Day 1: Introduction to Machine Learning
1. Morning Session: Machine Learning Fundamentals
– Welcome
– Introduction to statistical methods and machine learning concepts
– Types of machine learning: supervised, unsupervised, and reinforcement learning
– Real-world applications of machine learning
2. Afternoon Session: Setting Up the Environment
– Introduction to Jupyter Notebooks / Colab
– intro to necessary libraries (e.g., NumPy, pandas, scikit-learn, statsmodels)
– Loading and exploring datasets (Iris)
Day2: Nuts and Bolts of ML
- Morning Session: Fundamentals
- Bias
- Similarity measures
- Model evaluation for classification (e.g., accuracy, precision, recall, F1-score, AUC)
- Basic Concepts
- Overfit and Underfit
- Bias-Variance Trade off
Day 3: Supervised Learning
1. Morning Session: Linear Regression
– Introduction to linear regression
– Implementing linear regression with scikit-learn
– Model evaluation and metrics (e.g., MSE, R-squared)
2. Afternoon Session: Classification
– Introduction to classification
– Implementing logistic regression for binary classification
Day 4: Introduction to Deep Learning
Morning Session: Building Your First Neural Network
- Anatomy of a neural network (layers, weights, biases) Implementing a simple feedforward neural network using a deep learning framework (e.g., TensorFlow or PyTorch)
- Training a neural network with a toy dataset
Afternoon Session: Deep Learning Architectures
- Convolutional Neural Networks (CNNs) Introduction to CNNs and their applications (e.g., image classification)
- Implementing a CNN for image classification
Day 5: Advanced Topics and Next Steps
1. **Morning Session: Deep Learning**
– Introduction to neural networks and deep learning
– Implementing a simple neural network with TensorFlow or PyTorch
2. **Afternoon Session: Model Deployment and Future Learning**
– Deploying machine learning models (e.g., using Flask)
– Discussion of future learning resources and career opportunities in machine learning
Day 6: Hands-On Projects
1. Morning Session: Project Work
– Participants work on small machine learning projects in groups
– Mentors provide guidance and assistance
2. Afternoon Session: Project Presentations
– Each group presents their project
– Peer review and feedback.
