Machine Learning with Python Training in Kuala Lumpur, Malaysia

Build Python skills with varied approaches to Machine Learning

  • Supervised & Unsupervised Learning, Regression & Classifications and more
  • Advanced ML algorithms like KNN, Decision Trees, SVM and Clustering
  • Build and deploy deep learning and data visualization models in a real-world project
  • 220,000 + Professionals Trained
  • 250 + Workshops every month
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Grow your Machine Learning skills

In this four-week course, you will dive into the basics of machine learning using the well-known programming language, Python. Get introduced to data exploration and discover the various machine learning approaches like supervised and unsupervised learning, regression, and classifications and more.

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Highlights

  • 48 Hours of Live Instructor-Led Sessions

  • 80 Hours of Assignments and MCQs

  • 45 Hours of Hands-On Practice

  • 10 Real-World Live Projects

  • Fundamentals to an Advanced Level

  • Code Reviews by Professionals

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Why Machine Learning?

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Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire in-demand machine learning and Python skills and meet that need.  

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Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

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Get trained by leading practitioners who share best practices from their experience across industries.

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Six months of post-training mentor guidance to overcome challenges in your Data Science career.

Prerequisites

Prerequisites for Machine Learning with Python training

  • Sufficient knowledge of at least one coding language is required.
  • Minimalistic and intuitive, Python is best-suited for Machine Learning training.

Who should attend this course?

Anyone interested in Machine Learning and using it to solve problems

Software or data engineers interested in quantitative analysis with Python

Data analysts, economists or researchers

Machine Learning with Python Course Schedules

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What you will learn in the Machine Learning with Python course

1

Python for Machine Learning

Learn about the various libraries offered by Python to manipulate, preprocess, and visualize data  

2

Fundamentals of Machine Learning

Learn about Supervised and Unsupervised Machine Learning. 

3

Optimization Techniques

Learn to use optimization techniques to find the minimum error in your Machine Learning model

4

Supervised Learning

Learn about Linear and Logistic Regression, KNN Classification and Bayesian Classifiers

5

Unsupervised Learning

Study K-means Clustering  and Hierarchical Clustering

6

Ensemble techniques

Learn to use multiple learning algorithms to obtain better predictive performance 

7

Neural Networks

Understand Neural Network and apply them to classify image and perform sentiment analysis

Skill you will gain with the Machine Learning with Python course

Advanced Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Distribution of data: variance, standard deviation, more

Calculating conditional probability via Hypothesis Testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Logistic Regression models

K-means Clustering and Hierarchical Clustering

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for both regression and classification

Hyper-parameter tuning like regularisation

Ensemble techniques: averaging, weighted averaging, max voting

Bootstrap sampling, bagging and boosting

Building Random Forest models

Finding optimum number of components/factors

PCA/Factor Analysis

Using Apriori Algorithm and key metrics: Support, Confidence, Lift

Building recommendation engines using UBCF and IBCF

Evaluating model parameters

Measuring performance metrics

Using scree plot, one-eigenvalue criterion

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Machine Learning with Python Training Curriculum

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Learning objectives
In this module, you will learn the basics of statistics including:

  • Basics of statistics like mean (expected value), median and mode 
  • Distribution of data in terms of variance, standard deviation, and interquartile range; and explore data and measures and simple graphics analyses  
  • Basics of probability via daily life examples 
  • Marginal probability and its importance with respect to Machine Learning 
  • Bayes’ theorem and conditional probability including alternate and null hypotheses  

Topics

  • Statistical Analysis Concepts  
  • Descriptive Statistics  
  • Introduction to Probability 
  • Bayes’ Theorem  
  • Probability Distributions  
  • Hypothesis Testing and Scores  

Hands-on

  • Learning to implement statistical operations in Excel 

Learning objectives
In the Python for Machine Learning module, you will learn how to work with data using Python:

  • How to define variables, sets, and conditional statements 
  • The purpose of 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 with Python 
  • Data Visualization using Python libraries like matplotlib, seaborn and ggplot 

Topics

  • Python Overview  
  • Pandas for pre-Processing and Exploratory Data Analysis  
  • NumPy for Statistical Analysis  
  • Matplotlib and Seaborn for Data Visualization  
  • Scikit Learn 

Learning objectives
Get introduced to Machine Learning via real-life examples and the multiple ways in which it affects our society. You will learn:

  • Various algorithms and models like Classification, Regression, and Clustering.  
  • Supervised vs Unsupervised Learning 
  • How Statistical Modelling 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 and Underfitting  

Learning objectives
Gain an understanding of various optimisation techniques such as:

  • Batch Gradient Descent 
  • Stochastic Gradient Descent 
  • ADAM 
  • RMSProp

Topics

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

Learning objectives
In this module you will learn about Linear and Logistic Regression with Stochastic Gradient Descent via real-life case studies

  • Hyper-parameters tuning like learning rate, epochs, momentum, and class-balance 
  • The concepts of Linear and Logistic Regression with real-life case studies 
  • How KNN can be used for a classification problem with a real-life case study on KNN Classification  
  • About Naive Bayesian Classifiers through another case study 
  • How Support Vector Machines can be used for a classification problem 
  • About hyp

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

  • Build a regression model to predict the property prices using optimization techniques like gradient descent based on attributes describing various aspect of residential homes 
  • Use 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 based on the health metrics 
  • Use Naive Bayesian technique for text classifications to predict which incoming messages are spam or ham 
  • Build models to study the relationships between chemical structure and biodegradation of molecules to correctly classify if a chemical is biodegradable or non-biodegradable 

Learning objectives
Learn about unsupervised learning techniques:

  • K-means Clustering  
  • Hierarchical Clustering  

Topics

  • Clustering approaches  
  • K Means clustering  
  • Hierarchical clustering  
  • Case Study 

Hands-on

  • Perform a real-life case study on K-means Clustering  
  • Use K-Means clustering to group teen students into segments for targeted marketing campaigns

Learning objectives
Learn the ensemble techniques which enable you to build machine learning models including:

  • Decision Trees for regression and classification problems through a real-life case study 
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID 
  • Basic ensemble techniques like averaging, weighted averaging and max voting 
  • You will learn about bootstrap sampling and its advantages followed by bagging and how to boost model performance with Boosting 
  • Random Forest, with a real-life case study, and how it helps avoid overfitting compared to decision trees 
  • The Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis 
  • The comprehensive techniques used to find the optimum number of components/factors using scree plot, one-eigenvalue criterion 
  • PCA/Factor Analysis via a case study 

Topics

  • Decision Trees with a Case Study 
  • Introduction to Ensemble Learning  
  • Different Ensemble Learning Techniques  
  • Bagging  
  • Boosting  
  • Random Forests  
  • Case Study  
  • PCA (Principal Component Analysis)  
  • PCA 
  • Its Applications  
  • Case Study

Hands-on

  • Build a model to predict the Wine Quality using Decision Tree (Regression Trees) based on the composition of ingredients 
  • Use AdaBoost, GBM, and Random Forest on Lending Data to predict loan status and ensemble the output to see your results 
  • Apply Reduce Data Dimensionality on a House Attribute Dataset to gain more insights and enhance modelling.  

Learning objectives
Learn to build recommendation systems. You will learn about:

  • Association Rules 
  • Apriori Algorithm to find out strong associations using key metrics like Support, Confidence and Lift 
  • UBCF and IBCF including how they are used in Recommender Engines 

Topics 

  • Introduction to Recommendation Systems  
  • Types of Recommendation Techniques  
  • Collaborative Filtering  
  • Content-based Filtering  
  • Hybrid RS  
  • Performance measurement  
  • Case Study

Hands-on

  • Build a Recommender System for a Retail Chain to recommend the right products to its customers 

FAQs on the Machine Learning with Python Course

Machine Learning with Python Training

KnowledgeHut’s Machine Learning with Python workshop is focused on helping professionals gain industry-relevant Machine Learning expertise. The curriculum has been designed to help professionals land lucrative jobs across industries. At the end of the course, you will be able to: 

  • Build Python programs: distribution, user-defined functions, importing datasets and more 
  • Manipulate and analyse data using Pandas library 
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot 
  • Build data distribution models: variance, standard deviation, interquartile range 
  • Calculate conditional probability via Hypothesis Testing 
  • Perform analysis of variance (ANOVA) 
  • Build linear regression models, evaluate model parameters, and measure performance metrics 
  • Use Dimensionality Reduction 
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics 
  • Perform K-means Clustering and Hierarchical Clustering  
  • Build KNN algorithm models to find the optimum value of K  
  • Build Decision Tree models for both regression and classification problems  
  • Use ensemble techniques like averaging, weighted averaging, max voting 
  • Use techniques of bootstrap sampling, bagging and boosting 
  • Build Random Forest models 
  • Find optimum number of components/factors using scree plot, one-eigenvalue criterion 
  • Perform PCA/Factor Analysis 
  • Build Apriori algorithms with key metrics like Support, Confidence and Lift 
  • Build recommendation engines using UBCF and IBCF 

The program is designed to suit all levels of Machine Learning expertise. From the fundamentals to the advanced concepts in Machine Learning, the course covers everything you need to know, whether you’re a novice or an expert. 

To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities. 

This immersive and interactive workshop with an industry-relevant curriculum, capstone project, and guided mentorship is your chance to launch a career as a Machine Learning expert. The curriculum is split into easily comprehensible modules that cover the latest advancements in ML and Python. The initial modules focus on the technical aspects of becoming a Machine Learning expert. The succeeding modules introduce Python, its best practices, and how it is used in Machine Learning.  

The final modules deep dive into Machine Learning and take learners through the algorithms, types of data, and more. In addition to following a practical and problem-solving approach, the curriculum also follows a reason-based learning approach by incorporating case studies, examples, and real-world cases.

Yes, our Machine Learning with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job. 

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course.

The Machine Learning with Python course is ideal for:
  1. Anyone interested in Machine Learning and using it to solve problems  
  2. Software or Data Engineers interested in quantitative analysis with Python  
  3. Data Analysts, Economists or Researchers 

There are no prerequisites for attending this course, however prior knowledge of elementary Python programming and statistics could prove to be handy. 

To attend the Machine Learning with Python training program, the basic hardware and software requirements are as mentioned below

Hardware requirements 

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors 
  • 4 GB RAM 
  • 10 GB of free space  

Software Requirements  

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox  

System Requirements 

  • 32 or 64-bit Operating System 
  • 8 GB of RAM 

On adequately completing all aspects of the Machine Learning with Python course, you will be offered a course completion certificate from KnowledgeHut.  

In addition, you will get to showcase your newly acquired Machine Learning skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects. 

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a Machine Learning project in the real business world.  

Workshop Experience

The Machine Learning with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.  

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.    

The Machine Learning with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Machine Learning. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects. 

Our Machine Learning course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.  

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Machine Learning with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it. 

Schedules for our upcoming workshops in Machine Learning with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.   

You will receive a registration link from PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience platform and start your educational journey.  

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing. 

In case of any queries, our support team is available to you 24/7 via the Help and Support section on PRISM. You can also reach out to your workshop manager via group messenger. 

If you miss a class, you can access the class recordings from PRISM at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us on support@knowledgehut.com and we will be happy to get back to you. 

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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. Iverson Iverson Associates Sdn Bhd
  2. 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
  • Kuala Lumpur Artificial Intelligence Meetup
  • Kuala Lumpur AI & Machine Learning- EML Meetup
  • TensorFlow and Deep Learning Malaysia

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. 

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.

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