Weeks 1-3: Foundations

• Python Programming Refresher:
  Data structures, functions, object-oriented programming, NumPy, Pandas for data manipulation.
• Mathematics for ML:
  Linear algebra (vectors, matrices, operations), probability and statistics (distributions, hypothesis testing).
• Introduction to Machine Learning:
  Definition, types (supervised, unsupervised, reinforcement), common applications, ML lifecycle.

Weeks 4-6: Supervised Learning

• Regression: Linear Regression, Polynomial Regression, Ridge, Lasso.
• Classification: Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines (SVM).
• Decision Trees and Ensemble Methods: Random Forests, Gradient Boosting.
• Model Evaluation: Metrics (accuracy, precision, recall, F1-score, RMSE, R-squared), cross-validation.

Weeks 7-9: Unsupervised Learning and Feature Engineering

• Clustering: K-Means, Hierarchical Clustering, DBSCAN.
• Dimensionality Reduction: Principal Component Analysis (PCA), t-SNE.
• Feature Engineering: Feature scaling, encoding categorical variables, creating new features.

Weeks 10-12: Deep Learning and Project Implementation

• Introduction to Deep Learning: Neural Networks, activation functions, backpropagation.
• Deep Learning Frameworks: Introduction to TensorFlow or PyTorch.
• Convolutional Neural Networks (CNNs) / Recurrent Neural Networks (RNNs): Basic concepts and applications (depending on project scope).

Project Development:

• Problem definition and data collection.
• Data preprocessing and exploration.
• Model selection and training.
• Model evaluation and hyperparameter tuning.
• Deployment considerations (basic).
• Presentation of findings and project documentation.

Projects:

• Customer Churn Analysis: Analyzing customer data to predict which customers are likely to discontinue using a service, enabling targeted retention strategies.
• E-commerce Sales Forecasting: Utilizing transactional data to analyze sales trends, forecast future performance, and understand seasonality.
• Fraud Detection: Building models to identify fraudulent transactions based on various features within financial datasets.
• Social Media Sentiment Analysis: Analyzing text data from social media to understand public opinion and trends.
• House Price Prediction: Creating a regression model to predict house prices based on factors like location, size, and amenities.
• Image Classification: Building models to categorize images into predefined categories.
• Recommender Systems: Developing systems to suggest relevant items to users based on their preferences or past behavior.