Friday 14 June 2024

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

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Person climbing a staircase. Learn Data Science from Scratch: online program with 21 courses