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MODULE 1 : ARTIFICIAL INTELLIGENCE OVERVIEW
• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence
• Why Artificial Intelligence Now?
• Areas Of Artificial Intelligence
• AI Vs Data Science Vs Machine Learning
MODULE 2 : DEEP LEARNING INTRODUCTION
• Deep Neural Network
• Machine Learning vs Deep Learning
• Feature Learning in Deep Networks
• Applications of Deep Learning Networks
MODULE3 : TENSORFLOW FOUNDATION
• TensorFlow Structure and Modules
• Hands-On:ML modeling with TensorFlow
MODULE 4 : COMPUTER VISION INTRODUCTION
• Image Basics
• Convolution Neural Network (CNN)
• Image Classification with CNN
• Hands-On: Cat vs Dogs Classification with CNN Network
MODULE 5 : NATURAL LANGUAGE PROCESSING (NLP)
• NLP Introduction
• Bag of Words Models
• Word Embedding
• Hands-On:BERT Algorithm
MODULE 6 : AI ETHICAL ISSUES AND CONCERNS
• Issues And Concerns Around Ai
• Ai And Ethical Concerns
• Ai And Bias
• Ai:Ethics, Bias, And Trust
MODULE 1 : PYTHON BASICS
• Introduction of python
• Installation of Python and IDE
• Python Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
MODULE 2 : PYTHON CONTROL STATEMENTS
• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements
MODULE 3 : PYTHON DATA STRUCTURES
• Basic data structure in python
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods
MODULE 4 : PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions
MODULE 1 : OVERVIEW OF STATISTICS
• Introduction to Statistics
• Descriptive And Inferential Statistics
• Basic Terms Of Statistics
• Types Of Data
MODULE 2 : HARNESSING DATA
• Random Sampling
• Sampling With Replacement And Without Replacement
• Cochran's Minimum Sample Size
• Types of Sampling
• Simple Random Sampling
• Stratified Random Sampling
• Cluster Random Sampling
• Systematic Random Sampling
• Multi stage Sampling
• Sampling Error
• Methods Of Collecting Data
MODULE 3 : EXPLORATORY DATA ANALYSIS
• Exploratory Data Analysis Introduction
• Measures Of Central Tendencies: Mean,Median And Mode
• Measures Of Central Tendencies: Range, Variance And Standard Deviation
• Data Distribution Plot: Histogram
• Normal Distribution & Properties
• Z Value / Standard Value
• Empherical Rule and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
• Covariance & Correlation
MODULE 4 : HYPOTHESIS TESTING
• Hypothesis Testing Introduction
• P- Value, Critical Region
• Types of Hypothesis Testing
• Hypothesis Testing Errors : Type I And Type II
• Two Sample Independent T-test
• Two Sample Relation T-test
• One Way Anova Test
• Application of Hypothesis testing
MODULE 1: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
MODULE 2: PYTHON NUMPY PACKAGE
• Introduction to Numpy Package
• Array as Data Structure
• Core Numpy functions
• Matrix Operations, Broadcasting in Arrays
MODULE 3: PYTHON PANDAS PACKAGE
• Introduction to Pandas package
• Series in Pandas
• Data Frame in Pandas
• File Reading in Pandas
• Data munging with Pandas
MODULE 4: VISUALIZATION WITH PYTHON - Matplotlib
• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN
• Seaborn: Basic Plot
• Advanced Python Data Visualizations
MODULE 6: ML ALGO: LINEAR REGRESSION
• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python
MODULE 7: ML ALGO: LOGISTIC REGRESSION
• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python
MODULE 8: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Modeling in Python
MODULE 9: ML ALGO: KNN
• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python
MODULE 1: FEATURE ENGINEERING
• Introduction to Feature Engineering
• Feature Engineering Techniques: Encoding, Scaling, Data Transformation
• Handling Missing values, handling outliers
• Creation of Pipeline
• Use case for feature engineering
MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python
MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)
• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
MODULE 4: ML ALGO: DECISION TREE
• Introduction to Decision Tree & Random Forest
• How it works
• Modeling and Evaluation in Python
MODULE 5: ENSEMBLE TECHNIQUES - BAGGING
• Introduction to Ensemble technique
• Bagging and How it works
• Modeling and Evaluation in Python
MODULE 6: ML ALGO: NAÏVE BAYES
• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python
MODULE 7: GRADIENT BOOSTING, XGBOOST
• Introduction to Boosting and XGBoost
• How it works?
• Modeling and Evaluation of in Python
MODULE 1: TIME SERIES FORECASTING - ARIMA
• What is Time Series?
• Trend, Seasonality, cyclical and random
• Stationarity of Time Series
• Autoregressive Model (AR)
• Moving Average Model (MA)
• ARIMA Model
• Autocorrelation and AIC
• Time Series Analysis in Python
MODULE 2: SENTIMENT ANALYSIS
• Introduction to Sentiment Analysis
• NLTK Package
• Case study: Sentiment Analysis on Movie Reviews
MODULE 3: REGULAR EXPRESSIONS WITH PYTHON
• Regex Introduction
• Regex codes
• Text extraction with Python Regex
MODULE 4: ML MODEL DEPLOYMENT WITH FLASK
• Introduction to Flask
• URL and App routing
• Flask application – ML Model deployment
MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL
• MS Excel core Functions
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Data Table
• Goal Seek Analysis
• Pivot Table
• Solving Data Equation with EXCEL
MODULE 6: AWS CLOUD FOR DATA SCIENCE
• Introduction of cloud
• Difference between GCC, Azure,AWS
• AWS Service ( EC2 instance)
MODULE 7: AZURE FOR DATA SCIENCE
• Introduction to AZURE ML studio
• Data Pipeline
• ML modeling with Azure
MODULE 8: INTRODUCTION TO DEEP LEARNING
• Introduction to Artificial Neural Network, Architecture
• Artificial Neural Network in Python
• Introduction to Convolutional Neural Network, Architecture
• Convolutional Neural Network in Python
MODULE 1: DATABASE INTRODUCTION
• DATABASE Overview
• Key concepts of database management
• Relational Database Management System
• CRUD operations
MODULE 2: SQL BASICS
• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation
MODULE 3: DATA TYPES AND CONSTRAINTS
• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment
MODULE 4: DATABASES AND TABLES (MySQL)
• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table
MODULE 5: SQL JOINS
• Inner join
• Outer join
• Left join
• Right join
• Cross join
• Self join
• Windows functions: Over, Partition , Rank
MODULE 6: SQL COMMANDS AND CLAUSES
• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries
MODULE 7: DOCUMENT DB/NO-SQL DB
• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
MODULE 1: GIT INTRODUCTION
• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture
MODULE 2: GIT REPOSITORY and GitHub
• Git Repo Introduction
• Create New Repo with Init command
• Git Essentials: Copy & User Setup
• Mastering Git and GitHub
MODULE 3: COMMITS, PULL, FETCH AND PUSH
• Code commits
• Pull, Fetch and conflicts resolution
• Pushing to Remote Repo
MODULE 4: TAGGING, BRANCHING AND MERGING
• Organize code with branches
• Checkout branch
• Merge branches
• Editing Commits
• Commit command Amend flag
• Git reset and revert
MODULE 5: GIT WITH GITHUB AND BITBUCKET
• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
MODULE 1: BIG DATA INTRODUCTION
MODULE 2: HDFS AND MAP REDUCE
MODULE 3: PYSPARK FOUNDATION
MODULE 4: SPARK SQL and HADOOP HIVE
MODULE 1: TABLEAU FUNDAMENTALS
• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies
MODULE 2: POWER-BI BASICS
• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION
MODULE 3 : DATA TRANSFORMATION TECHNIQUES
• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values
MODULE 4 : CONNECTING TO VARIOUS DATA SOURCES
• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model
MODULE 1: NEURAL NETWORKS
• Structure of neural networks
• Neural network - core concepts(Weight initialization)
• Neural network - core concepts(Optimizer)
• Neural network - core concepts(Need of activation)
• Neural network - core concepts(MSE & RMSE)
• Feed forward algorithm
• Backpropagation
MODULE 2: IMPLEMENTING DEEP NEURAL NETWORKS
• Introduction to neural networks with tf2.X
• Simple deep learning model in Keras (tf2.X)
• Building neural network model in TF2.0 for MNIST dataset
MODULE 3: DEEP COMPUTER VISION - IMAGE RECOGNITION
• Convolutional neural networks (CNNs)
• CNNs with Keras-part1
• CNNs with Keras-part2
• Transfer learning in CNN
• Flowers dataset with tf2.X(part-1)
• Flowers dataset with tf2.X(part-2)
• Examining x-ray with CNN model
MODULE 4 : DEEP COMPUTER VISION - OBJECT DETECTION
• What is Object detection
• Methods of Object Detections
• Metrics of Object detection
• Bounding Box regression
• labelimg
• RCNN
• Fast RCNN
• Faster RCNN
• SSD
• YOLO Implementation
• Object detection using cv2
MODULE 5: RECURRENT NEURAL NETWORK
• RNN introduction
• Sequences with RNNs
• Long short-term memory networks(part 1)
• Long short-term memory networks(part 2)
• Bi-directional RNN and LSTM
• Examples of RNN applications
MODULE 6: NATURAL LANGUAGE PROCESSING (NLP)
• Introduction to Natural language processing
• Working with Text file
• Working with pdf file
• Introduction to regex
• Regex part 1
• Regex part 2
• Word Embedding
• RNN model creation
• Transformers and BERT
• Introduction to GPT (Generative Pre-trained Transformer)
• State of art NLP and projects
MODULE 7: PROMPT ENGINEERING
• Introduction to Prompt Engineering
• Understanding the Role of Prompts in AI Systems
• Design Principles for Effective Prompts
• Techniques for Generating and Optimizing Prompts
• Applications of Prompt Engineering in Natural Language Processing
MODULE 8: REINFORCEMENT LEARNING
• Markov decision process
• Fundamental equations in RL
• Model-based method
• Dynamic programming model free methods
MODULE 9: DEEP REINFORCEMENT LEARNING
• Architectures of deep Q learning
• Deep Q learning
• Reinforcement Learning Projects with OpenAI Gym
MODULE 10: Gen AI
• Gan introduction, Core Concepts, and Applications
• Core concepts of GAN
• GAN applications
• Building GAN model with TensorFlow 2.X
• Introduction to GPT (Generative Pre-trained Transformer)
• Building a Question answer bot with the models on Hugging Face
MODULE 11: Gen AI
• Introduction to Autoencoder
• Basic Structure and Components of Autoencoders
• Types of Autoencoders: Vanilla, Denoising, Variational, Sparse, and Convolutional Autoencoders
• Training Autoencoders: Loss Functions, Optimization Techniques
• Applications of Autoencoders: Dimensionality Reduction, Anomaly Detection, Image
AI is the future, and DataMites helped me step into it! The online Data Science course was easy to follow, and the tutors explained everything clearly. Even after completing my course, the team supported me with placements and I successfully cracked a job! Grateful to DataMites for shaping my career.
I joined DataMites to learn Data Science and gained strong skills in ML, DL, and Statistics. Through my internship at Rubixe AI Solutions, I worked on real-world projects, which helped me land job interviews and an offer. The trainers were very supportive and explained every concept clearly. Thanks to DataMites, my data science journey was smooth and successful.
My mentor, a DataMites alumnus, suggested I join the Data Science course, and it was one of my best decisions! The offline classes were interactive and full of guidance, and I got the chance to intern at Rubixe AI Solutions. The placement team was very supportive and helped me perfect my resume. Truly thankful to DataMites for this amazing learning journey.
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I joined DataMites for the Data Science course and learned Python, SQL, Machine Learning, and Deep Learning through hands-on projects. The trainers are excellent at explaining concepts clearly. The placement team is very supportive and provides great opportunities thanks to DataMites for helping me start my data science journey.
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I started my data science journey with DataMites, and it’s been an amazing experience! The Certified Data Scientist Program helped me learn everything from scratch through hands-on projects and an internship. The placement team was incredibly supportive, always ready to clarify my doubts. Thanks to their guidance, I successfully got placed. Highly recommend DataMites to anyone starting their data science career
Enrolling at DataMites was one of the best decisions I made for my career. Coming from a freelance writing background with no prior technical experience, I was initially unsure about stepping into the field of data analytics. But DataMites made the transition smooth and achievable. I’d highly recommend them to anyone looking for a guided, industry-ready data analytics course. A true DataMites success story!
Eligibility for an artificial intelligence course in Johannesburg depends on the course level, with beginner programmes often accepting learners who have completed Grade 12 or an equivalent qualification. Advanced programs may require a relevant degree, programming experience, mathematics, or statistics knowledge.
An online AI course in Johannesburg can suit school leavers, university students, graduates, working professionals, entrepreneurs, and people changing careers. Beginners can start with introductory AI concepts, while learners with technical experience can choose specialized courses in machine learning, automation, or generative AI.
The duration of an online artificial intelligence course in Johannesburg varies according to the depth of study and learning format.
Part-time study may take longer depending on your schedule and the amount of practical work involved.
As a general budgeting guide, short online AI courses may cost around R500 to R5,000, while comprehensive professional programs may range from R8,000 to R40,000 or more. These are indicative estimates, not official or universal fees; the actual cost depends on course duration, curriculum, practical projects, tutor support, and certification.
Completing an AI course can help prepare learners for roles that involve developing intelligent systems, analyzing information, or automating business tasks. Depending on your qualifications and practical experience, career options include AI developer, machine learning engineer, data scientist, AI analyst, automation specialist, and natural language processing developer.
Indeed's Johannesburg salary page reports an average base salary of R914,827 per year for an AI developer, based on five reported salaries; see Indeed's AI developer salary data. This is a small sample rather than a guaranteed market rate, and actual earnings depend on experience, technical skills, industry, employer, and responsibilities.
An online artificial intelligence course in Johannesburg may cover the following skills, depending on the syllabus:
Yes, working professionals can study through part-time, self-paced, evening, or weekend learning formats. An online AI course can help you develop relevant skills alongside your current job, provided you set aside regular time for practice and projects.
No, prior programming knowledge is not essential for a beginner-level artificial intelligence course in Johannesburg. However, learning Python, basic mathematics, and statistics will be useful when progressing to machine learning, model development, and more advanced AI applications.
Artificial intelligence (AI) refers to computer systems designed to perform tasks such as recognizing patterns, understanding language, making predictions, and generating content. These systems use rules, data, and computational models to produce results, which should be checked for accuracy, fairness, and reliability.
Python is widely used for AI because it has extensive libraries for data analysis, machine learning, and deep learning. Other useful languages include R for statistics, Java and C++ for certain software and performance-intensive applications, and SQL for working with structured data.
Yes, people from business, finance, marketing, administration, and other non-technical fields can begin learning AI through introductory courses and practical exercises. Roles involving AI tools, business analysis, workflow automation, and AI implementation may offer entry points, although technical development positions often require programming, mathematics, or relevant qualifications.
AI-related opportunities in Johannesburg may be found across financial services, consulting, technology, telecommunications, retail, and other industries. Potential roles include:
Requirements vary by employer, and some positions require substantial software development or data science experience.
Johannesburg's financial services and technology sectors provide potential applications for AI in fraud detection, customer support, risk assessment, forecasting, and business automation. PwC's May 2026 AI performance research, released in Johannesburg, found that 82% of surveyed organizations were running AI pilots, although relatively few had scaled adoption across their businesses; this suggests opportunities alongside continuing implementation challenges.
AI can be a worthwhile career option for people who enjoy problem-solving, technology, and working with data, particularly as organizations explore automation and intelligent software. The World Economic Forum's Future of Jobs Report 2025 identifies AI and machine learning specialists among the fastest-growing job categories globally, but career prospects in Johannesburg will depend on employers' needs, relevant skills, experience, and available vacancies.
Artificial intelligence is the broad field of creating computer systems that perform tasks associated with human intelligence, while machine learning is a branch of AI in which systems learn patterns from data. Deep learning is a specialized form of machine learning that uses multi-layered neural networks, particularly for tasks such as image recognition, speech processing, and advanced language applications.
Generative AI tools can help employees draft documents, summarize information, write code, answer customer queries, and develop content more efficiently. PwC's May 2026 research found that 64% of surveyed workers across Africa were already using AI, highlighting adoption potential, while accuracy, privacy, security, and human oversight remain important considerations.
Demand for AI skills is supported by growing interest in automation, data-driven decisions, and AI-enabled business services, although the number of vacancies varies by role and experience level. The World Economic Forum's Future of Jobs Report 2025 identifies AI and big data among the fastest-growing skill areas and highlights rising demand for AI and machine learning specialists in South Africa; learners should check current job listings to understand specific employer requirements.
The DataMites Artificial Intelligence course in Johannesburg is designed to help learners develop knowledge of AI concepts, machine learning, and practical applications of artificial intelligence. Our AI training focuses on building relevant technical skills through structured learning and practical exercises. It is suitable for individuals interested in exploring AI and developing their understanding of intelligent technologies.
Learners interested in the AI Engineer course should review the program's entry requirements before enrolling. Eligibility may depend on their educational background and existing technical knowledge, so we recommend contacting the DataMites team to confirm the requirements for the Johannesburg program.
The DataMites Artificial Intelligence course in Johannesburg has a duration of 9 months, with 780+ learning hours. This learning period allows participants to study AI concepts and build their understanding progressively through the program.
The course fee ranges from ZAR 37,500 to ZAR 53,480, depending on the applicable program option. For confirmation of the exact fee, available payment arrangements, and any applicable charges, please contact the DataMites team before registering.
DataMites offers online learning options for learners who want to study artificial intelligence remotely. Please contact our team to confirm the live online format, batch schedule, and availability for the Artificial Intelligence course in Johannesburg.
Learners who meet the applicable requirements can earn IABAC certification after completing the relevant program requirements. NASSCOM FutureSkills Certification may also be available for eligible NRI learners, subject to the applicable certification conditions.
To enroll, visit the DataMites website, open the Artificial Intelligence course page, and contact our team to discuss the available program options. Our team can guide you through registration, confirm the applicable fee, and explain the payment process before you complete your enrollment.
DataMites accepts online debit and credit card payments through Razorpay, and credit card EMI may be available. A token advance is collected during registration, while the remaining course fee must be paid in full before course completion.
The course is intended to help learners develop practical knowledge of artificial intelligence, but internship availability and specific project inclusions should be confirmed with the DataMites team. Please contact us for details about the practical exercises and project components included in your selected program.
Learners without a technical background who are interested in artificial intelligence can explore the program and assess its entry requirements. We recommend discussing your educational background and current skills with the DataMites team to determine whether the course is suitable for your learning needs.
The course focuses on artificial intelligence concepts and related technical skills, with the specific tools and technologies depending on the program curriculum. Contact the DataMites team for the latest syllabus covering AI techniques, machine learning, programming tools, and other technologies included in the course.
You can contact the DataMites team to ask whether a demo session or trial class is available for the Artificial Intelligence course in Johannesburg. Our team can confirm the current options and explain how you can learn more about the program before enrolling.
If you miss a live online session, contact the DataMites team to confirm whether a recording, alternate batch, or another learning option is available. The available arrangements may depend on the course format and batch schedule.
DataMites offers a Flexi Pass option that allows learners to attend multiple batches of the same course during its validity period, helping them revisit topics as needed. The Flexi Pass remains valid for 3 months from the date of activation, subject to the applicable terms.
The course fee depends on the applicable program option and its included services. Before enrolling, contact the DataMites team to confirm what the quoted fee covers, including learning resources, practical activities, and any applicable certification charges.
Refund eligibility, applicable deductions, and cancellation conditions depend on DataMites' current enrollment terms. Please contact the DataMites team before cancelling to obtain the official refund policy applicable to your registration.
The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -
The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.
No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.
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