AI & Machine Learning

Build a solid foundation in Python before diving into AI and ML libraries.

• Introduction to Python
• Installing Python
• Installing Anaconda
• Jupyter Notebook
• Google Colab
• Python Variables
• Python Data Types
• Python Operators
• Input and Output
• Conditional Statements
• Loops
• Functions
• Lambda Functions
• Lists
• Tuples
• Sets
• Dictionaries
• Strings
• List Comprehension
• Exception Handling
• Python Modules
• Python Packages

Learn NumPy and Pandas - the core libraries for data manipulation.

• Introduction to NumPy
• NumPy Arrays
• Array Indexing and Slicing
• Array Operations
• NumPy Mathematical Functions
• Introduction to Pandas
• Pandas Series
• Pandas DataFrames
• Reading CSV Files
• Reading Excel Files
• Data Selection
• Filtering Data
• Sorting Data
• Handling Missing Values
• Grouping Data
• Merging DataFrames

Communicate insights effectively with charts and plots.

• Introduction to Matplotlib
• Line Charts
• Bar Charts
• Pie Charts
• Histograms
• Scatter Plots
• Customizing Charts
• Introduction to Seaborn
• Statistical Data Visualization
• Distribution Plots
• Box Plots
• Heatmaps
• Pair Plots

Understand what artificial intelligence is and how it is organised.

• What is Artificial Intelligence?
• History of Artificial Intelligence
• Types of Artificial Intelligence
• Narrow AI
• General AI
• Super AI
• Applications of Artificial Intelligence
• AI vs Machine Learning
• AI vs Deep Learning
• Introduction to Intelligent Systems
• AI Models
• AI Agents
• Types of AI Agents
• AI Ethics
• Limitations of Artificial Intelligence

Learn how machines learn from data and make predictions.

• What is Machine Learning?
• How Machine Learning Works
• Types of Machine Learning
• Supervised Learning
• Unsupervised Learning
• Reinforcement Learning
• Machine Learning Workflow
• Training Data
• Testing Data
• Features and Labels
• Training a Model
• Making Predictions
• Model Evaluation

Clean and prepare raw data so models can learn from it effectively.

• Introduction to Data Preprocessing
• Understanding Datasets
• Data Collection
• Data Cleaning
• Handling Missing Values
• Handling Duplicate Data
• Handling Outliers
• Data Transformation
• Data Encoding
• Label Encoding
• One-Hot Encoding
• Feature Scaling
• Normalization
• Standardization
• Feature Selection
• Train-Test Split

Train models on labelled data using regression and classification.

• Introduction to Supervised Learning
• Regression
• Classification
• Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Logistic Regression
• K-Nearest Neighbors (KNN)
• Decision Tree
• Random Forest
• Support Vector Machine (SVM)
• Naive Bayes

Predict continuous values and evaluate regression performance.

• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Regression Predictions
• Regression Evaluation
• Mean Absolute Error
• Mean Squared Error
• Root Mean Squared Error
• R-Squared Score

Classify data into categories using popular classifiers.

• Introduction to Classification
• Binary Classification
• Multiclass Classification
• Logistic Regression
• K-Nearest Neighbors
• Decision Tree Classifier
• Random Forest Classifier
• Support Vector Machine
• Naive Bayes Classifier

Measure how well your models perform and avoid common pitfalls.

• Introduction to Model Evaluation
• Accuracy Score
• Precision
• Recall
• F1 Score
• Confusion Matrix
• Classification Report
• Cross Validation
• Overfitting
• Underfitting
• Bias and Variance

Find structure in unlabelled data using clustering and reduction.

• Introduction to Unsupervised Learning
• Clustering
• K-Means Clustering
• Hierarchical Clustering
• DBSCAN Clustering
• Cluster Evaluation
• Dimensionality Reduction
• Principal Component Analysis (PCA)

Learn how agents act in environments to maximise reward.

• Introduction to Reinforcement Learning
• Agent
• Environment
• State
• Action
• Reward
• Policy
• Exploration
• Exploitation
• Q-Learning
• Applications of Reinforcement Learning

Understand the neural network foundations of modern AI.

• What is Deep Learning?
• Machine Learning vs Deep Learning
• Neural Networks
• Artificial Neurons
• Input Layer
• Hidden Layers
• Output Layer
• Weights and Bias
• Activation Functions
• Forward Propagation
• Loss Function
• Backpropagation
• Gradient Descent
• Epochs
• Batch Size
• Learning Rate

Build, train and evaluate neural networks with TensorFlow and Keras.

• Introduction to TensorFlow
• Installing TensorFlow
• Introduction to Keras
• Creating Neural Networks
• Sequential Model
• Dense Layers
• Compiling a Model
• Training a Model
• Evaluating a Model
• Making Predictions
• Saving and Loading Models

Process images and build vision models using OpenCV.

• Introduction to Computer Vision
• Digital Images
• Image Data
• Image Preprocessing
• Introduction to OpenCV
• Reading Images
• Displaying Images
• Resizing Images
• Image Cropping
• Image Rotation
• Image Filtering
• Edge Detection
• Image Classification
• Object Detection
• Face Detection

Use CNNs to classify and understand images.

• Introduction to CNN
• Convolution Layer
• Filters and Kernels
• Feature Maps
• Pooling Layer
• Max Pooling
• Flatten Layer
• Fully Connected Layer
• Image Classification Using CNN
• Training a CNN Model
• Evaluating a CNN Model

Work with text data and build language models.

• Introduction to NLP
• Text Data
• Text Preprocessing
• Tokenization
• Stop Words
• Stemming
• Lemmatization
• Bag of Words
• TF-IDF
• Text Classification
• Sentiment Analysis

Understand how modern generative models and LLMs work.

• Introduction to Generative AI
• Generative AI Models
• Large Language Models
• Prompt Engineering Basics
• Tokens
• Embeddings
• Transformers
• AI Chatbots
• Generative AI Applications
• Limitations of Generative AI

Turn a trained model into a working application that serves predictions.

• Introduction to Model Deployment
• Saving Machine Learning Models
• Pickle
• Joblib
• Creating a Simple ML Application
• Introduction to Flask
• Building an ML Web Application
• Introduction to Streamlit
• Deploying an ML Model

Apply everything you have learned to realistic ML projects.

• Student Performance Prediction
• House Price Prediction
• Loan Approval Prediction
• Spam Email Detection
• Customer Churn Prediction
• Sales Prediction
• Movie Recommendation System
• Sentiment Analysis Project
• Image Classification Project

Deliver a complete end-to-end machine learning project.

• Problem Definition
• Data Collection
• Data Cleaning
• Exploratory Data Analysis
• Feature Engineering
• Model Selection
• Model Training
• Model Evaluation
• Model Improvement
• Model Saving
• Application Development
• Project Documentation
• Project Presentation