Machine Learning with A

Course Overview

This intensive 3-month course provides a comprehensive introduction to machine learning and artificial intelligence. Students will progress from fundamental concepts to advanced techniques through a combination of theoretical learning and hands-on projects.

Prerequisites

• Basic programming knowledge (preferably Python)
• Foundational mathematics (linear algebra, calculus, probability and statistics)
• Access to a computer with internet connection

Learning Objectives

By the end of this course, students will be able to:

• Understand core ML concepts and algorithms
• Implement and evaluate various ML models
• Process and analyze data for ML applications
• Build deep learning architectures
• Deploy ML models to solve real-world problems
• Understand ethical considerations in AI applications

 

 

Required Tools

• Python 3.x
• Jupyter Notebooks
• NumPy, Pandas, Matplotlib, Scikit-learn
• TensorFlow or PyTorch
• Git and GitHub

 

Month 1: Foundations of Machine Learning

Week 1: Introduction to Machine Learning

Theory:

• What is Machine Learning?
• Types of ML: Supervised, Unsupervised, Reinforcement Learning
• ML workflow and project lifecycle
• Setting up the development environment
• Python refresher for ML
• Introduction to data manipulation with NumPy and Pandas
• Data visualization with Matplotlib and Seaborn

Assignment: Exploratory data analysis on a real-world dataset

 

 

 

 

Week 2: Data Preprocessing and Feature Engineering

Theory:

• Data cleaning and preprocessing
• Feature selection, extraction, and engineering
• Handling missing values, outliers, and imbalanced data
• Data normalization and standardization
• Practical:
• Preprocessing techniques with Scikit-learn
• Building data pipelines
• Cross-validation techniques

Assignment: Prepare a messy dataset for machine learning

 

 

Week 3: Supervised Learning – Regression

Theory:

• Linear Regression
• Polynomial Regression
• Regularization techniques (Ridge, Lasso)
• Evaluation metrics for regression
• Practical:
• Implementing regression models with Scikit-learn
• Model evaluation and hyperparameter tuning

Assignment: Housing price prediction project

Week 4: Supervised Learning – Classification

Theory:

• Logistic Regression
• Decision Trees
• Support Vector Machines
• Evaluation metrics for classification
• Implementing classification models
• Confusion matrix, precision, recall, F1-score
• ROC curves and AUC

Project: Binary and multi-class classification problems

 

Month 2: Advanced Machine Learning

Week 5: Ensemble Methods

Theory:

• Bagging and Boosting
• Random Forests
• Gradient Boosting Machines
• XGBoost, LightGBM

Practical:

• Implementing ensemble methods
• Feature importance analysis

Assignment: Kaggle-style prediction competition

  

 

 

Week 6: Unsupervised Learning

Theory:

• Clustering (K-means, DBSCAN, Hierarchical)
• Dimensionality Reduction (PCA, t-SNE)
• Anomaly Detection
• Practical:
• Implementing clustering algorithms
• Visualizing high-dimensional data

Assignment: Customer segmentation project

Week 7: Introduction to Neural Networks

Theory:

• Perceptrons and Multi-layer Neural Networks
• Activation functions
• Backpropagation and gradient descent
• Introduction to deep learning frameworks
• Building a simple neural network from scratch
• Introduction to TensorFlow/PyTorch

Assignment: Handwritten digit recognition with neural networks

Week 8: Deep Learning Fundamentals

Theory:

• Deep neural network architectures
• Overfitting and regularization in deep learning
• Optimization algorithms (SGD, Adam, etc.)
• Batch normalization, dropout

Practical:

• Building and training deep networks
• Monitoring and improving model performance

Project: Image classification with deep learning

 

 

Month 3: Advanced AI and Applications

Week 9: Convolutional Neural Networks

Theory:

• CNN architecture and principles
• Convolutional layers, pooling, and fully connected layers
• Transfer learning and pre-trained models

Practical:

• Building CNNs with TensorFlow/PyTorch Fine-tuning pre-trained models

Assignment: Object detection and recognition project

 

Week 10: Recurrent Neural Networks and NLP

Theory:

• RNN architecture and principles
• LSTM and GRU units
• Natural Language Processing basics
• Word embeddings and transformers introduction

 

Practical: Text preprocessing for NLP Building RNNs for sequence data

Assignment: Sentiment analysis or text generation project

 

Week 11: Advanced AI Topics

Theory:

• Generative Adversarial Networks (GANs)
• Reinforcement Learning basics
• Attention mechanisms and Transformers
• Large Language Models (LLMs) overview

Practical:

• Experimenting with pre-trained models (BERT, GPT)Simple reinforcement learning implementation

Assignment: Creative application using advanced AI techniques

Week 12: ML Operations and Final Project

Theory:

• Model deployment and serving
• ML system design
• Ethics in AI and responsible ML
• Industry applications and career paths

 

Practical

• Model packaging and API creation
• A/B testing and monitoring

Final Project: End-to-end ML solution for a real world problem

Assessment and Grading:

• Weekly assignments: 40%
• Mid-term project: 20%
• Final project:30%
• Participation and quizzes: 10%

 

Office Hours and Support

• Instructor office hours: [Schedule TBD]
• Teaching Assistant support: [Schedule TBD]
• Online discussion forum for peer learning and support