Machine Learning with Python Training in Johannesburg, South Africa

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
  • 250,000 + 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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  • 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?


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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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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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  


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


  • 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 


  • 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 


  • 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


  • 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


  • Linear Regression Case Study  
  • Logistic Regression Case Study  
  • KNN Classification Case Study  
  • Naive Bayesian classifiers Case Study  
  • SVM - Support Vector Machines Case Study


  • 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  


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


  • 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 


  • 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


  • 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 


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


  • 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 and we will be happy to get back to you. 

The KnowledgeHut Edge

Learn by Doing

Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on. 

Real-World Focus

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

Industry Experts

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

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

Continual Learning Support

Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

Exclusive Post-Training Sessions

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


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.

What you will learn in the Machine Learning with Python course

Python for Machine Learning

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

Fundamentals of Machine Learning

Learn about Supervised and Unsupervised Machine Learning.

Optimization Techniques

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

Supervised Learning

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

Unsupervised Learning

Study K-means Clustering  and Hierarchical Clustering.

Ensemble techniques

Learn to use multiple learning algorithms to obtain better predictive performance .

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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Invest in forward-thinking data talent to leverage data’s predictive power, craft smart business strategies, and drive informed decision-making.  

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Learn Machine Learning

Learn Machine Learning in Johannesburg, South Africa

Machine Learning is the application of Artificial Intelligence concepts that makes systems automatically learn, perform and improve tasks, without the need of human input. The idea is to develop systems that can work on their own. The algorithms used in Machine Learning can be categorized into two categories:

  1. Supervised algorithms: These algorithms take the learnings from the past data and apply them to the new data made available by making use of labelled examples and help predict future events. 
  2. Unsupervised algorithms: These are the type of algorithms that are used when the information required for the training of the system and the algorithm are either not labelled or have not been classified. Such systems, while being unable to figure out the correct result, are able to explore the available data and draw inferences from the available datasets in order to describe and identify hidden structures from unlabelled data.

The concept of Machine Learning is about systems taking in a huge amount of data, analysing it and solving problems through training to obtain an outcome. It is a way for humans to be able to solve problems, without having to actually know and understand what the problem really is, as well as why a particular approach to a problem actually works.

  • It gives results

Machines work faster than human brains do and solve problems faster than we ever can. They systematically work out, resolve and simultaneously evaluate all the options in order to obtain the best possible outcome or result.

  • Used in a lot of applications today

All industries, starting from health care, nursing, transport, customer service to government and financial institutions are benefitting from Machine Learning, which is what makes it an indispensable part of our society as it stands today.

Johannesburg is the largest city in South Africa and boasts of 543 Tech startups.  It is also at the centre of the global gold and diamond trade. Some of the benefits of learning Machine Learning in Johannesburg include the following:

  • Great job opportunities: Machine Learning is estimated to be worth $20 bn by 2025. Currently, there are 130+ jobs AI jobs in Johannesburg.With a whopping 344% growth in job postings, machine learning engineer is an extremely promising position. 
  • Machine Learning engineers earn pretty: Fueled by AI, demand for machine learning skills is growing. The average salary for an Entry-Level Data Scientist with Machine Learning skills in South Africa is R323,504
  • Demand for Machine Learning skills is increasing: Artificial intelligence is becoming an imperative for businesses that want to maintain a competitive edge. All these companies are looking for skilled Machine Learning engineers. The demand as well as the pay for professionals with Machine Learning skills is only going to increase in the future.

Machine learning is a huge field which is expanding day by day due to its diverse and open culture. It has great scope in cities like Johannesburg. Below are the steps you can follow:

  • Structured Plan: Create a structured plan on the topics that you want to learn. 
  • Prerequisite: Pick a programming language that you are comfortable with and start polishing your mathematical and statistical skills as ML involves dealing with statistical data.
  • Learning: Start learning according to the plan created in step (1). Refer to various reliable sources online or from books. You can also join offline courses in  Johannesburg. Understanding the workflow of ML algorithms is a must.
  • Implementation: We can never truly learn unless we learn how to implement our skills. Try to build projects using the algorithms you learned. Take datasets from the internet and start solving problems. Take part in online competitions such as Kaggle to learn more.

You can learn ML by yourself by following the steps below:

  • Adjust your mindset: Take cognizance of what is holding you back from achieving your goals in Machine Learning. Read through all the related articles and connect with machine learning professionals to know more about the field. You can join meetups or conferences in Johannesburg to understand the scope and potential opportunities. 
  • Pick a process that suits you best: Choose a process that is best suited to your abilities and even enhances them. Do not worry if you only possess basic knowledge of it. You will learn more as you move along.
  • Pick a tool: Tools are of vital importance in ML. Pick one that goes with your capabilities. Beginners are recommended to opt for the Weka Workbench. Intermediate level learners are recommended the Python Ecosystem. Advanced level learners are recommended the R Platform.
  • Practice on Datasets: Look for data sets in books or online and solve them/work on them to brush up your skills. This will give you an idea of the problems you will be solving and helping you put your knowledge to test.
  • Build your own portfolio: Making a portfolio or the work you’ve done- however immature it may seem to you is a great way to keep track of your own work as well as make your CV strong.

In order to understand the concepts of Machine Learning, it is important to know the following:

  • Programming languages: To be able to comfortably work with programming languages such as Python, Java, Scala etc is an important prerequisite before you embark upon your Machine Learning journey. 
  • Database skills: A prior knowledge and experience of working with MySQL as well as relational databases is essential to carry out data analysis on a database.
  • Machine Learning visualization tools: A basic knowledge and understanding of some of the visualization tools will turn out to be helpful while you are applying the concepts of Machine Learning in real life. 
  • Machine learning frameworks: Several statistical, as well as mathematical algorithms, are made use of in order to design a Machine Learning model to learn from the input data and come to a prediction for a given data set.
  • Mathematical skills: It is through the mathematical algorithms and concepts that the data is processed, analyzed and used in order to form a Machine Learning model. The following is a list of some of the mathematical concepts that a student of Machine learning must know:
    • 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

If you want your ML project to be executed successfully, we have compiled the steps for the same below:

  • Gathering data: The most important step is getting the correct data for your project upon which you will be performing your ML magic. The quality and quantity of your data are directly proportional to the performance of your model.
  • Cleaning and preparing data: We first carefully clean the data which means correcting the missing data that we may have and secondly preparing the data. Preparing the data means specifically feature engineering. Convert the raw data to the data our model is expecting and finally divide the data into 2 parts: training data and testing data.
  • Visualize the data: Visualization helps in understanding the kind of data that we have in our hands. This will help to make a good selection of model accordingly.
  • Choosing the correct model: After visualizing of data, we have a good knowledge about how this data can be harvested and which model or algorithm is best suited to do so. 
  • Train and test: We have our prepared data ready to be injected into our chosen model. As in earlier step we have divided our data into training and testing data, we now train our model with the training data and after it is trained, its accuracy is tested.
  • Adjust parameters: Fine tuning the model is an important step in the process. It helps provide final touches and make sure everything runs smooth. One example is changing the number of neurons in a neural network.

It is vital for learners to understand and ingrain the concepts of Machine Learning algorithms. Here’s how to do that:

  • List the various Machine Learning algorithms: While each algorithm is unique and important in its own way, it is vital for you to decide and list down some algorithms that you want to start with. This particular activity will also help you build familiarity with the various classes and types of algorithms that are available and prepare you for what lies ahead.
  • Apply the Machine Learning algorithms that you listed down: Apart from learning the basic concepts and theory of Machine Learning algorithms, it is also important to practice Applied Machine Learning. Start building up an intuition for the various Machine Learning algorithms such as Support Vector Machines, decision trees etc. 
  • Describe these Machine Learning algorithms: A thorough analysis and understanding of Machine Learning algorithms will help you create a description of these algorithms. Being able to describe and analyze every algorithm will increase your knowledge of algorithms.
  • Implement Machine Learning Algorithms: By implementing the algorithm yourself, you will be able to understand the micro decisions involved in the implementation of Machine Learning concepts.
  • Experiment on Machine Learning Algorithms: Understanding the parameters at play during the working on algorithm is a great way to be able to customize its working in order to suit your needs while working on a problem. It also enables you to better scale and adapt an algorithm to suit your needs in the future.

Machine Learning Algorithms

The K Nearest Neighbours algorithm is a simplistic and uncomplicated Machine Learning algorithm.

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

The answer to this question depends upon what you intend to do with your Machine Learning knowledge. 

  • In case you wish to use the existing Machine Learning algorithms, you can study Machine Learning without knowing any classic algorithms. 
  • If you wish to innovate with the help of Machine Learning, having some basic knowledge of the workings and uses of algorithms is a critical prerequisite. The knowledge regarding the correctness of an algorithm, how much time an algorithm takes, its complexity, costs involved etc are all concepts that you will get familiar with, only after you have sufficient knowledge of the algorithms themselves. 

Machine Learning algorithms are basically of three types:

  • 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 
    • Logistic Regression 
    • Classification and Regression Trees (CART)
    • Naïve Bayes 
    • K-Nearest Neighbours
  • Unsupervised Learning: In these types of problems, only the input variables are given and not the output ones. Thus, the underlying structure of the given data sets is analysed to reveal possible associations and clusters. Examples of such algorithms include-
    • Apriori 
    • K-Means 
    • Principal Component Analysis (PCA)
  • Ensemble Learning: By building on the premise that groups of learners perform better than just one, these algorithms combine the results of each learner and then analyse them as a whole to obtain a fairly accurate representation of the actual outcome. Examples of such algorithms include-
    • Bagging
    • Boosting

The simplest of machine learning algorithms solves the problems like simple recognition is k-nearest neighbor algorithm. It is a classification algorithm though can be used for regression as well. It classifies based on the similarity measure and is non-parametric. 

Below are some of the most effective ways to choose the right machine learning algorithm:

  • Understanding your data: It is very important to understand your data to find the correct algorithm. Visualize your data by plotting graphs. Try to find correlation among the data indicating strong relationships. Clean your data if you find any missing data or bad data.
  • Get the intuition about the task: Many times we don’t understand the underlying aim of the task, and that’s why ML is needed to solve the problem in the first place. Then you need to see which kind of learning will help your model complete the task. 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 out there work on high-end machines and require high data storage and manipulation resources. Constraints can be in the hardware or software as well. Depending upon time constraints, whether we can allow training phase to be of long duration or not, or testing phase to be short or long is an important choice to be made.
  • Find available algorithms: This is the last step. Find an algorithm following the above-mentioned steps and finally implement the algorithm. 

The process to follow for the implementation of Machine Learning algorithm should include the following:

  • Select a programming language: This decision will influence the standard libraries as well as the APIs that you are going to make use of in your implementation.
  • Select the algorithm that you wish to implement: Decide on all of the specifics of the algorithm and be as decisive and precise as possible. This means that you should decide on everything from the type of algorithm, the classes as well as the specific implementation and the description of what you wish to implement.
  • Select the problem you wish to work upon: Next, move on to the selection of the canonical problem set that you are going to use in order to test and validate the efficiency and correctness of your algorithm implementation. 
  • Research the algorithm that you wish to implement: Taking multiple descriptions and outlooks on your algorithm is important as it helps you give a total overall perspective of the uses and implementation methodologies of your algorithm. It also helps you overcome roadblocks and any wrong assumptions that you may have made about your algorithm.

Below are some of the most important topics you must familiarize yourself with:

  • Decision Trees: Decision trees aid the system in deciding on which features are to be chosen and which conditions are to be used in order for splitting.Advantages of decision tree methods:
    • They are easy to understand, visualize and interpret.
    • Decision trees are not affected by non-linear relationships between parameters.
    • Decision trees require minimal efforts in the direction of data preparation from the user.
    • Decision trees are able to handle and analyze both categorical as well as numerical data.
  • Support Vector Machines: Support Vector Machines are classification methodologies that provide better accuracy in classification problems.Advantages of Support Vector Machines:
    • Support Vector Machines provide guaranteed optimality in the solutions that they provide, owing to the nature of convex optimizations.
    • They are useful in both, Linearly Separable (Hard margin) as well as Non-linearly separable (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, which are able to undertake the process of feature mapping by carrying out simple dot products.
  • Naive Bayes: The Naive Bayes algorithm 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 the data.Advantages of the Naive Bayes algorithm:
    • It is very simplistic technique of classification  
    • It requires less training data as compared to other techniques used for classification.
    • It is highly scalable. 
  • Random Forest algorithm: The Random Forest algorithm 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.
    Advantages of the Random Forest algorithm:

    • It is 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 a very easy to use and handy algorithm.
    • The number of hyper parameters included in a random forest are not high and are relatively easy to understand.

Machine Learning Conference in Johannesburg, South Africa


Conference nameDateVenue
1.Machine learning and Deep Learning DayOctober 25th 2019Gallagher convention centre
2.SingularityUOctober 16-17th 2019Kyalami international convention centre
  1.  Machine learning and Deep Learning Day, Johannesburg
    1. About the conference: The conference aims to discuss the roles, future and challenges of Machine learning and Deep Learning. 
    2. Event Date: October 25th 2019
    3. Venue: Gallagher convention centre 
    4. Days of Program: 1
    5. Timings: 8:45 – 17:00
    6. How many speakers: 7
    7. With whom can you Network in this Conference:
      • Data Engineers/Developers/Scientists
      • Analytics Professionals
      • Startup Professionals
      • Scientists/Researchers
      • Professors
      • President/Vice president
      • Chairs/Directors
    8.  Registration cost:
      • Group of three or more (Early Bird): ZAR 4000
      • Group of three or more (Standard): ZAR 4500
      • Individual (Early Bird): ZAR 6000
      • Individual (Standard): ZAR 7200
    9. Who are the major sponsors:
      • Top market group
      • Tech samvad
      • Crixfeed
      • Testing Olympiad
      • 1.21GWS Insights
      • Michael crouch innovation centre
      • Aricent
      • Bitfwd
      • BOT mantra
      • Call strat
      • Makpro tech 
      • Agile testing alliance

  2. SingularityU, Johannesburg
    1. About the conference: The conference will have a discussion on the human impact and connection to machine learning and how it may affect us in the future.
    2. Event Date: 16-17th October, 2019
    3. Venue: Kyalami International Convention centre
    4. Days of Program: 2
    5. With whom can you Network in this Conference:
      • Investors 
      • C-Level suite execs
      • Government officials 
      • Innovators
      • Inventors
      • Entrepreneurs
      • Students and future leaders
    6. Registration cost: 
      • Early : 16,650
      • Standard 18,500
      • Late: 20,350
    7. Who are the major sponsors
      • Development bank of south Africa 
      • Deloitte
      • Absa
      • MTN
S.NoConference NameDateVenue
1.AI and Machine Learning Half Day HackathonOctober 18, 2017Entelect Training Centre, Melrose Arch
2.Artificial Intelligence and Predictive Analysis Conference 2018November 28 2018 - November 29 2018Indaba Hotel, Four Ways, Johannesburg
  1. AI and Machine Learning Half Day Hackathon, Johannesburg
    1. About the conference: The conference provided theories, cases and practical implementation of various concepts of Machine Learning.
    2. Event Date: October 18, 2017
    3. Venue: Entelect Training Centre, Melrose Arch
    4. Days of Program: 1
    5. Timings: 1:00 PM - 6:00 PM
    6. Purpose: The purpose of this conference was to learn about the concepts of Machine Learning and Artificial Intelligence. 
    7. Registration cost: SAMRA Members: R500 ex VAT, Non-members R1000 ex VAT
  1. Artificial Intelligence and Predictive Analysis Conference 2018, Johannesburg

    1. About the conference: The conference explored the subject of Artificial Intelligence and how to apply this knowledge in Predictive Analysis.  
    2. Event Date: November 28 2018 - November 29 2018
    3. Venue: Indaba Hotel, Four Ways, Johannesburg
    4. Days of Program: 2

Machine Learning Engineer Jobs in Johannesburg, South Africa

A Machine Learning Engineer is responsible for the following:

  • Design and develop ML systems
  • Implement the ML algorithms
  • Data Analysis
  • Build data sets
  • Troubleshoot the Machine Learning Model Testing Issues

Known as the country’s fast-growing startup space, Johannesburg is the provincial capital of Gauteng, the wealthiest province in the country. It is also home to a seasoned investor scene with players like AngelHub Ventures, Africa Angels Network, the South African Business Angel Network, Jozi Angels, etc. It is home to many leading AI startups, such as Flock, Flow,, Aajoh, etc. By the looks of the research, things are only getting bigger in Johannesburg. There are several companies in Johannesburg that are looking for Machine Learning Engineers to join their team and help gain insights from the data. 

In Johannesburg, the following companies have open positions for Machine Learning engineers:

  • Liza Van den Berg
  • Siemens PLC
  • Cisco
  • Hitachi Vantara
  • Jason Pretorius
  • Nigel Frank International

In Johannesburg, you can network with other Machine Learning Engineer through the following professional groups:

  • Johannesburg Machine Learning
  • Machine Intelligence Institute of Africa
  • Johannesburg Artificial Intelligence Meetup
  • Machine Learning Johannesburg

The following ML job roles are in demand in 2019:

  • Machine Learning Engineer
  • Cloud Architect
  • Data Scientist
  • Cyber Security Analyst
  • Data Architect
  • Data Mining Specialist

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

  • Conferences and tech talks
  • Meetups
  • Online platforms like LinkedIn

Machine Learning with Python Johannesburg, South Africa

Here's how you can get started with Python for use in Machine Learning:

  1. Adjust your mindset to Machine Learning Concepts.
  2. Download and install the Python SciPy Kit for Machine learning and install all useful packages.
  3. Understand tools 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.
  5. Practice datasets to gain a better understanding of the concepts.
  6. Start small and work your way to bigger and more complicated projects.
  • Scikit-learn: Used for data mining, data analysis and in data science as well.
  • Numpy: Useful with N-dimensional arrays.
  • Pandas: Used in high-level data structures as well as data extraction and preparation.
  • Matplotlib: Used for plotting of graph in 2D.
  • TensorFlow: If you are using deep learning in your project then this library, created by Google, is the go-to library as it uses multi-layered nodes which allow us to quickly train, setup and deploy artificial neural networks.

Follow the below steps to execute a successful Machine Learning project with Python (ML Project): 

  1. Collect Data: Gather data that is suitable for a specific project. The quality and quantity of data will determine the performance of your ML model.
  2. Clean data: Clean and prepare your data. This is done so that the data corresponds to the model of ML that is being used. The data is then divided into 2 parts: training data and testing data.
  3. Visualize the data and choose the right model:  This will help in understanding the data and selecting the correct model. The kind of ML model that will be ideal for data harvesting is important as it determines the performance of the algorithm.
  4. Training and testing: The training data set is passed through the model to train it with the new set of data, then the accuracy of the model is checked.
  5. Adjust parameters: After determining the accuracy of the model, the parameters are fine-tuned. 

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

  • Code every day. Consistency is very important when you are learning a new programming language. 
  • Make good study-notes. Writing down stuff by hand helps you plan out your code on paper before you move to actually implementing it on your computer.
  • Go interactive! The interactive Python shell is one of the best learning tools, irrespective of whether you are writing code for the first time, learning about Python data structures such as dictionaries, list, strings etc or debugging an application.
  • Surround yourself with other people who are learning. Join meetups or participate in bootcamps in Johannesburg to connect with professionals.

Here is a list of Python libraries which are best for machine learning:

  • Scikit-learn: Used majorly for data mining, data analysis and in data science as well.
  • SciPy: Has packages for both Maths and engineering.
  • Numpy: free and fast vector and matrix operations, performed with efficiency.
  • Keras: When one thinks Neural network, one goes to get the help of Keras.
  • TensorFlow: It uses multi-layered nodes which allow us to quickly train, setup and deploy artificial neural networks.
  • Pandas: Provides high-level data structures. Significantly helpful while during data extraction and preparation.
  • Matplotlib: It helps in the visualization of data by plotting of graph in 2D.
  • Pytorch: If NLP is our aim, Pytorch is our go-to library. 

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Lea Kirsten Senior Developer

The learning methodology put it all together for me. I ended up attempting projects I’ve never done before and never thought I could. 

Attended Back-End Development Bootcamp workshop in July 2021

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The learning methodology put it all together for me. I ended up attempting projects I’ve never done before and never thought I could.

Attended Full-Stack Development Bootcamp workshop in May 2021

Madeline R Developer

I know from first-hand experience that you can go from zero and just get a grasp on everything as you go and start building right away. 

Attended Back-End Development Bootcamp workshop in April 2021

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The learning system set up everything for me. I wound up working on projects I've never done and never figured I could. 

Attended Front-End Development Bootcamp workshop in April 2021

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All my questions were answered clearly with examples. I really enjoyed the training session and am extremely satisfied with the overall experience. Looking forward to similar interesting sessions. KnowledgeHut's interactive training sessions are world class and I highly recommend them .

Attended PMP® Certification workshop in July 2020

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The course which I took from Knowledgehut was very useful and helped me to achieve my goal. The course was designed with advanced concepts and the tasks during the course given by the trainer helped me to step up in my career. I loved the way the technical and sales team handled everything. The course I took is worth the money.

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The customer support was very interactive. The trainer took a very practical oriented session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, I would be able to sit for the exam with confidence.

Attended Agile and Scrum workshop in April 2020

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Machine Learning with Python Course in Johannesburg

Throughout the 80?s and 90?s Johannesburg went into a state of decline with poverty, crime, and racial tensions at their peak. But the city managed to put these in the past and surged ahead in the new millennium. Johannesburg today is a beautiful city with choc o bloc office and residential buildings, vibrant cultural districts with posh restaurants, museums, theatres, and a bustling night life. Among the world?s leading financial centres, Johannesburg accounts for a significant portion of South Africa?s economy. It houses the JSE which is Africa?s largest stock exchange and several manufacturing, technology, banking, insurance, trade, retail and real estate companies. A major transit point, it also has a well-connected urban transport system. The city has several museums, art galleries, and world heritage sites such as the Cradle of Humankind, Sterkfontein fossil site, and several others. Professionals who wish to thrive in their career would find that they can do well here, with certifications such as PRINCE2, PMP, PMI-ACP, CSM, CEH, CSPO, Scrum & Agile, MS courses and others. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.

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