Machine Learning with Python: From Beginner to Advanced course syllabus

 

Module 1: Introduction to Machine Learning

• Week 1: Overview of Machine Learning

  • What is Machine Learning?
  • Types of Machine Learning: Supervised, Unsupervised, Reinforcement
  • Real-world applications of Machine Learning
  • Setting up Python environment: Anaconda, Jupyter Notebooks, essential libraries (NumPy, pandas, matplotlib, scikit-learn)

• Week 2: Python for Data Science

  • Python basics: Data types, control flow, functions
  • NumPy for numerical computing
  • pandas for data manipulation
  • Data visualization with matplotlib and seaborn

Module 2: Supervised Learning

• Week 3: Regression

  • Introduction to regression analysis
  • Simple Linear Regression
  • Multiple Linear Regression
  • Evaluation metrics: Mean Squared Error, R-squared

• Week 4: Classification

  • Introduction to classification
  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Evaluation metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC

• Week 5: Advanced Supervised Learning Algorithms

  • Decision Trees
  • Random Forests
  • Gradient Boosting Machines (XGBoost)
  • Support Vector Machines (SVM)

Module 3: Unsupervised Learning

• Week 6: Clustering

  • Introduction to clustering
  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN

• Week 7: Dimensionality Reduction

  • Introduction to dimensionality reduction
  • Principal Component Analysis (PCA)
  • t-Distributed Stochastic Neighbor Embedding (t-SNE)
  • Singular Value Decomposition (SVD)

Module 4: Reinforcement Learning

• Week 8: Fundamentals of Reinforcement Learning

  • Introduction to Reinforcement Learning
  • Key concepts: Agents, Environments, Rewards
  • Markov Decision Processes (MDP)
  • Q-Learning

• Week 9: Deep Reinforcement Learning

  • Deep Q-Networks (DQN)
  • Policy Gradient Methods
  • Applications of Reinforcement Learning

Module 5: Deep Learning

• Week 10: Introduction to Neural Networks

  • Basics of Neural Networks
  • Activation Functions
  • Training Neural Networks: Forward and Backward Propagation

• Week 11: Convolutional Neural Networks (CNNs)

  • Introduction to CNNs
  • CNN architectures: LeNet, AlexNet, VGG, ResNet
  • Applications in Image Recognition

• Week 12: Recurrent Neural Networks (RNNs)

  • Introduction to RNNs
  • Long Short-Term Memory (LSTM) networks
  • Applications in Sequence Prediction

Module 6: Advanced Topics

• Week 13: Natural Language Processing (NLP)

  • Introduction to NLP
  • Text Preprocessing
  • Sentiment Analysis
  • Topic Modeling

• Week 14: Model Deployment and Production

  • Saving and loading models
  • Introduction to Flask for API creation
  • Deployment on cloud platforms (AWS, Google Cloud, Heroku)

• Week 15: Capstone Project

  • Work on a real-world project
  • End-to-end model development: Data collection, preprocessing, model training, evaluation, and deployment
  • Presentation and review