the specifics of the algorithm and be as decisive and precise as possible. This includes the type of algorithm you will use, its classes, and the specific implementation and description of what you want.
  • Select the problem: Selection of the canonical problem set, that you are going to use in order to test and validate the efficiency and correctness of your algorithm implementation plays an important role. 
  • Algorithm research: Go through the books, research papers, libraries, websites and blogs that contain descriptions of the algorithm, its implementation, conceptual understanding, etc. This will give you a wider perspective of the different methodologies and uses of the algorithm.
  • Undertake unit testing: For each function of the algorithm, start developing and running unit tests. This will help you understand what to expect and purpose from each code unit of the algorithm.

Below are some of the most Essential Topics of Machine Learning one should study to become a master in ML:

  1. Decision Trees: A Decision tree is a type of a supervised learning algorithm that is used for classification problems. Some benefits of decision tree methods:
    • They are relatively simple.
    • Easy to understand, visualize and interpret.
    • They implicitly perform feature selection as well as variable screening.
    • Decision trees are not affected by non linear relationships between parameters.
    • Decision trees require minimal efforts in the direction of data preparation from the user.
    • Decision trees handle and analyze both categorical as well as numerical data.
    • Decision trees also handle problems that require multiple outputs.
  2. Support Vector Machines: Support Vector Machines are a type of classification methodology that provide a higher degree of accuracy in classification problems. Support Vector Machines can also be used in problems of regression as well. Some benefits of a Support Vector Machine:
    • Support Vector Machines provide guaranteed optimality.
    • They can be used both in Linearly Separable (Hard margin) as well as Non-linearly separable (Soft Margin) data.
  3. Naive Bayes: It is a classification technique, based on Bayes’ theorem that assumes the independence between variables. Below are some advantages of the Naive Bayes algorithm include the following:
    • It is a very simple technique of classification
    • It is a classification technique that is highly scalable
    • It requires less training data as compared to other techniques used for classification.
    • It converges quicker than other traditional discriminative models.
  4. Random Forest algorithm: The Random Forest is a collection of randomized decision trees trained using the bagging method. Some of the advantages of the Random Forest algorithm include:
    • Used for regression as well as classification problems
    • Easy to use and handy algorithm
    • Count of hyper parameters included in a random forest is not high
    • Produces a good prediction result

Machine Learning Engineer Salary in Singapore

The median salary of a Machine Learning Engineer in Singapore is $72,000/yr.

Singapore is one of the most developed islands in the world. According to International Data Corporation, 61% of organizations insist that Machine Learning & Artificial Intelligence will be among their top data initiatives in 2018 and 2019, which means ML Engineers are in good demand there.

Being the dream job for the engineering graduates in 2018, a job of a Machine learning engineer in Singapore offers various benefits such as - 

  • High Pay - Being involved in the future tech, graduating with a degree in it, understanding the potential of Machine Learning and Artificial Intelligence and having the skills to achieve such possibilities demands a good pay and that is exactly what the industry is paying now.

  • Opportunities - Machine learning engineering is one of the fastest growing jobs today. Even though data scientist is hailed as the ‘sexiest job of the 21st century’  experts now believe Machine learning engineering has the potential to surpass data scientists in terms of need and demand. As more and more companies in Singapore are adopting ML and AI, it is only natural for this field to expand exponentially.

Machine learning is not just in demand due to better salary but for also its other perks, such as -

  • Reach - In order to be an ML engineer, you would need a PhD or at least masters to show your in-depth knowledge in the field. This intensive knowledge attracts schools, colleges, workshops and professionals to invite them to share such vast data. This eventually develops a healthy network and popularity.
  • Possibilities - The future is artificial intelligence. Even today so much has changed around us and this is just the beginning of the AI age. Machine learning offers a key to unlock the door of endless possibilities.

Although there are quite many companies offering jobs to Machine Learning Engineers in Singapore, following are the prominent companies - 

  • Apple
  • Siemens Healthineers
  • Institute of Microelectronics
  • Dia&Co
  • Microsoft
  • J.P. Morgan
  • SAP
  • i2R

Machine Learning Conference in Singapore

S.NOName of the conferenceDateVenue
1.INTERPOL worldJuly 2 - 4, 2019

Marina Bay Sands

10 Bayfront Ave, Singapore 018971

2.

ICRASP – International conference on robotics, automation and signal processing

July 4 - 5, 2019

Holiday Inn Singapore Atrium 317 Outram Rd, Singapore 16907

3.

ICSESP – International conference on space electronics and signal processing

July 4 - 5, 2019

Holiday Inn Singapore Atrium 317 Outram Rd, Singapore 169075

4.

ICAIME – International conference on artificial intelligence and mechanical engineering

July 4 - 5, 2019

Holiday Inn Singapore Atrium 317 Outram Rd, Singapore 169075

5.

TBD Data council Singapore

July 17 – 18, 2019

Microsoft Operations

1 Marina Blvd, #22-01, Singapore 018989

6.

Big data and AI leaders summit

September 10 – 12, 2019

Marina Bay Sands Singapore

10 Bayfront Ave, Singapore 018956

7.

ICISPCS – International conference on intelligent signal processing and communication systems

September 10 - 11, 2019

Singapore

8.

ICCAI – International conference on computing and artificial intelligence

September 10 - 11, 2019

Singapore
9.

6th IEEE International conference on cloud computing and intelligence system

September 25 – 27, 2019

Singapore

10.Singapore FinTech festivalNovember 11 – 15, 2019

Singapore

11.

ICAAIML – International conference on Architecture, Artificial intelligence and machine learning

November 18 - 19, 2019

Singapore

12.

ICAIA- International conference on architecture and artificial intelligence

November 18 - 19, 2019

Singapore

13.

ICISP- International conference on  imaging and signal processing

November 18 - 19, 2019

Singapore

14.

ICSPPRA – International conference on signal processing, pattern recognition and applications

November 18 - 19, 2019

Singapore

  1.  INTERPOL World, Singapore
    1. About the conference: This conference being organized by INTERPOL World is looking forward to bringing together innovators, researchers, experts, designers, AI scientists and discuss AI, ML can be used for crime prevention in innovative ways around the globe.
    2. Event Date: July 2nd – 4th 2019
    3. Venue: Marina Bay Sands 10 Bayfront Ave, Singapore 018971
    4. Days of Program: 3 days
    5. Timings: From 9:00 am (2nd July) to 6:00 pm (4th July)
    6. Purpose:  The main purpose of this conference is to bring two worlds together and discuss ideas and knowledge that can make this world a better and safer place. This conference aims to help the experts from the crime control sectors and experts from the AI and technological world together to collectively make the best of both fields and come up with more efficient, new and fast crime controlling tools.
    7. How many speakers: More than 50 speakers
    8. Speakers & Profile:
    9. Richard Van Hooijdonk, International keynote speaker, crime and technology trendwatcher & futurist
      • Dr Ayesha Khanna, Co-Founder and CEO, ADDO AI Singapore
      • Dr Mary Aiken, Cyberpsychologist and academic advisor, Europol’s European Cyber Crime Centre (EC3), United Kingdom
      • Irakli Beridze, Head, Centre for Artificial Intelligence and Robotics United Nations Interregional Crime and Justice Research Institute (UNICRI)
      • Bijoy Bhaskaran, Lead, Automotive Systems and Autonomous Driving, ASEANTUV-SUD Asia Pacific Pte Ltd, Singapore
      • Anna Borgström, CEO, NetClean Technologies, Sweden
      • Christopher Brand, Director and Country Manager, Axon Public Safety Northern Asia, Hong Kong
      • Ami Braun, Vice President, Cyber Technologies and Solutions, AddOn APAC Innovative Solutions, Israel
      • Augustine Chiew, APAC Lead, Public Safety, Huawei Technologies Co Ltd, Singapore
      • Dr Magda Lila Chelly, Managing Director, Responsible Cyber Pte Ltd, Singapore
      • Cheryl Chung, Co-Director, Executive Education Department, Lee Kuan Yew School of Public Policy, National University of Singapore
      • Assaf Cohen, CEO, Anqlave, Singapore
      • Dr Andreas Deppeler, Director, Data and Analytics, PwC, Singapore
      • Daniel Faggella, CEO and Founder, Emerj Artificial Intelligence Research United States
      • Zsuzsanna Felkai-janssen, Head of Sector and DG Coordinator for Artificial Intelligence, DG HOME and Migration,European CommissionBelgium
      • Dr Pavel Gladyshev, Director, Digital Forensics Investigation Research Lab
        University College Dublin, Ireland
      • Amelia Green, Chief Digital Officer, PwC, United Kingdom
      • Dr Bernhard Haslhofer, Senior Scientist, Digital Insight Lab, Austrian Institute of Technology, Austria
      • Dr Eleanor Hobley, Head of Research, Big Data, ZITiS, Germany
      • Tadahiko Ito, Research Engineer, Intelligent Systems Laboratory, SECOM Co., Ltd.,Japan
      • Registration cost: Free
      • Who are the major sponsors:  
      • HUAWEI
      • LASPERSKY LAB
      • TAB
      • SAS – the power to know
      • iB ONEBERRY
  2. ICRASP – International conference on robotics, automation and signal processing, Singapore
    1. About the conference: This conference organized on International level would be dealing with the rapidly growing fields like Robotics, Automation and Signal Processing.   
    2. Event Date: July 4 - 5, 2019
    3. Venue: Holiday Inn Singapore Atrium 317 Outram Rd, Singapore 169075
    4. Days of Program: 2 days
    5. Timings: Yet to be declared
    6. Purpose: The main purpose of this conference is to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results about the fields of discussion i.e. Robotics, Automations and Signal Processing as well as discuss the challenges and restrictions. This conference also aims to be able to provide researchers, practitioners and educators involved a common and interdisciplinary platform to expand their networks.
    7. How many speakers: More than 30 speakers
    8. Speakers & Profile:   
      • Prof. Dr. May George, Smith College
      • Prof. Dr. Hiroyuki Yamauchi, Fukuoka Institute of Technology
      • Prof. Dr. Maico Roris Severino, Federal University of Goiás
      • Assist. Dr. Dora Pokaz, University of Zagreb
      • Assoc. Prof. Dr. Arzu Baloglu, Marmara University
      •  Prof. Dr. Kemal Yıldırım, La Universidad de Santo Tomás (USTOM) Granada Nicaragua
      •  Assoc. Prof. Dr. Dejan Tanikic, University of Belgrade
      • PhD Candidate Karrar Kamoona, Yildiz Technical University
      • Assoc. Prof. Dr. Erol Kam, Yildiz Technical University
      • Dr. Farzad Kiani, Istanbul Sabahattin Zaim University
      •  Dr. Göktürk Poyrazoğlu, Özyeğin University
      • Prof. Dr. Nuray Ucar, Istanbul Technical University
      •  Prof. Dr. Martin Holeňa, Czech Academy of Sciences
      • Prof. Dr. Ellie Abdi, Montclair State University
    9. Registration cost: Maximum cost 500 €
    10. Who are the major sponsors: Yet to be declared
  3. ICSESP – International conference on space electronics and signal processing, Singapore
    1. About the conference: This conference will focus on rapidly growing fields like Space Electronics and Signal Processing.   
    2. Event Date: July 4 - 5, 2019
    3. Venue: Holiday Inn Singapore Atrium 317 Outram Rd, Singapore 169075
    4. Days of Program: 2 days
    5. Purpose: Discussion on the future of Space Electronics and Signal Processing.
    6. Registration cost: Maximum cost 500 €
  1. ICAIME – International conference on artificial intelligence and mechanical engineering, Singapore
    1. About the conference: The conference focuses on Artificial Intelligence and Mechanical Engineering.
    2. Event Date: July 4 - 5, 2019
    3. Venue: Holiday Inn Singapore Atrium 317 Outram Rd, Singapore 169075
    4. Days of Program:   2 days
    5. Purpose: Discussion on Artificial Intelligence and Mechanical Engineering as well as their challenges in the future. 
    6. Registration cost: Maximum cost 500 €
    7. Who are the major sponsors: Yet to be declared

 5. TBD Data council Singapore, Singapore

    1. About the conference: This conference is looking forward to bring together AI scientists, data analysts, innovators, engineers from across the globe to discuss the future of respective fields. 
    2. Event Date: July 17th – 18th 2019
    3. Venue: Microsoft Operations 1 Marina Blvd, #22-01, Singapore 018989
    4. Days of Program: 2 days
    5. Purpose: The key purpose of this conference is to provide a common platform for data analysts, AI scientists, experts, innovators, designers, engineers and researchers from all over the world to interact. 
    6. How many speakers: 50+ speakers
    7. Speakers & Profile:
      • Catherine Tarsney, Analytics Director at the Democratic National Committee
      • Kelley Rivoire, Engineering manager at Stripe
      • Max Beauchemin, Creator of Apache Superset & Apache Airflow, Stealth
      • Josh Ferguson, Co-founder and Chief Architect, Mode
      • Andy Eschbacher,  Senior data scientist at CARTO
      • Heidi Razavi, Co-founder and COO of Keewi
      • Kai Brusch, Product Manager for Delphi at Airbnb
      • Andrew Hoh, Product Manager for the Machine Learning Infrastructure team at Airbnb
      • Austin Wilt, Head of Product Analytics, Slack
      • Anish Doshi, Engineer at Trifacta
      • Bonnie Barrilleaux , Data science manager at LinkedIn
      • Gregn Neiheisel, Co-founder and CTO of Astronomer
      • Karthik Ranganathan,  Co-Founder & CTO, YugaByte
      • Sid Choudhury, VP, Product at YugaByte
      • Anitha Kannan,  AI Researcher, Curai
      • Eric Colson,  Chief Algorithm Officer, Stictch Fix
      • Carl Gold, Chief Data Scientist, Zuora
      • Luke Merrick,  Datra Scientist, Fiddler Labs
      • Nicholas Schrock,  Founder and CEO, Elementl
      • Dan Robinson, CTO, Heap
      • Igor Canadi, Software Engineer, Rockset
      • Spincer Barton,  Data Scientist, Branch International
      • Andrew Colombi,  Co-founder and CTO, Tonic
      • Amiraj Dhawan, Software Engineer,  Yelp
      • Amit Ramesh, Software Engineer, Yelp
      • Praneeth Vepakomma, Researcher, MIT
      • Gian Merlino, Co-founder and CTO,  Imply
      • Arun Krishnamurthy, Lead Data Scientist, Work Day
      • Rupa Parameswaran, Security Architect, Demandbase
      • Abe Gong, Co-founder and CEO ,  Superconductive Health
      • Alex Ratner – Author of Snorkel, Stanford University
      • Robert Nishihara, Machine Learning Researcher, UC Berkeley
    8. Registration cost: $549
    9. Who are the major sponsors:
      1. Twitter
      2. Qubole
  1. Big data and AI leaders summit, Singapore
      1. About the conference: The conference will discuss and share the recent developments and innovations in the field of data science and artificial intelligence.
      2. Event Date: September 10th – 12th 2019
      3. Venue: Marina Bay Sands Singapore 10 Bayfront Ave, Singapore 018956
      4. Days of Program: 3 days
      5. Timings: Wed, 11 Sep 2019, 08:00 -Thu, 12 Sep 2019, 19:00 
      6. Purpose: The purpose of this conference is to bring together data scientists, analysts, engineers, AI researchers and business executives from the wide range of industries spread across the globe to share, discuss, learn, network and experience the full potential of Artificial Intelligence.
      7. How many speakers: More than 10 speakers
      8. Speakers & Profile:
        • Chi Keong Goh – AI Technical Director, YooZoo Games
        • Augus Kong – AVP, Data Science, tokopedia
        • Maneesh Mishra – VP, Data Science
        • Victor Setya – Head of Analytics,, tokopedia
        • Alexandre Hubert – Lead Data Scientist, dataiku
        • Bima Tjahja – AVP, Data, BukaLapak
        • Venaktesh Shivanna – Sr. Manager, Analytics Engineering Data and AI, EA
        • Ridwan Ismeer –  Data Scientist, People Analytics, KraftHeinz
        • Yuliya Seregina – Head of Digital Ecosystems, Delivery and Conversational AI for Consumer Banking, DBS
        • Kenneth Andersson – Head of Innovation and Product Transformation, AirAsia
        • Ravi Madavaram – Head of Artificial Intelligence, Axiata
        • Richard Cart Wright – Director, Speech Analytics, DOLBY
        • Johnson Poh – Head of Group Enterprise AI, UOB
        • Yongsheng Wee – Head of Big Data, ML platform, Pinterest
  1.   Registration cost: $699
  2. Who are the major sponsors:   
      • Daitaiku
      • KDnuggets
      • PR Newswire
      • DATAFLOQ
      • Tractica
      • CIO Advisor
      • Asia Research News
  1. ICISPCS – International conference on intelligent signal processing and communication systems, Singapore
    1. About the conference: The conference focuses on Intelligent Signal Processing and Communication systems.
    2. Event Date: September 10 - 11, 2019
    3. Venue: Singapore (exact address yet to be declared)
    4. Days of Program: 2 days
    5. Purpose: The main purpose of this conference is to bring together leading academic scientists, researchers and research scholars to exchange and share their ideas on Intelligent Signal Processing and Communication.
    6. Registration cost: 450 €  (early registration)
  1. ICCAI – International conference on computing and artificial intelligence, Singapore
    1. About the conference: This conference will have a discussion on Computing and Artificial Intelligence.
    2. Event Date: September 10 - 11, 2019
    3. Venue: Singapore (exact address yet to be declared)
    4. Days of Program: 2 days
    5. Purpose:  The conference aims to be able to provide researchers, practitioners and educators involved a common and interdisciplinary platform to discuss the most recent innovations, trends, and concerns in the fields of  Computing and Artificial Intelligence.
    6. Registration cost:  450 € 
  1. 6th IEEE International conference on cloud computing and intelligence system, Singapore
    1. About the conference:  This conference is looking forward to bringing researchers, analysts, data and tech scientists, academicians, engineers etc. from all over the world and provide them a platform to share their vision and ideas on the continuously rising fields of Machine Learning, Computational Intelligence, Evolutionary Computation, Big Data Computing Systems, Computer Vision, Pattern Recognition, Deep Learning etc.
    2. Event Date: September 25th – 27th 2019
    3. Venue: Singapore, exact address yet to be declared.
    4. Days of Program: 3 days
    5. Timings: From 7:30 am (25th September) to 6:00 pm (27th September)
    6. Purpose: The main purpose of this conference is to provide a platform for researchers and experts to put forth their ideas, research and latest innovations on the continuously rising fields of Machine Learning, Computational Intelligence, etc.  
    1. Who are the major sponsors:
      • IEEE Beijing Section
      • Chinese Association for Artificial Intelligence, China
  1. Singapore FinTech festival, Singapore
    1. About the conference: This festival is expected to turn out as one of the biggest financial technology events. It would be accommodating financial and technology experts, investors, innovators, data analysts, artificial intelligence researchers, tech scientists, researchers etc. from all over the globe.
    2. Event Date: November 11th – 15th 2019
    3. Days of Program: 5 days
    4. Registration cost: Free
    5. Who are the major sponsors:
      • AMTD
      • VISA
      • UOB
      • GIC
      • BBVA
      • DBS
      • NEC
      • ORACLE
      • SMBC
      • EXIGER
      • OCBC  Bank
      • KPMG
      • POINT72 ventures
      • Milliman
      • Intel
      • Standard Chartered
      • Hdac
      • Credit Agricole
      • Centrality
      • AWS
      • Mastercard
      • Prudential
      • Google Cloud
      • Deloitte
  1. ICAAIML – International conference on Architecture, Artificial Intelligence and Machine Learning, Singapore
    1. About the conference: The conference aims to discuss the future and challenges in the fields of Architecture, Artificial Intelligence and Machine learning.
    2. Event Date: November 18 - 19, 2019
    3. Venue: Singapore (exact address yet to be declared)
    4. Days of Program:  2 days
    5. Purpose: The main purpose of this conference is to bring together leading academic scientists, researchers and research scholars to exchange and share their experiences and research results about the fields of discussion i.e. Architecture, Artificial Intelligence and Machine learning.
    6. Registration cost: 450 €  (early registration)
  1. ICAIA- International conference on Architecture and Artificial intelligence, Singapore
    1. About the conference: This conference includes discussion on the rapidly growing fields like Architecture and Artificial Intelligence.
    2. Event Date: November 18 - 19, 2019
    3. Venue: Singapore (exact address yet to be declared)
    4. Days of Program: 2 days
    5. Purpose:  The main purpose of this conference is to share ideas on Architecture and Artificial Intelligence as well as future challenges.
    6. Registration cost: 450 €  (early registration)
  1. ICISP- International conference on  imaging and signal processing, Singapore
    1. About the conference: The conference aims to have a discussion on Imaging and Signal Processing and to provide researchers, practitioners and educators involved a common and interdisciplinary platform to expand their networks.
    2. Event Date: November 18 - 19, 2019
    3. Venue: Singapore (exact address yet to be declared)
    4. Days of Program: 2 days
    5. Registration cost: 450 €   (early registration)
  1. ICSPPRA – International conference on signal processing, pattern recognition and applications, Singapore
    1. About the conference: This conference organized on International level would be dealing with the rapidly growing fields like Signal Processing, Pattern Recognition and applications.
    2. Event Date: November 18 - 19, 2019
    3. Venue: Singapore (exact address yet to be declared)
    4. Days of Program: 2 days
    5. Registration cost: 450 € (early registration)
S.NOName of the conferenceDateVenue
1.AI- ASIAOctober 30th -31st 2017

10 Bayfront Ave

10 Bayfront Ave, Singapore 018956

2.Big Data and Artificial Intelligence Leaders SummitOctober 31st - November 01st 2017

Singapore Marriott Tang Plaza Hotel

320 Orchard Rd, Singapore 238865

3.

FOSSASIA open tech summit

March 17th – 19th  2017

Science Centre Singapore

15 Science Centre Rd, Singapore 609081

4.Deep learning summitApril 27th – 28th 2018

Grand Copthorne Waterfront

392 Havelock Rd, Singapore 169663

5.

6th world convention on robotics, autonomous vehicles and deep learning

September 10th – 11th 2018

Holiday Inn Singapore Atrium

317 Outram Rd, Singapore 169075

6.

FOSSASIA open tech summit

March 17th – 19th  2017

Science Centre Singapore

15 Science Centre Rd, Singapore 609081

  1.  AI- ASIA, Singapore
    1. About the conference: The conference aimed to discuss the introduction and rise of deep learning and AI.
    2. Event Date: October 30th -31st 2017
    3. Venue: 10 Bayfront Ave 10 Bayfront Ave, Singapore 018956
    4. Days of Program: 2 days
    5. Timings: 8:30 am (30th October) - 5:30 pm (31st October)
    6. Purpose: The main purpose of the conference was to bring together the leading experts and innovators of the deep learning and AI world to share their research and ideas.
    7. Who are the major sponsors: Singapore Futurists
  1. Big Data and Artificial Intelligence Leaders Summit, Singapore
    1. About the conference: The conference focused brought together developers, innovators, tech scientists, researchers and AI companies and start-ups together to discuss the future of AI.
    2. Event Date: October 31st – November 01st 2017
    3. Venue: Singapore Marriott Tang Plaza Hotel 320 Orchard Rd, Singapore 238865
    4. Days of Program: 2 days
    5. Purpose: The major purpose of the conference was to exchange of knowledge and ideas for the future of the world. It also provides the people involved to expand their networks as well as vision with the help of talks, presentations, discussions etc.
    6. How many speakers: 19 speakers
    7. Speakers & Profile:
      • Danfeng Li – Chief Data Officer, Umeng+ (Alibaba Group)
      • Brody Huval – Co-founder of  ‘drive.ai’
      • Terence Hung – Chief of  Future Intelligence Technologies
      • Miao Sang – VP and CIO, ASPAC (Johnson & Johnson)
      • Binoo Joseph – Head of Information Technology – TESCO
      • Arun Sundar – Chairman, Asia Analytic Alliance
      • Daniel Hulme – Director, Business analytics MSC
      • Sreeram Iyer – Chief Operating Officer (ANZ)
      • Kelvin Tan – Head of FinTech and Data, (SGX)
      • Brice Richard – Digital Planning lead (ARVP)
      • Ratikant Sahu – Executive Director, Group technology (UOB)
      • Lawrence Wee – Chief Data Scientist ( Zuellig Pharma)
      • Julie Olszeuski – Executive Director, Singapore IT Hub (MSD)
      • Ouy- Doan Do – CIO, Global Markets and Treasury Technology (BNB PARIVAS)
      • Joerg Auumueller – Global Product Manager, AI (SEIMENS Healthineers)
      • David Low – Co-founder and Chief Data Scientist (pand.ai)
      • Senmeng Koo – Deputy Director, Strategic Alliances  (AI Singapore)
      • Carsten Schleicher – Senior Area Manager, Business Information Management  (Asia Cloud Computing Association)
      • Pascal Bornet – Leader for Robotic Process Automation and AI (EY)
    8. Who were the major sponsors:
      • DATAFLOQ
      • Events – AI
      • Send pulse
      • ASIAN SCIENTIST
      • INSIDE BIGDATA
      • DEEP LEARNING WEEKLY
      • DAN – Digital Agency Network
      • TIA – Top Interactive Agencies
  1. FOSSASIA open tech summit, Singapore
    1. About the conference: This open tech summit brought the developers, designers, start-ups, AI communities etc. together and provided a platform for exchange of new innovative ideas.
    2. Event Date: March 22nd – 25th 2018
    3. VenueLifelong Learning Institute 11 Eunos Rd 8, Singapore 408601
    4. Days of Program: 4 days
    5. Timings: From 12:00 pm (22nd March) to 7:30 pm (25th March)
    6. Purpose: The purpose of this conference was to provide the developers, AI communities and companies, designers, start-ups etc. a platform to come together and discuss the future and challenges of the field.
    7. How many speakers: 50+ speakers
    8. Speakers & Profile: some of the speakers are as follows:-
      • Vlado Koljibahib -  Head of CASE IT at Daimler AG
      • Kaz Sato -  Developer Advocate at Google
      • Chris Aniszczyk – Vice President at Cloud Native Computing Foundation
      • Michael  Christen – Founder , SUSI AI
      • Ramji Venkateswaran – Global Head of Cloud ecosystem at J.P MORGAN
      • Douglas Gray – Senior Vice President, Engineering at INDEED
      • Liang Moung – Head of Digital Technology at Singapore Press Holdings
      • Dr. Graham Williams – Data Science Director at Microsoft
      • Bunnie Huang – Chibitronics PTE LTD
      • Carsten Haitzler – Samsung Electronics
      • Frank Karlitschek – Founder and Managing Director at Nextcloud
      • Jean- Baptiste Kempf – President of VideoLAN and VLC Lead Developer
      •  Jonas Von Malottki – Senior Manager Finance at Daimler
      • Chris Van Tuin – Chief TechnologisT at RED HAT
    9. Who were the major sponsors:
      • Daimler
      • Google cloud
      • Microsoft
      • J.P Morgan
      • Indeed
  1. Deep learning summit, Singapore
    1. About the conference: The conference was a new step taken towards the revolution of the artificial intelligence field. It brought innovators together to enlighten people with the potential and glory of the field. 
    2. Event Date: April 27th – 28th 2018
    3. Venue: Grand Copthorne Waterfront 392 Havelock Rd, Singapore 169663
    4. Days of Program: 2 days
    5. Timings: 8:30 am (27th April) - 3:00 pm (28th April)
    6. Purpose: The major purpose of the conference was to help people explore the impact of deep learning on various other growing industries in the world. It also facilitated attendees to learn through innovators and experts of the industry as well as provided them with a platform to expand their circle through networking.
    7. How many speakers: 30+ speakers
    8. Speakers & Profile: 
      • Jeffrey de Fauw – Research Engineer (Deep Mind)
      • Anuroop Sriram – Research Scientist ( Baidu Silicon Valley AI Lab)
      • Jun Yang – Algorithm Architect (Alibaba)
      • Vikramank Singh – Software Engineer (Facebook)
      • Brian Cheung – Phd Student VC Berkeley/Google brains
      • Yipping Goh – Venture partner (Quest Ventures)
      • Jin Hian Lee – Co-founder (mimnetic.ai)
    9. Who were the major sponsors: INTEGER| ALPHA
  1. 6th world convention on robotics, autonomous vehicles and deep learning, Singapore
  1. About the conference: The conference had vivid range of Presentations, Oral talks, Poster presentations and Exhibitions and provided a platform for the developers, scientists, thinkers, designers, AI companies and start-ups to come together and discuss all the trending ideas and recent innovations.
  2. Event Date: September 10th – 11th 2018
  3. Venue: Holiday Inn Singapore Atrium 317 Outram Rd, Singapore 169075
  4. Days of Program: 2 days
  5. Timings: 9:00 am (10th September) - 12:00 pm (11th September)
  6. How many speakers: 4
  7. Speakers & Profile:
      • Nachson (Sean Goltz)  – University of Waikato New Zealand
      • Zhon Xing – Director of AI for Autonomous Driving Borgward, BAIC group USA
      • Rohan Solgarkar – Vice President (Markets & Markets, India)
      • Mr. Salvador Reyna Rico – Founder of the Technological Innovation Group (CIT)
  1. FOSSASIA open tech summit, Singapore

    1. About the conference: This open tech summit brought the developers, designers, start-ups, AI communities etc. together and provided a platform for exchange of new innovative ideas.
    2. Event Date: March 17th – 19th 2017
    3. Venue: Science Centre Singapore 15 Science Centre Rd, Singapore 609081
    4. Days of Program: 3 days
    5. Timings: 9:00 am (17th  March) - 6:00 pm (19th March)
    6. Purpose:   The purpose of this conference was to provide the developers, AI communities and companies, designers, start-ups etc. a platform to come together and discuss the future of the field as well as exchange ideas and enhance their learning.
    7. How many speakers: 50+
    8. Speakers & Profile:
      • Chan Cheow Hoe – Government CIO, GovTech Singapore
      • Frank Karlitschek – Software Developer, Nextcloud
      • Jan Michael Graef – CFO of CASE at Daimler
      • Andrey Terekhov – Open Source Lead, APAC HQ at Microsoft
      • Michael Meskes – President at Credativ International GmbH
      • Dietrich Ayala – Evangelist at Mozilla
      • Stephanic Taylor – Program Manager at Google on the open source Outreach
      • Sanjay Manwani – MySQL, Director, India
    9. Who were the major sponsors:
      • Google Cloud
      • Daimler
      • Microsoft
      • openSUSE
      • Nextcloud
      • Intel
      • KI group
      • Credativ
      • Gandi.net
      • MBM
      • XL Tech
      • mySQL ORACLE

Machine Learning Engineer Jobs in Singapore

The basic responsibility of a Machine Learning Engineer is creating Artificial Intelligence products. To achieve this, they need to create Machine Learning systems with training models. They need programming and statistical skills to do this job successfully. All their responsibilities revolve around the following:

  • Developing ML systems
  • Implementing ML algorithms
  • Running ML experiments and tests

More and more companies have started to integrate AI and Machine Learning products. They are expanding their human potential in terms of speed, capability, and intelligence. This means that they are recruiting machine learning engineers to help them with their data. These companies are of varying sizes, from startups to mid-sized companies to big corporations, all these companies in Singapore are looking for skilled machine learning engineers to help them implement appropriate ML algorithms.

If you are in Singapore and looking for a job in the field of Machine Learning, here are a few companies with open positions:

  • Biofourmis Pte Ltd
  • Grab
  • Aitech Robotics and Automation Pte Ltd
  • PayPal
  • SAP

Some of the in-demand ML job roles include:

  • Data Architect
  • Data Scientist
  • Data Mining Specialists
  • Machine Learning Engineer
  • Cyber Security Analysts
  • Cloud Architects

Here is how you can network with other Machine Learning Engineers in Singapore:

  • Machine Learning meetups
  • Platforms like LinkedIn
  • Machine Learning conferences

Machine Learning with Python Singapore

Here's how you can get started on the use of Python for Machine Learning:

  • Adjust your mindset and believe that you can apply Machine Learning Concepts.
  • Download and install the Python SciPy Kit for Machine learning and install all useful packages.
  • Take a tour of the tools in order to get an idea of all the functionalities available and their uses.
  • Load a dataset and make use of statistical summaries and data visualization in order to understand its structure and workings.
  • Practice some of the most commonly used and popular datasets so as to gain a better understanding of the concepts.
  • Start small and work your way to bigger and more complicated projects.
  • Gathering all this knowledge will eventually give you the confidence of slowly embarking on your journey of applying Python for Machine Learning Projects.

The large and diverse open source community of Python has some very useful libraries as listed below:

  • Scikit-learn: Used for data science, data analysis, and data mining.
  • Numpy: Provides high performance with N-dimensional arrays.
  • Pandas: Used for high-level data structures, data extraction and preparation.
  • Matplotlib: Using graph for data representation.
  • TensorFlow: Allows quick training, setting-up, and deploying of artificial neural networks through multi-layered nodes.
  • Pandas: Provides high-level data structures. Significantly helpful during data extraction and preparation.
  • Pytorch: If NLP is our aim, Pytorch is our go-to library.

The steps required for executing a successful Machine Learning project with Python include:

  • Gathering data: The first and foremost step is to gather the correct data for the project. The quality and quantity of your data are directly proportional to the performance of your model.
  • Cleaning and preparing data: Data gathered is just raw data and cannot be injected directly into our model. In this step, we carefully correct the missing data and prepare the data. 
  • Visualize the data: Sometimes this is the final step of the project, to just show the prepared data and find the correlation between the variables. Visualization helps in understanding the kind of data that we have in our hands and help to make a good selection of model accordingly.
  • Choosing the correct model: After visualizing the data, we get to know how it can be harvested and which model or algorithm is best suited to do so. 
  • Train and test: In this step, train our model with the training data and after it is trained, we test its accuracy with the test data in which it wasn’t trained.
  • Adjust parameters: After finding how accurate your model is, we can fine tune our parameters. 

The following are some tips to help you learn basic Python skills:

  • Consistency is Key: Code every day. Consistency is very important when you are learning a new programming language. While it may be hard to believe, it is a fact that muscle memory plays a big role in programming. It may seem like a daunting task at first but do not give up. Start small with coding for 25 minutes each day and progressively increase your efforts.
  • Write it out: As you move further in your journey as a programmer, there will be moments when you wonder if you should have taken notes from the beginning. You should! Studies have proven over the past several years that writing down a particular thing with your own hands, is the key to long term retention of the concept. Another benefit of writing down stuff by hand is that it helps you plan out your code on paper before you move to actually implementing it on your computer, where visualization of your code is an issue at the beginning of your coding journey.
  • Go interactive!: The interactive Python shell is one of the best learning tools, irrespective of whether you are writing code for the first time, learning about Python data structures such as dictionaries, list, strings etc or debugging an application. In order to initialize the Python shell, simply open your terminal and type in Python or Python3 (as the case may be) into the command line and hit Enter.
  • Assume the role of a Bug Bounty Hunter: It is inevitable that you will run into bugs. The best way to pick up basic Python programming skills is to sit down and solve the bugs on your own. Do not let the bugs frustrate you. Instead, take up the challenge as a means of learning Python in the best possible way and take pride in becoming a Bug Bounty Hunter.
  • Surround yourself with other people who are learning: Coding may seem like a solitary activity, but it actually brings out the best results when it is done in a collaborative manner. It is important for you to surround yourself with other people who are learning Python as well as this not only gives you a boost and keeps you going, but also helps you receive helpful tips and tricks from others, along the way.
  • Opt for Pair programming: Pair programming is a technique in which two developers work on a particular piece of work/ code together. One programmer acts as the Driver, while the other acts as a Navigator. The driver of the code is the one who is actually writing the code, while the Navigator is the developer who guides the entire process, gives reviews and feedback as well as confirms the correctness of the code while it is being written. 

The best Python libraries essential for Machine Learning in 2019 include:

  • Scikit-learn: Used for data science, data analysis, and data mining.
  • SciPy: Includes packages for Engineering, Science, and Mathematics.
  • Numpy: Offers free and fast vector and matrix operations.
  • Keras: Used for Neural network.
  • TensorFlow: Used for training, setting-up, and deploying artificial neural networks using multi-layered nodes.
  • Pandas: Offers high-level data structures used for extracting and preparing data.
  • Matplotlib: Provides data visualization in 2D.
  • Pytorch: Go-to library for NLP. 

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Senior Web Administrator
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Telecommunications Specialist
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Faqs

The Course

Machine learning came into its own in the late 1990s, when data scientists hit upon the concept of training computers to think. Machine learning gives computers the capability to automatically learn from data without being explicitly programmed, and the capability of completing tasks on their own. This means in other words that these programs change their behaviour by learning from data. Machine learning enthusiasts are today among the most sought after professionals. Learn to build incredibly smart solutions that positively impact people’s lives, and make businesses more efficient! With Payscale putting average salaries of Machine Learning engineers at $115,034, this is definitely the space you want to be in!

You will:
  • Get advanced knowledge on machine learning techniques using Python
  • Be proficient with frameworks like TensorFlow and Keras

By the end of this course, you would have gained knowledge on the use of machine learning techniques using Python and be able to build applications models. This will help you land lucrative jobs as a Data Scientist.

There are no restrictions but participants would benefit if they have elementary programming knowledge and familiarity with statistics.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are Machine Learning experts who have years of industry experience.

Finance Related

Any registration cancelled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of written request for refund. Kindly go through our Refund Policy for more details.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor.

Have More Questions?

Machine Learning with Python Course in Singapore

A cosmopolitan city-state situated on the equator and known as having one of the greenest urban landscapes, Singapore is also an economic powerhouse of the world. Ease of business and technological innovation in business are hallmarks that have made Singapore a financial hub of the region. A city is also a great place for dining and shopping, also considered national pastimes and Singapore is also an important center for performing arts and contemporary culture. The city boasts a highly talented workforce and healthy economy and with this backdrop, KnowledgeHut presents the Data Analysis using Python course in Singapore.

Machine Learning with Python Course in Singapore 

Python is a very popular high-level programming language that enables users to create small and large scale programs. Python is a great choice as it offers an easy learning curve and deployment and also boasts many features that facilitate easy reading and comprehension. This language is used in many sciences, engineering, and business applications, and is a favorite of those who create web apps and games. The Machine Learning with Python Training in Singapore by KnowledgeHut is an online training program that will help you get acquainted with the fundamentals of this framework. In these 48 hours of instructor-led online classes, you will be given assignments and get to participate in project work. In this course, you will initially be introduced to the application scenarios where one can use Python for data analysis. As the Machine Learning course in Singapore progresses, you will be taught design methods in data analysis solutions like data classification and machine learning amongst others. The course will include an overview of frameworks that are available for data analysis and there will be sections on Matplotlib and Scipy & Numpy. During this training, our instructors will teach you about Pandas, the IPython toolkit and sci-kit-learn including the subtopics. Understanding Python's place in the Hadoop ecosystem is an important part of this Machine Learning using Python course in Singapore. After you finish this training, you will be provided a course completion certification from KnowledgeHut with a credit for each hour. This Machine Learning training using Python will arm you with the skills and knowledge to pass any exam based on the topic.

The KnowledgeHut Advantage

KnowledgeHut is a global leader in the field of e-learning, with a presence in over 70 countries. Our courses are impactful and are delivered using the best online learning technology that combines a live classroom environment with easy access. Along with our hallmark tutorship quality at a great price, you will also get a downloadable e-book for extra guidance. If you are a webmaster, scientist, programmer, analyst, professional software developer, and entrepreneur or someone looking to get in-depth knowledge of Python, this Machine Learning using Python course in Singapore is an ideal platform for you.