Machine Learning with Python Training in Kuala Lumpur, Malaysia

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
HRDF Claimable

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 Kuala Lumpur, Malaysia

Machine Learning, simply put, is the application of systems that imbibes Artificial Intelligence concepts and provides the systems the ability to learn, perform and improve upon set tasks on their own, without needing reprogramming or human input. The process begins with the use of data in various ways like direct experience. Then, the program looks for patterns in said data with the aim to make better decisions in the future.There are various methods to perform Machine Learning, broadly categorized into 2 categories:

  • Supervised algorithms: These algorithms take the learnings from the past data and apply them to the current data and predict future events or events that will occur in the future. 
    • A dataset already studied on is entered into the system for it to learn from.
    • A learning algorithm derived from that dataset is produced in the form of an inferred function that is capable of making predictions.
    • These algorithms give results for new inputs after sufficient training.
  • Unsupervised algorithms: These algorithms are required when the data needed for the training of the machine learning system is either not labeled or unclassified. 
    • Unsupervised learning systems infer functions so as to describe a hidden structure from an unlabeled dataset.
    • Such systems do not provide you with the correct result but draw inferences from the available data to describe/identify hidden patterns from uncategorized data.

Kuala Lumpur is home to several leading tech companies, such as IBM, Samsung, HP, Dell, etc. Machine learning is helping these companies enhance business scalability and boost business operations for companies across the globe.

Machine Learning deals with computers and systems. These systems take huge amounts of data, analyze and solve real-life problems through training on that data. Machine Learning provides a way for humans to solve problems without having to actually understand the problem.

  • It's easy. It works.

Machines work faster than human brains and thus, are able to solve problems faster as well. If, for instance, there are a million approaches to a problem, a machine can systematically work out, resolve and simultaneously evaluate all the options and obtain the best outcome.

  • Used in a wide range of applications today

Machine Learning has so many practical uses in real life. It helps businesses save time and money. It allows people to get things done efficiently and effectively. Every industry from health care to customer service and financial institutions are enjoying the benefits of Machine Learning. It makes it an indispensable part of today’s society.

All organizations from startups to MNCs in Kuala Lumpur function on the immense amounts of data generated every day. Data is reshaping technology and business and will continue to do so in the future. 

Machine Learning is a new field of work and research as of now. Tech experts have been making use of it increasingly over the years. Walmart product recommendations, Social media feeds (Facebook and Instagram), Google Maps, etc are all examples of it - enabled by powerful Machine Learning algorithms without human interference. 

We partake in machine learning even when we do not know about it. Learning about it is an inevitable next step that any professional - especially someone involved in the field of Information Technology and Data Science - must take so as to not become irrelevant. 

Some of the benefits of learning Machine Learning in Kuala Lumpur are:

  1. Better job opportunities: According to a report in Tractica, services driven by AI were worth $1.9 billion in 2016. This number is expected to rise to approx. $19.9 billion by the end of 2025. With most industries in the world looking at expansion with Machine Learning and Artificial Intelligence, their knowledge brings you brighter career opportunities. Kuala Lumpur is a great place for machine learning engineers to work in. Some of the companies that employ machine learning professionals in Kuala Lumpur include Grab, Air Asia, Amazon, ExxonMobil, ABB, DXC, Hitachi Vantara, Hays, Boston Consulting Group, Inmagine, Fusionex, NXP Semiconductors, MoneyLion, etc.
  2. Better pay: The average machine learning engineer salary in Kuala Lumpur is RM144,017. They also get an average bonus of RM6,612. Based on the compensation data available, the estimated salary potential for Machine Learning Engineer in Kuala Lumpur will increase 33 % over 5 years. 
  3. Increasing demand: There is a huge gap between the demand and the availability of professionals with Machine Learning skills in Kuala Lumpur. Simply put, the demand, as well as the pay for Machine Learning engineers, is only going to increase in the coming future. An entry level machine learning engineer (1-3 years of experience) earns an average salary of RM101,499 in Kuala Lumpur. 
  4. Industries in Kuala Lumpur are shifting to Machine Learning: Most industries deal with great amounts of data every day. By extracting analysis and patterns from the data they record, companies aim to work more competently, gaining an edge over their competitors in the process. At such a time, every industry is a ripe field for professionals engaging in Machine learning. 

The best way to learn Machine Learning is through a course. In Kuala Lumpur, there are several institutions that offer courses in Machine Learning including

  1. Excelr
  2. Iverson
  3. Ikompass
  4. Tertiary Courses
  5. iTrain

Machine learning is a big field and is only expanding with time. However, self-learning ML is an effective way to keep oneself relevant. Keep these pointers in mind:

  • Learn to implement practical skills through hands-on training instead of going through research papers and textbooks. Practical implementation is the very heart of ML.
  • Build your profile as an ML enthusiast through projects. Projects are the best way to attract employers as well as test our skills.

You can follow these steps:

  • Structural Plan: Create a structured plan of the topics that you need to familiarize yourself with first and what can be left to be learned later.
  • Prerequisite: Choose a programming language you are comfortable with first. Then start polishing your mathematical and statistical skills.
  • Learn: Start learning as per the plan you created in step 1. You can refer to sources online or to textbooks. Having an understanding of the ML algorithms is important.
  • Implement: Work on projects that use the algorithms you’ve learned. Solve problems from the internet, take part in online competitions to learn more.

If you are a beginner in Machine learning, networking with other professionals will help you get a clear understanding of Machine learning concepts and the current trends. Here are a few meetups organized in Kuala Lumpur for Machine Learning professionals:

  1. AI Saturdays KL
  2. TensorFlow and Deep Learning Malaysia
  3. AI Geeks KL
  4. Kuala Lumpur PyTorch & Deep Learning
  5. GDGKL

Follow these 5 steps:

  • Adjust your mindset: Think of ML as a field that unravels itself the more you practice it. So take cognizance of what you think might be holding you back and focus on improving yourself in your weak areas. Work with people who support you.
  • Pick a process that works for you: Derive a process that is systematic and suits your way of working through ML problems.
  • Pick a tool: Pick a tool that suits your comfort level with the Machine Learning concepts and map the following into your process:
    • Beginners should opt for the Weka Workbench.
    • Intermediate level learners should go for the Python Ecosystem.
    • Advanced level learners are recommended the R Platform.
  • Practice datasets: Choose from available datasets and practice the process of data collection and manipulation.
  • Build your portfolio: Demonstrate your knowledge and skills in the form of a portfolio to be sent out to potential employers in Kuala Lumpur.

Kuala Lumpur is the hub for several tech companies that are willing to pay handsomely to skilled Machine Learning professionals. These organizations include Novartis, Echobox, Metapair, SR Technics UK, ResMed, Cognizant Technology Solutions, Celcom Axiata Berhad, Comfort Works, Mindvalley, Micro Focus, Topdanmark, etc. You will need the following 5 technical skills:

  • Programming languages: You need to be familiar with programming languages such as Python, Java, Scala, etc. Knowledge of formatting data formats and processing it to be compatible with ML algorithms is important.
  • Database skills: A prior knowledge of working with MySQL and related databases are needed in the ML field. Ml professionals have to make data sets from various data sources simultaneously. Programmers must be able to read and convert data effectively.
  • Visualization tools: There are several tools available for visualizing the data used in Machine Learning. Knowledge of these tools is needed to apply the concepts in real life. 
  • Machine learning frameworks: Several statistical and mathematical algorithms are used to design a Machine Learning model - to learn from data and come to a prediction. They include Apache Spark ML, ScalaNLP, R, TensorFlow, etc.
  • Mathematical skills: Mathematics is the most important part of Machine Learning. Here is a list of some mathematical concepts you as an ML professional must be familiar with:
    • 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

Follow these 6 steps to execute an ML project with Python:

  • Gather data: It is the most important step - collecting data for your project. Remember that the quality and quantity of your data dictates the performance of your ML model.
  • Clean and prepare data: Correct the missing data and then convert the raw data to the requirements of the model. Finally, divide it into 2 parts: training data and testing data.
  • Visualize data: Many times, this is the final step of the project. It helps in understanding the data to make the right decisions.
  • Choose the correct model: Now that you have good knowledge about the data, choose the correct model that sits your algorithm.
  • Train and test: We had divided the data into training and testing data in Step 2. Now, train the model with the training data and afterward, test its accuracy with the test data.
  • Adjust parameters: This step is course correction. After gauging how accurate the model is, fine tune the parameters. 

Algorithms are an integral part of Machine Learning. Here’s how you can effectively learn them:

  • List the various algorithms: List the algorithms that you wish to begin your Machine Learning journey with. Make a text or doc file and don’t forget to list the category of the algorithm as well.
  • Apply the listed algorithms: The best learning comes from the practical application of Machine Learning algorithms to data sets. Thus, it is important to practice. Build up an intuition for the various algorithms such as Support Vector Machines, decision trees, etc.
  • Describe the algorithms: The next step is to explore these algorithms. An analysis of Machine Learning algorithms will help you build a description of them. Continue adding more information to these descriptions as you go.
  • Implement the Algorithms: The implementation of Machine Learning algorithms is vital to learn the workings of an algorithm. You’ll understand the micro decisions involved in the implementation of Machine Learning concepts.
  • Experiment on Algorithms: Once you have completed the above steps, you are well placed to experiment with algorithms. You can use standardized data sets, control variables, etc to experiment. 

Machine Learning Algorithms

The K Nearest Neighbours algorithm is a simplistic Machine Learning algorithm. Given a multiclass dataset to be worked on with the aim to predict the class of the data point, use the K Nearest Neighbour algorithm to reach a solution.

  • The primary prerequisite of the nearest neighbour algorithm is the pre-defined number, which will be stored as the value of ‘k’. This number defines the number of training samples closest in distance to a new data point to be classified. 
  • The label that will be assigned to the new data point will be one that has already been assigned to (and defined) by the neighbours. 
  • K-nearest neighbor classifiers hold a fixed user-defined constant for the no. of neighbors that have to be determined.
  • The algorithms work on the radius based classification. The fixed radius is a metric measure of the distance - the Euclidean distance between the points.
  • All methods that are based on the classification of the neighbours are also called ‘the non-generalizing Machine Learning methods’. Classification is based on majority voting conducted among the nearest neighbours of the sample.

In spite of its simplicity, the K Nearest Neighbour algorithm has proven to be a successful and useful solution to a number of regression problems, not to mention, classification problems.

This depends on what you intend to do with Machine Learning as a field:

  • If you simply wish to use the existing ML algorithms, then you can study it without knowing any classic algorithms. There are several courses that offer knowledge on Machine Learning without delving deep into the algorithms.
  • In case you want to innovate in the field of Machine Learning, having knowledge of both the working as well as the uses of algorithms is a critical prerequisite. You need the tools and the knowledge to adapt and design using Machine Learning.

Machine Learning Algorithms can be broadly classified into 3 categories - 

  1. Supervised Learning: Linear Regression, Logistic Regression, Classification and Regression Trees (CART), Naïve Bayes, K-Nearest Neighbours 
  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 and output variables - (x) and (y) is expressed as y = a + bx
    • Logistic Regression - Logistic Regression is just like the linear regression model; the only difference is the outcome of the regression is probabilistic, rather than exact values.
    • CART - Classification and Regression Trees, abbreviated as CART, is a form of implementation of Decision Trees. This algorithm charts the possibility of each outcome and predicts the result on the basis of defined nodes and branches.
    • 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 Neighbours - This algorithm charts the entire data set given, and after assigning a pre-defined 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 this type 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 or instances of two items occurring together, 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. By performing iterations of the steps to ensure the distance between the data point and the centroid is the closest, the real centroid arrives for each cluster.
    • PCA -  Principal Component Analysis (PCA) makes the data space easier to visualize, by reducing the number of variables. It does this by mapping the maximum variances of each point onto a new coordinate system, with axes corresponding to the principal components chosen. The basic principle of orthogonality ensures that each pair of components is unrelated to each other.
  • Ensemble Learning: Groups of learners are more likely to perform better compared to singular learners. By building on this premise, these type 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 worked on to obtain the real outcome.
    • Boosting - This algorithm is similar to the above one, but 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 simplest of machine learning algorithms are the ones that solve the easiest ML problems (simple recognition). We can base it on the following criteria:

  • Easy to understand.
  • Easy to implement.
  • Takes less time and resources to train and test the data.

K-nearest neighbor algorithm is the simplest ML algorithm. Here is why:

  • Simplest supervised learning algorithms and best suited for beginners.
  • A classification algorithm that can also be used for regression.
  • Non-parametric and classifies based on the similarity measure.
  • Some real-life examples of KNN use:
    • Searching inside documents containing similar topics.
    • Detecting patterns in credit card usage.
    • Vehicular number plate recognition.

ML has loads of tools, algorithms, and models that you can choose from. But one has to keep in mind certain things while selecting the algorithm for your project.

  • Understanding your data:
    • Visualize your data by plotting graphs.
    • Try to find strong relationships among the data.
    • Clean up your data by adding missing particulars and discarding unneeded stuff.
    • Prepare your data to be injected into your model.
  • Get the intuition about the task: You need to see 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 your constraints: The best models and algorithms require high data storage and manipulation resources. Constraints can be hardware or software.
    • 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.
    • Depending upon time constraints, we need to decide whether we can allow the training phase to be of long duration or not, or testing phase to be short or long.

  • Find available algorithms: Only after going through the above three phases, we can check which algorithms adhere to our requirements, and constraints and finally go and implement it! 

Follow these 5 steps:

  • Select a programming language: Choose the programming language that you wish to use in order to undertake your implementation. This decision influences the standard libraries as well as the APIs.
  • Select the algorithm: Decide on all of the specifics of the algorithm and be as decisive and precise as possible. You should decide on everything from the type of algorithm, the classes as well as the specific implementation and the description.
  • Select the problem: Move on to the selection of the canonical problem set that you are going to use in order to test.
  • Research the algorithm: Go through books, research papers, libraries, websites and blogs that contain descriptions of your algorithm and its implementation, etc.
  • Undertake testing: Run unit tests for every function of your algorithm. This enables you to understand the expectations as well as the purpose of each unit of a code of your algorithm.

ML is a growing field with unlimited applications and uses. There are various training centres in Kuala Lumpur offering ML courses. 

You can try the following

  1. Cnergy ww.mmu-cnergy.com
  2. Iverson Iverson Associates Sdn Bhd
  3. MaGic MaGIC: Malaysian Global Innovation & Creativity Centre

The following are some essential topics of Machine Learning that every learner should be acquainted with:

  • Decision Trees: A Decision tree is a type of supervised learning algorithm that is used for classification problems. Advantages of decision tree methods:
    • They are relatively simplistic
    • They are easy to understand, visualize and interpret.
    • They are able to perform feature selection other than variable screening.
    • Decision trees are not affected by nonlinear relationships between parameters.
    • Decision trees require minimal efforts in the direction of data preparation.
    • Decision trees are able to handle and analyze both categorical and numerical data.
    • Decision trees can handle problems that require multiple outputs.
  • Support Vector Machines: These provide a higher degree of accuracy in classification problems and can also be used in problems of regression. Some benefits of a Support Vector Machine:
    • They provide guaranteed optimality in the solutions that they provide. The solution is not a local minimum, but a global minimum, thereby guaranteeing its optimality.
    • They are useful in both, Linearly Separable (also known as Hard margin) as well as Non-linearly separable (also known as Soft Margin) data.
    • Feature Mapping, which used to be a huge burden on the computational complexity of an algorithm, is a reduced burden owing to the ‘Kernel Trick’ provided by Support Vector Machines.
  • Naive Bayes: The Naive Bayes algorithm is a classification technique that is based on Bayes’ theorem. It assumes that the presence of a particular feature in a said sample of data is independent of and unrelated to the presence of any other feature in that particular sample of data. Some of the advantages are as follows:
    • A simplistic technique of classification.
    • It needs less training data for classification.
    • It converges quicker than traditional models.
  • Random Forest algorithm: The Random Forest algorithm is a supervised learning algorithm. It creates a forest of decision trees and randomizes the inputs so as to prevent the system from identifying any pattern in the input data owing to its order. Some of the advantages are here:
    • Random forest can be used for both regression and classification problems.
    • It is easy to view the relative importance that a random forest assigns to the input features
    • It is easy to use.
    • The number of hyperparameters included in a random forest is relatively easy to understand.
    • A Random Forest produces a good prediction result.

Machine Learning Engineer Salary in Kuala Lumpur, Malaysia

The average salary of Machine Learning Engineer in Kuala Lumpur is RM 31,243/yr.

The average salary of a machine learning engineer in Kuala Lumpur compared with is Portland  RM 31,243/yr whereas, in Singapore, it’s $73,193/yr.

According to Eset, Malaysia is the most cyber-savvy nation in Asia and its capital Kuala Lumpur is renowned for various tech organizations, including Ably Resources, Tele-Temps Sdn Bhd, iPrice gathering and others. These organizations have realised the significance of ML and thus the demand for ML engineers is growing at a faster pace.

Following are the advantages of arriving into the desirable work' of the engineering alumni - 

  • Growth in Career - According to a recent study, there are 9.8 times more Machine Learning Engineers working today than five years ago and there are currently 1,829 open Machine Learning Engineering positions on LinkedIn. These numbers suggest that ML Engineers are much in demand and the demand is here to stay.
  • High Salary - According to Indeed, machine learning engineer is the best job of 2019 due to high salaries.

A career in machine learning is critical and involves skills in Python, SQL programming, and others important for machine engineer profile. As more and more companies are now investing in machine learning and looking for ML experts for cutting-edge research. And according to a study revealed by Gartner, AI is projected to create 2.3 million jobs by 2020. So, it is safe to say that machine learning will remain an in-demand skill.

Some of the companies that are hiring Machine Learning Engineer in Kuala Lumpur-

  • Ably Resources 
  • Tele-Temps Sdn Bhd
  • iPrice group
  • EPS
  • Glueck Technologies Sdn Bhd
  • Alegion

Machine Learning Conference in Kuala Lumpur, Malaysia

S.No

Conference Date Venue
1.

International Conference on Data Mining, Statistics and Machine Learning Techniques ICDMSMLT

December 05-06, 2019

TBA

2.

2nd International e-Symposium on Information Science and Technology 2019

August 30, 2019

Virtual Conference

3.

The 13th Multi-disciplinary International Conference On Artificial Intelligence

November 17-19, 2019

TBA

4.

ICAIML 2019

December 8 - 10, 2019

TBA

5.

Annual Summit on Artificial Intelligence and Machine Learning

November 25-26, 2019

TBA
6.

International Conference on Digital Circuits, Systems, and Signal Processing

December 7 - 8, 2020

TBA

7.

International Conference On Computer Science, Machine Learning And Artificial Intelligence (ICCSMLAI - 2019)

19th Jul, 2019

Flamingo by the lake, Kuala Lumpur, No.5, Tasik Ampang, Jalan Hulu Kelang, 68000 Ampang, Selangor Darul Ehsan

  1.  ICDMSMLT 2019, International Conference on Data Mining, Statistics and Machine Learning Techniques, Kuala Lumpur
    1. About the conference: The International Research Conference brings together dedicated professionals to share research findings and experiences in the industry. 
    2. Event Date: December 5 - 6, 2019
    3. Venue: To Be Announced 
    4. Days of Program: Two 
    5. Timings: To Be Announced 
    6. Purpose: CAIE teams up with the Special Journal Issue on Artificial Intelligence and      3Energy. High-impact papers are selected for the issue’s special journal. 
    7. Registration cost: Non-Student Oral/Poster Presenter Registration: 350 €, Student Oral/Poster Presenter Registration: 300 €, Listener Registration: 250 €, Additional Paper Publication: 100 €
  2. 2nd International e-Symposium on Information Science and Technology, IESIST, 2019, Kuala Lumpur
    1. About the conference: IESIST 2019 provides a digital platform to professionals and academicians from diverse platforms to present their recent findings in their fields. 
    2. Event Date: 30th August, 2019
    3. Days of Program: 1
    4. How many speakers: 2
    5. Speakers & Profile
      1. Dr. Nurhizam Safie Mohd Satar, Ph.D. (IIUM), MIT (UKM), BSc. (IIUM), Dip. (UPM)
      2. Dr. Yahaya Abd. Rahim, Ph.D. (UTeM), MSc. (UTM), BSc. (Hon) (UUM), Dip. (UPM) 
    6. With whom can you Network in this Conference: Social Media Analysts, Academics and Industry Professionals.
    7. Registration cost: $375
    8. Who are the major sponsors: SCOPUS, ELSEVIER
  3. The 13th Multi-disciplinary International Conference On Artificial Intelligence, Kuala Lumpur
    1. About the conference: The Multi-disciplinary International Conference on Artificial Intelligence (MIWAI) calls on professionals from multiple disciplines and gives them a stage to discuss future challenges. 
    2. Event Date: November 17-19, 2019
    3. Venue: TBA 
    4. Days of Program: 2
    5. Timings: TBA 
    6. With whom can you Network in this Conference: Academic Researchers, Industrial Practitioners and Developers.
    7. Registration cost: TBA
    8. Who are the major sponsors: University Utara Malaysia, IT, Mahasarakham University, Artificial Intelligence Association Of Thailand
  4. International Conference on Artificial Intelligence and Machine Learning, ICAIML 2019, Kuala Lumpur
    1. About the conference: ICAIML focuses on topics that have a theoretical application regarding Machine Learning.
    2. Event Date: December 8 - 10, 2019
    3. Venue: TBD
    4. Days of Program: Three
    5. Timings: 9:00 AM to 5:00 PM
    6. Purpose: Research Scientists, Faculties and heads from over 50 countries come together to present papers and exchange ideas. 
    7. How many speakers: Two
    8. Speakers & Profile
      1. Prof. Dr. Kelvin Joseph Bwalya, Vice Dean of the Faculty of Business and Economics, University of Johannesburg, South Africa 
      2. Prof. Dr. Xiao-Guang Yue, Editor, College Consultant. 
    9. With whom can you Network in this Conference: International colleagues and speakers
    10. Registration cost: $390
    11. Who are the major sponsors: IETI, IRIEM, IDSAI      
  5. Annual Summit on Artificial Intelligence and Machine Learning, Kuala Lumpur
    1. About the conference: The conference will be held focusing on the recent innovations and paves cool opportunities for new innovations.
    2. Event Date: 25th - 26th November, 2019
    3. Venue: TBD
    4. Days of Program: Two
    5. Timings
    6. How many speakers: TBD
    7. Speakers & Profile: TBD
    8. With whom can you Network in this Conference: Experts on the state-of-the-art in Computer Science and Scientists
    9. Registration cost: Package A: $699, Package B: $899, Package C: 1099
    10. Who are the major sponsors: PS3 Laboratories LLP, Vellatrix. 
  6. International Conference on Digital Circuits, Systems, and Signal Processing, Kuala Lumpur
    1. About the conference: The conference brings together a diverse team of professionals who present papers on Machine Learning and Analysts.
    2. Event Date: December 7 - 8, 2019
    3. Venue: TBD
    4. Days of Program: Two
    5. Timings: TBD
    6. Purpose: The conference covers Digital Circuits, Signal Processing, and Systems and provides a stage to researchers and academic scientists.
    7. How many speakers: TBD
    8. Speakers & Profile: TBD
    9. With whom can you Network in this Conference: Presenters and Career Professionals. 
    10. Registration cost: Non-Student Oral/Poster Presenter Registration: 350 €, Student Oral/Poster Presenter Registration: 300 €, Listener Registration: 250 €, Additional Paper Publication: 100 €
    11. Who are the major sponsors: TBD
  7. International Conference On Computer Science, Machine Learning And Artificial Intelligence (ICCSMLAI - 2019), Kuala Lumpur
    1. About the Conference: ICCSMLAI brings together a global community of computer scientists, machine learning and artificial intelligence experts and gives them a stage to share their recent findings and developments with their peers. 
    2. Event Date: 19th July, 2019
    3. Venue: Flamingo by the lake, Kuala Lumpur, No.5, Tasik Ampang, Jalan Hulu Kelang, 68000 Ampang, Selangor Darul Ehsan
    4. Days of Program: One
    5. Timings: TBA
    6. Purpose: ICCSMLAI provides a platform for participants from all around the world. It helps them in establishing business networking and paves the way for collaboration in the future.
    7. How many speakers: TBA
    8. Speakers & Profile: TBA
    9. Registration cost: Authors (Academician/Practitioner): $300 USD, Authors (Student Masters/PhD): $250, Authors (Bachelors): $200.
    10. Who are the major sponsors: DRJI, Scholarsteer, Springer, EBSCO.

S.No

Conference Name

Date

Venue

1.

RiTA 2018 The 6th International Conference on Robot Intelligence Technology and Applications

16th to 18th December 2018

Kuala Lumpur, Malaysia

2.

IMAN 2018: 6th International Conference on Islamic Applications in Computer Science and Technologies

20th to 23rd December 2018

Kuala Lumpur, Malaysia

3.

MySEC 2018: Malaysian Software Engineering Conference

7th to 8th August 2018

Kuching, Sarawak, Malaysia

4.

ICDMSMLT 2018: International Conference on Data Mining, Statistics and Machine Learning Techniques

5th to 6th December 2018

Kuala Lumpur, Malaysia

5.

2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET 2018)

8th to 9th November 2018

Kota Kinabalu Kota Kinabalu, SBH, MY

6.

Programmatic Malaysia 2018 Conference

26th to 28th September 2018

Petaling Jaya, Selangor. New World Petaling Jaya Hotel

7.

Next Big Tech Asia 2018

2nd to 8th October 2018

Kuala Lumpur, Malaysia

8.

ROBIO 2018 - IEEE International Conference on Robotics and Biomimetics

12th to 15th December 2018

Kuala Lumpur Convention Centre, Kuala Lumpur, Malaysia

9.

GCMT'18 - 2nd Global Conference on Computing and Media Technology

14th to 15th November 2018

Kuala Lumpur, Malaysia

  1.  RiTA 2018 The 6th International Conference on Robot Intelligence Technology and Applications, Kuala Lumpur
    1. About the conference: The 6th International Conference was a platform for the free exchange of ideas in robotics and AI that could be useful for business organizations and the scientific community alike.
    2. Event Date: 16th to 18th December, 2018
    3. Venue: Kuala Lumpur, Malaysia
    4. Days of Program: 2 days 
    5. Purpose: The purpose of the conference was to collaborate with the corporate and scientific leaders to create pragmatic and realistic solutions using Robotic intelligence and app development 
    6. Who were the major sponsors: RiTA  
  2. IMAN 2018: 6th International Conference on Islamic Applications in Computer Science and Technologies, Kuala Lumpur
    1. About the conference: The IMAN 2018 was a platform where anyone could present their ideas on computer science and its impact on the IT sector. 
    2. Event Date: 20th to 23rd December, 2018
    3. Venue: Kuala Lumpur, Malaysia
    4. Days of Program: 3 days 
    5. Purpose: To indulge in an informative discourse on AI and computer science technologies
    6. Who were the major sponsors: IMAN  
  3. MySEC 2018: Malaysian Software Engineering Conference, Kuala Lumpur
    1. About the conference: MySEC 2018 was a platform that encouraged local and international tech experts and engineers to collaborate and read out research papers on software engineering and coding
    2. Event Date: 7th to 9th August, 2018
    3. Venue: Kuching, Sarawak, Malaysia
    4. Days of Program: 2 days 
    5. Purpose: The conference aimed at bringing together academics and scientists for developing new and actionable solutions to the hassles faced by IT companies using Machine Learning 
    6. Who were the major sponsors: MySEC
  4. ICDMSMLT 2018: International Conference on Data Mining, Statistics and Machine Learning Techniques, Kuala Lumpur
    1. About the conference: This international conference created a holistic platform for scientists and students to meet and discuss the concepts of Data Mining and Machine Learning  
    2. Event Date: 5th to 6th December, 2018
    3. Venue: Kuala Lumpur, Malaysia
    4. Days of Program: 1 day 
    5. Purpose: The purpose of this conference was to collaborate with intelligent minds to figure out cost-effective and time-saving ways to deal with the technical issues in the IT sector using Machine learning and Data Mining Concepts. 
    6. Who are the major sponsors: ICDMSMLT
  5. 2018 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET 2018), Kuala Lumpur
    1. About the conference: The conference was a space for business leaders to meet the brightest minds in the country and discuss the basics of AI, programming, Machine Learning, and Data Mining 
    2. Event Date: 8th to 9th November 
    3. Venue: Kota Kinabalu, Kota Kinabalu, SBH, MY
    4. Days of Program: 1 day 
    5. Timings: 9:00am to 5:00pm
    6. Purpose: IEEE 2018 conference tried to bring together engineers, software experts, technicians to discuss AI and technological advancements in IT and its impact on the corporate world 
    7. Who were the major sponsors:  IEEE
  6. Programmatic Malaysia 2018 Conference, Kuala Lumpur
    1. About the conference: The conference was all about promoting programmers and coders to talk about their findings and figure out new and practical ways to tackle real-time issues in IT.
    2. Event Date: 26th to 28th October, 2018
    3. Venue: Petaling Jaya, Selangor. New World Petaling Jaya Hotel
    4. Days of Program: 2 days 
    5. Timings: 9:00am to 5:00pm
    6. Purpose: The Programmatic Malaysia conference brought together coders, programmers and tech enthusiasts from across the world to discuss the unique concepts of AI and Machine Learning.
    7. Who are the major sponsors: Star Media
  7. Next Big Tech Asia 2018, Kuala Lumpur
    1. About the conference: Next Big Tech Asia 2018 was a platform that facilitated a transparent and free exchange of innovative ideas on AI and its effect on business.
    2. Event Date: 2nd to 8th October, 2018
    3. Venue: Kuala Lumpur, Malaysia
    4. Days of Program: 7 days 
    5. Purpose: The conference aimed to create a holistic and healthy atmosphere for research scholars and business leaders to meet and collaborate
    6. Who are the major sponsors: MDEC
  8. ROBIO 2018 - IEEE International Conference on Robotics and Biomimetics, Kuala Lumpur
    1. About the conference: The IEEE international conference involved a discussion on machine learnings and robotics and the development of Biometrics.  
    2. Event Date: 12th to 15th December, 2018
    3. Venue: Kuala Lumpur Convention Centre, Kuala Lumpur, Malaysia
    4. Days of Program: 3 days 
    5. Timings: 8 am to 4 pm
    6. Purpose: The conference gave scholars and scientists a platform to present their ideas on Robotics and Biometrics and discuss ways in which AI could revolutionize the corporate sector 
    7. Who are the major sponsors: ROBIO
  9. GCMT'18 - 2nd Global Conference on Computing and Media Technology, Kuala Lumpur
    1. About the conference: The conference brought together the best minds of the IT sector, research scholars, academics and teachers to discuss cloud computing and its effect on media 
    2. Event Date: 14th to 15th November, 2018
    3. Venue: Kuala Lumpur, Malaysia
    4. Days of Program: 1 day 
    5. Purpose: The conference aimed to create a holistic environment where academics could discuss the latest trends in computing and find machine learning solutions.
    6. Who are the major sponsors: GCMT 

Machine Learning Engineer Jobs in Kuala Lumpur, Malaysia

The responsibilities of a Machine Learning Engineer include:

  • Executing suitable ML algorithms
  • Operating machine learning tests and experiments
  • Designing and developing machine learning and deep learning systems

Kuala Lumpur is not only home to several leading MNCs but also has a very good start-up climate. There are more than 800 Tech startups in Kuala Lumpur and new startups are popping up almost every month. All these companies and startups are looking for ML experts to help them extract meaningful information from a huge set of raw data. 

Some of the companies hiring in Kuala Lumpur are:

  • IRIS Corporation Berhad– Kuala Lumpur
  • Amazon
  • Hays
  • Blinkware Technology Sdn Bhd

Some of the ML job roles in demand are:

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

If you want to get hired fast in Kuala Lumpur, referrals work best. You can create your network with other Machine Learning Engineers through the following:

  • Online platforms like LinkedIn
  • Machine Learning conferences
  • Social gatherings like meetups

A Machine Learning Engineer in Kuala Lumpur earns around RM144,017 in a year. 

Machine Learning with Python Kuala Lumpur, Malaysia

Here's how you can do so:

  1. Adjust your mindset to apply Machine Learning Concepts.
  2. Download and install the Python SciPy Kit for Machine learning. Don’t forget to install all the useful packages as well.
  3. Take a tour of the tool in order to get an idea of all the functionalities available and their uses.
  4. Load a dataset and make use of statistical summaries and data visualization to understand its structure and workings.
  5. Practice on commonly used datasets so as to gain a better understanding of the concepts.
  6. Start small and work your way to bigger and more complicated projects.
  7. Gathering all this knowledge will eventually give you the confidence of slowly embarking on your journey of applying Python for Machine Learning Projects.

Some very useful libraries are listed below:

  • Scikit-learn: Primarily used for data mining, data analysis, and data science.
  • Numpy: A useful library due to its high performance, especially when dealing with N-dimensional arrays.
  • Pandas: Used for high-level data structures and data extraction and preparation.
  • Matplotlib: In almost every ML problem, we need to plot a graph for better data representation, and matplotlib helps in the plotting of graph in 2D.
  • TensorFlow: It is the go-to library for most as it uses multi-layered nodes which allows to quickly train, setup and deploy artificial neural networks.

 

Below are the steps required to execute a successful Machine Learning project with Python-

  1. Gather data: It is the most important step - collecting data for your project. Remember that the quality and quantity of your data dictates the performance of your ML model.
  2. Clean and prepare data: Correct the missing data and then convert the raw data to the requirements of the model. Finally, divide it into 2 parts: training data and testing data.
  3. Visualize data: Many times, this is the final step of the project. It helps in understanding the data to make the right decisions.
  4. Choose the correct model: Now that you have good knowledge about the data, choose the correct model that suits your algorithm.
  5. Train and test: We had divided the data into training and testing data in Step 2. Now, train the model with the training data and afterward, test its accuracy with the test data.
  6. Adjust parameters: This step is course correction. After gauging how accurate the model is, fine tune the parameters.

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

  1. Consistency is Key: Code every day. 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 from there on out.
  2. Write it out: Writing down a particular thing with your own hands is the key to long term retention of the concept. This tip is especially beneficial for those programmers who are learning Python with the aim of becoming full-time developers. 
  3. Go interactive!: The interactive Python shell is one of the best learning tools. In order to initialize the Python shell, simply open your terminal and type in Python or Python3 into the command line and hit Enter.
  4. Assume the role of a Bug Bounty Hunter: It is inevitable that you will run into bugs. Take up the challenge as a means of learning Python in the best possible way and take pride in becoming a Bug Bounty Hunter.
  5. Surround yourself with others who are learning: 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. You can join various Machine Learning groups in Kuala Lumpur.
  6. Opt for Pair programming: Pair programming in a technique in which two developers work 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. Pair Programming helps developers learn mutually.

Here, we have compiled a list of Python libraries that are best for machine learning:

  • Scikit-learn: Used for data mining, data analysis, and data science.
  • SciPy: Used for Mathematics, engineering, and science (manipulation).
  • Numpy: Provides you with free and fast vector and matrix operations.
  • Keras: When one thinks Neural network, one goes to get the help of Keras.
  • TensorFlow: It uses multi-layered nodes which allow to quickly train, setup and deploy artificial neural networks.
  • Pandas: Provides high-level data structures and is helpful while during data extraction and preparation.
  • Matplotlib: It visualizes data by plotting of graph in 2D.
  • Pytorch: If NLP is our aim, Pytorch is our go-to library. 

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Attended Certified ScrumMaster®(CSM) workshop in May 2018
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I really enjoyed the training session and extremely satisfied. All my doubts on the topics were cleared with live examples. KnowledgeHut has got the best trainers in the education industry. Overall the session was a great experience.

Tilly Grigoletto

Solutions Architect.
Attended Agile and Scrum workshop in May 2018
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The trainer took a practical session which is supporting me in my daily work. I learned many things in that session with live examples.  The study materials are relevant and easy to understand and have been a really good support. I also liked the way the customer support team addressed every issue.

Marta Fitts

Network Engineer
Attended PMP® Certification workshop in May 2018

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 Kuala Lumpur

Machine Learning Course with Python in Kuala Lumpur

The contemporary skyline of the Kuala Lumpur is dotted with the world famous 451m tall Petronas Towers. It is the capital of Malaysia and is packed with lush parks, historical monuments, steel dressed skyscrapers, trendy nightspots, massive shopping malls, and vibrant street markets. Kuala Lumpur, in terms of economy, is a fast-growing metropolitan region in South East Asia. As the economic pivot of the country, it is no surprise that a large number of top-notch companies exist here. They create employment and want to hire skilled employees that can contribute effectively to their overall growth. Organizations are desirous of hiring individuals enrolled for attaining a certification in data analysis using Python course in Kuala Lumpur. For professionals, this is an opportunity and to make sure they can avail it, they must consider joining an e-learning program, conducted by KnowledgeHut, where the online classes will give them an in-depth understanding of the principles and concepts of data analytics and machine learning using Python. Python is used in the deployment of projects of any scale and is the preferred choice for the serious programmer. Python's inherent feature allows easy programming and companies use it to their advantage to build applications. Naturally, they require professionals who are fluent with the medium and hence it is advisable that they consider the machine learning using Python course in Kuala Lumpur. Completing this course will be highly advantageous as it will give professionals a valid and widely accepted credential. According to the Globalization and World Cities Study Group and Network, Kuala Lumpur is rated as an alpha city, and is the only global city in Malaysia. This makes it a hotbed for commercial activity and job seekers flock here for employment. The need for programmers with machine learning and Python capabilities has led to the availability of several online training programs that will arm individuals with the requisite skill sets to become proficient developers.

New Alternative - Python

Python is a high-level programming language used by developers worldwide to develop applications for any scale and size. Serious programmers prefer the use of this language in both machine learning and scientific computing. Along with this space, it is also a chosen medium for science, engineering and business applications. Python is easy to deploy and the design features syntax clarity along with simplified readability and comprehension.

Keeping Ahead of the Curve

A Machine Learning with Python training in Kuala Lumpur is a great investment for developers as it will reap many professional rewards and help them stay ahead of the curve. KnowledgeHut through its online classes facilitates seamless learning under the tutelage of an eminent panel of teachers. The online course will aid individuals in developing core-competence and become able developers. Knowledge Hut Empowers You The KnowledgeHut program on machine learning training using Python is available at an affordable price point in Kuala Lumpur. The online modules are created to enable programmers to appear for an exam with the same ease with which they develop applications.