Lesson 1: What is AI?

• • Overview and History of AI
  • Types of AI: Narrow AI vs General AI

Lesson 2: Introduction to Machine Learning

• • Definitions and Key Concepts
  • Supervised, Unsupervised, and Reinforcement Learning

Lesson 1: Python Basics

• • Variables, Data Types, and Control Structures
  • Functions and Modules

Lesson 2: Data Manipulation with Pandas

• • DataFrames, Series, and Indexing
  • Data Cleaning and Transformation

Lesson 3: Data Visualisation

• • Plotting with Matplotlib and Seaborn
  • Interactive Visualisations with Plotly

Lesson 1: Descriptive Statistics

• • Measures of Central Tendency and Dispersion
  • Data Distributions

Lesson 2: Probability Theory

• • Basics of Probability
  • Probability Distributions

Lesson 3: Inferential Statistics

• • Hypothesis Testing
  • Confidence Intervals and p-values

Lesson 1: Regression Analysis

• • Linear Regression
  • Multiple Linear Regression

Lesson 2: Classification Algorithms

• • Logistic Regression
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)

Lesson 3: Model Evaluation

• • Metrics: Accuracy, Precision, Recall, F1 Score
  • Cross-Validation

Lesson 1: Clustering Techniques

• • K-means Clustering
  • Hierarchical Clustering

Lesson 2: Dimensionality Reduction

• • Principal Component Analysis (PCA)
  • Linear Discriminant Analysis (LDA)

Lesson 3: Association Rule Learning

• • Apriori Algorithm
  • Market Basket Analysis

Lesson 1: Ensemble Methods

• • Bagging, Boosting, and Stacking
  • Gradient Boosting Machines (GBM) and XGBoost

Lesson 2: Feature Engineering

• • Feature Selection and Extraction
  • Handling Categorical Data and Missing Values

Lesson 3: Model Optimisation

• • Hyperparameter Tuning
  • Grid Search and Random Search

Lesson 1: Neural Networks

• • Basics of Neural Networks
  • Activation Functions and Loss Functions

Lesson 2: Deep Learning Frameworks

• • TensorFlow and Keras
  • Building and Training Neural Networks

Lesson 3: Convolutional Neural Networks (CNNs)

• • CNN Architecture
  • Image Classification with CNNs

Lesson 4: Recurrent Neural Networks (RNNs)

• • RNN Architecture
  • Sequence modelling with RNNs

Lesson 1: Introduction to NLP

• • Text Preprocessing
  • Tokenization and Text normalisation

Lesson 2: NLP with Python

• • Using NLTK and SpaCy
  • Sentiment Analysis and Text Classification

Lesson 3: Advanced NLP Techniques

• • Word Embeddings and Word2Vec
  • Transformers and BERT

Lesson 1: Basics of Reinforcement Learning

• • Key Concepts: Agents, States, Actions, and Rewards
  • Markov Decision Processes (MDPs)

Lesson 2: RL Algorithms

• • Q-Learning and SARSA
  • Deep Q-Networks (DQN)

Lesson 3: Applications of RL

• • Game Playing
  • Robotics

Lesson 1: Model Deployment

• • Saving and Loading Models
  • Deploying Models with Flask and FastAPI

Lesson 2: Scaling ML Solutions

• • Using Cloud Platforms (AWS, Google Cloud, Azure)
  • Containerisation with Docker

Lesson 3: Monitoring and Maintenance

• • Monitoring Model Performance
  • Updating and Maintaining Models

Lesson 1: AI Ethics

• • Bias and Fairness in AI
  • Privacy and Security Considerations

Lesson 2: Responsible AI Development

• • Best Practices for Ethical AI
  • Tools and Frameworks for Responsible AI

Project Planning and Implementation

• • Developing an End-to-End AI/ML Solution
  • Integrating Machine Learning Models
  • Deploying the AI/ML Solution to Production

Course Review and Q&A

• • Final Examination
  • Certification and Career Guidance