Machine Learning

Section 1: Introduction to Machine Learning

Duration:1 week

Topics:

• Overview of machine learning and its applications
• Types of ML: Supervised, unsupervised, and reinforcement learning
• Machine learning workflow: Data collection, preprocessing, modeling, evaluation
• Setting up a Python ML environment

Learning Outcomes:

• Understand the scope and types of machine learning
• Set up Python tools for ML development

Activities:

• Case study: Identifying ML use cases in industry
• Tool setup: Install Anaconda, Jupyter Notebook, Scikit-learn, TensorFlow

Resources:

• Anaconda, Jupyter Notebook
• Libraries: NumPy, Pandas, Scikit-learn

Section 2: Data Preparation for Machine Learning

Duration:2 weeks

Topics:

– Week 1:Data Cleaning with Python

• Importing and exploring data with Pandas
• Handling missing values, duplicates, and outliers
• Feature engineering: Encoding categorical variables, scaling numerical features
• Introduction to NumPy for efficient data manipulation

– Week 2:SQL for Data Extraction

• Introduction to relational databases and MySQL
• Writing SELECT queries, filtering with WHERE, and JOINs
• Aggregations: GROUP BY, COUNT, SUM, AVG
• Exporting SQL query results for ML pipelines

Learning Outcomes:

• Prepare clean, structured datasets for modeling
• Query data from databases using SQL

Activities:

• Hands-on: Clean a dataset with Pandas
• Project: Extract and preprocess a customer dataset using SQL and Python

Resources:

• MySQL Workbench, Pandas, NumPy
• Sample datasets (e.g., Kaggle, UCI Repository)

Section 3: Supervised Learning

Duration:3 weeks

Topics:

– Week 1:Regression

• Linear regression: Theory, assumptions, and implementation
• Model evaluation: Mean Squared Error (MSE), R²
• Regularization: Ridge, Lasso, Elastic Net
• Polynomial regression

– Week 2:Classification

• Logistic regression: Binary and multiclass
• Decision trees and random forests
• Support Vector Machines (SVM)
• Evaluation metrics: Accuracy, precision, recall, F1-score, ROC-AUC

– Week 3:Advanced Supervised Learning

• Ensemble methods: Gradient Boosting, XGBoost, LightGBM
• K-Nearest Neighbors (KNN)
• Cross-validation and hyperparameter tuning with GridSearchCV
• Handling imbalanced datasets: SMOTE, class weights

Learning Outcomes:

• Build and evaluate regression and classification models
• Apply ensemble methods and tuning for improved performance

Activities:

• Hands-on: Predict house prices with regression
• Project: Build a churn prediction model with classification

Resources:

• Scikit-learn, XGBoost, LightGBM
• Sample datasets (e.g., Boston Housing, Titanic)

Section 4: Unsupervised Learning

Duration:2 weeks

Topics:

– Week 1:Clustering

• K-means clustering: Theory and implementation
• Hierarchical clustering
• DBSCAN for density-based clustering
• Evaluation: Silhouette score, inertia

– Week 2:Dimensionality Reduction

• Principal Component Analysis (PCA)
• t-SNE for visualization
• Feature selection techniques
• Anomaly detection with isolation forests

Learning Outcomes:

• Apply clustering for data segmentation
• Reduce dimensionality for visualization and efficiency
• Detect anomalies in datasets

Activities:

• Hands-on: Cluster customer data with K-means
• Project: Visualize high-dimensional data with PCA and t-SNE

Resources:

• Scikit-learn
• Sample datasets (e.g., Iris, customer segmentation)

Section 5: Deep Learning

Duration:3 weeks

Topics:

– Week 1:Introduction to Neural Networks

• Basics of neural networks: Neurons, layers, activation functions
• Building feedforward neural networks with TensorFlow/Keras
• Loss functions and optimizers (e.g., SGD, Adam)

– Week 2:Convolutional Neural Networks (CNNs)

• CNN architecture: Convolution, pooling, fully connected layers
• Image classification with CNNs
• Transfer learning with pre-trained models (e.g., VGG16, ResNet)

– Week 3:Recurrent Neural Networks (RNNs)

• RNNs for sequential data: LSTM, GRU
• Time-series prediction and text processing
• Introduction to attention mechanisms and transformers

Learning Outcomes:

• Build and train neural networks for various tasks
• Apply CNNs and RNNs to image and sequential data
• Leverage transfer learning for efficiency

Activities:

• Hands-on: Classify images with a CNN
• Project: Predict stock prices with an LSTM

Resources:

• TensorFlow, Keras
• Sample datasets (e.g., MNIST, CIFAR-10)

Section 6: Model Evaluation and Optimization

Duration:1 weeks

Topics:

• Advanced evaluation metrics: Precision-recall curves, log loss
• Bias-variance tradeoff and overfitting
• Hyperparameter optimization: Random Search, Bayesian optimization
• Model interpretability: SHAP, LIME

Learning Outcomes:

• Evaluate models comprehensively
• Optimize models for performance and interpretability

Activities:

• Hands-on: Tune a random forest model
• Case study: Interpret a model’s predictions with SHAP

Resources:

• Scikit-learn, SHAP, LIME

Section 7: Model Deployment

Duration:1 weeks

Topics:

• Introduction to model deployment
• Building REST APIs with Flask or FastAPI
• Deploying models with Streamlit for interactive apps
• Overview of cloud deployment (e.g., AWS SageMaker, Google Cloud AI)

Learning Outcomes:

• Deploy machine learning models for real-world use
• Create user-friendly interfaces for model predictions

Activities:

• Hands-on: Deploy a classification model with Flask
• Project: Build a Streamlit app for a prediction model

Resources:

• Flask, FastAPI, Streamlit
• Sample models from previous Modules

Section 8: Capstone Project

Duration:2 weeks

Objective:Apply machine learning techniques to solve a complex real-world problem

Project Examples:

• Predict customer lifetime value using regression
• Classify medical images for disease detection
• Cluster users for personalized recommendations

Deliverables:

• Preprocessed dataset using SQL and Python
• Trained and evaluated ML model
• Deployed model or interactive app
• Report summarizing methodology and results

Learning Outcomes:

• Synthesize ML skills to deliver end-to-end solutions
• Communicate technical results effectively

Resources:

• Kaggle datasets, UCI Repository

Section 9: Career Preparation

Duration:1 week

Topics:

• Building a machine learning portfolio
• Resume and LinkedIn optimization
• Preparing for ML interviews (coding, algorithms, case studies)
• Overview of certifications: TensorFlow Developer, AWS Machine Learning

Learning Outcomes:

• Create a professional ML portfolio
• Prepare for machine learning job applications

Activities:

• Build a portfolio with capstone project
• Mock interviews with Python and ML challenges

Course Duration

• Total: 16 weeks (assuming 10-15 hours per week)
• Format: Self-paced with optional instructor-led sessions

Prerequisites

• Basic Python programming
• Familiarity with statistics and linear algebra
• Basic SQL knowledge (or completion of Module 2)

Tools and Software

• Python: Anaconda, Jupyter Notebook (free)
• SQL: MySQL Workbench (free)
• Libraries: NumPy, Pandas, Scikit-learn, TensorFlow, Keras, SHAP, Flask, Streamlit
• Git: Version control (free)

Recommended Resources

• Online platforms: Coursera, edX, Kaggle, Fast.ai
• Books: “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron, “Deep Learning” by Ian Goodfellow et al.
• Datasets: Kaggle, UCI Machine Learning Repository, Google Dataset Search

Certification Preparation

• TensorFlow Developer Certificate
• AWS Certified Machine Learning – Specialty
• Google Professional Machine Learning Engineer