Artificial Intelligence and Machine Learning Course in South Africa

Module 1.1 : Introduction to Python

• Overview of Python: history, features, and use cases.
• Installing Python and setting up the development environment.
• Basic syntax: variables, data types, and simple operations.
• Hands-on: Writing a simple Python program to understand basic concepts.

Module 1.2 :Sequences and File Operations

• Introduction to sequences: lists, tuples, sets, and dictionaries.
• File handling: reading and writing files in Python.
• Intro to Mathematical Concepts: Basic probability—how to generate random numbers and use them to simulate random events.
• Hands-on: A program simulating coin tosses or dice rolls to explore simple probability.

Module 1.3 :Functions and Object-Oriented Programming

• Defining and using functions: arguments, return values, and scope.
• Object-oriented concepts: classes, objects, inheritance, and polymorphism.
• Hands-on: Writing classes to represent basic statistical concepts (e.g., creating a class for a random variable or distributions).

Module 1.4 :Working with Modules and Handling Exceptions

• Importing and using Python modules.
• Exception handling: try, except, and finally blocks.
• Hands-on: Use Python’s built-in statistics module for basic statistical operations (mean, median, variance).

Module 2.1 :Array Manipulation using NumPy

• Introduction to NumPy and its applications.
• Creating and manipulating arrays.
• Mathematical Concepts: Introduction to descriptive statistics—mean, median, mode, variance, and standard deviation using NumPy.
• Probability distributions (e.g., normal distribution) using NumPy’s random module.
• Hands-on: Use NumPy to calculate statistical measures from data arrays, simulate random data based on distributions.

Module 2.2 :Introduction to Pandas

• Basic Pandas operations: importing/exporting data, Series, and DataFrames.
• Cleaning and transforming data.
• Mathematical Concepts: Exploring data distributions (frequency distribution, data dispersion).
• Hands-on: Using Pandas to compute statistics (mean, standard deviation) from real datasets.

Module 2.3 :Advanced Data Manipulation with Pandas

• Merging, joining, and reshaping data.
• GroupBy, pivot tables, and handling time-series data.
• Mathematical Concepts: Correlation and covariance between datasets.
• Hands-on: Use Pandas to explore relationships between datasets using correlation and apply statistics to grouped data.

Module 2.4 :Data Visualization with Matplotlib and Seaborn

• Basic visualization: line plots, bar charts, and scatter plots.
• Mathematical Concepts: Visualizing statistical distributions (histograms, box plots) and relationships between variables (scatter plots).
• Hands-on: Plotting histograms, scatter plots, and box plots to visualize data distributions and compare variables.

Module 2.5 :Advanced Data Visualization

• Creating subplots, multiple axes, and advanced customizations in Matplotlib.
• Statistical visualizations in Seaborn (e.g., pair plots, heatmaps).
• Mathematical Concepts: Visualizing probability distributions, regression analysis, and statistical relationships between variables (pair plots, correlation heatmaps).
• Hands-on: Plotting probability density functions and visualizing relationships between multiple variables using Seaborn.

Module 2.6 : Data Gathering and Web Scraping using Python

• Introduction to web scraping: HTML parsing and scraping static pages with BeautifulSoup.
• Using Selenium for scraping dynamic websites.
• Hands-on: Scraping data from websites and performing simple data analysis/statistical insights from the scraped data.

Module 3.1: Foundations of Machine Learning 1.Introduction to Machine Learning

• Definition, applications, and key differences from traditional programming.
• Types of ML: Supervised, unsupervised, semi-supervised, reinforcement learning.

2.Mathematics for Machine Learning

• Linear Algebra: Vectors, matrices, eigenvalues, and SVD.
• Calculus: Gradients, chain rule, optimization techniques.
• Probability and Statistics: Distributions, Bayes' theorem, hypothesis testing.

3.Introduction to ML Tools

• Tools and libraries: Scikit-learn, TensorFlow, and Pandas.

Module 3.2: Supervised Learning 1.Regression

• Linear Regression: Implementation with Scikit-learn.
• Regularization: Lasso and Ridge regression.
• Evaluation Metrics: R-squared, MSE, MAE.
• Hands-on: Predict house prices.

2.Classification

• Logistic Regression: Implementation and interpretation.
• Evaluation Metrics: Confusion matrix, precision, recall, F1-score, ROC curve.
• Hands-on: Breast cancer classification dataset.

3.Feature Engineering

• Handling missing data, feature scaling, encoding, and transformations.

1.Ensemble Methods

• Bagging and boosting (Random Forest, Gradient Boosting, XGBoost).
• Hands-on: Build and evaluate ensemble models.

2.Hyperparameter Tuning

• Techniques: Grid search, random search, and Bayesian optimization.
• Best practices: Cross-validation and early stopping.
• Hands-on: Tuning hyperparameters for an ML model.

3.Unsupervised Learning

• Clustering: K-Means, DBSCAN, Gaussian Mixture Models (GMM).
• Dimensionality Reduction: PCA, t-SNE, and UMAP.
• Hands-on: Customer segmentation and visualization with dimensionality reduction.

1.Time Series Analysis

• Components of time series data: Trend, seasonality, and noise.
• Forecasting techniques: ARIMA, exponential smoothing.
• Hands-on: Forecast sales or stock prices.

2.Big Data Tools

• Introduction to distributed ML with Spark MLlib.
• Working with large datasets using PySpark.

3.Model Scalability

• Deployment basics: Using Flask and FastAPI for REST APIs.
• Deployment to cloud platforms like AWS, GCP, or Azure.
• Hands-on: Deploy a machine learning model as an API.

Module 6.1: Deep Learning and NLP 1.Deep Learning Fundamentals

• Neural Networks: Architecture, activation functions, backpropagation.
• Building basic TensorFlow models.

2.Recurrent Neural Networks (RNNs)

• Topic: Understanding the architecture and applications of RNNs.
• Hands-On: Building an RNN for a text-generation task.

3.Convolutional Neural Networks (CNNs)

• Topic: Understanding CNN architecture and its application to NLP tasks.
• Hands-On: Implementing a CNN for text classification.

4.Advanced Neural Network Architectures

• Transformer models: BERT, GPT for NLP tasks.
• Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs).
• Hands-on: Text classification with BERT; image generation with GANs.

5.NLP Applications

• Text Pre-processing: Tokenization, stemming, lemmatization, TF-IDF.
• Hands-on: Text classification and sentiment analysis.

Module 6.2: Ethics and Real-World Applications 1.Ethics in AI

• Fairness, bias, and transparency in machine learning.
• Societal implications: Privacy, job displacement, and decision-making.
• Tools for ethical AI: Explainability frameworks like LIME and SHAP.

2.Real-World Applications

• Domain-specific case studies: Healthcare, finance, marketing.
• Solving problems with real-world datasets: Handling imbalanced and messy data.

3.Capstone Projects

• End-to-end ML project involving data preprocessing, model building, hyperparameter tuning, and deployment.
• Kaggle-style competition to test skills in a collaborative setting.

Module 7.1 : Data Preparation using Tableau Prep

• Overview of Tableau Prep for data cleaning and transformation.
• Importing, filtering, and shaping data for analysis.
• Data profiling and preparing datasets for analysis.

Module 7.2 : Data Connection with Tableau Desktop

• Connecting to various data sources (spreadsheets, databases, cloud).
• Understanding live connections vs extracts.
• Managing data joins, unions, and blends.

Module 7.3 : Basic Visual Analytics

• Building foundational visualizations like bar charts, line charts, and scatter plots.
• Sorting, filtering, and grouping data.
• Working with visual marks (size, color, labels) for enhanced data presentation.

Module 7.4 : Calculations in Tableau

• Creating row-level and aggregate calculations.
• Using string functions, logical functions, and conditional calculations.
• Practical applications of calculations for deriving insights.

Module 8.1 : Advanced Visual Analytics

• Incorporating reference lines, trend lines, and forecasts.
• Using parameters to create dynamic visualizations.
• Highlight actions, sets, and advanced tooltips for interactivity.

Module 8.2 : Level of Detail (LOD) Expressions in Tableau

• Understanding Fixed, Include, and Exclude LOD calculations.
• Practical scenarios for using LOD expressions in reporting.
• Combining LOD expressions with other calculations.

Module 8.3 : Advanced Charts in Tableau

• Creating dual-axis charts, waterfall charts, and bullet graphs.
• Understanding when and how to use advanced charts for storytelling.
• Hands-on practice with custom chart creation.

Module 8.4 :Dashboards and Stories

• Building interactive dashboards from multiple sheets.
• Using dashboard actions to filter and highlight data dynamically.
• Creating stories to present data insights in a narrative format.