5-Month Course: Python Fundamentals and Machine Learning

Month 1: Introduction to Python Programming
Week 1: Python Basics
  • Introduction to Python
  • Basic Syntax and Data Types
  • Control Flow and Functions

Project: Simple Python programs to understand basic syntax and control structures

Week 2: Data Structures in Python
  • Lists, Tuples, and Sets
  • Dictionaries and Their Applications
  • Understanding Python Modules

Project: Implementing data structures and basic algorithms in Python

Week 3: Object-Oriented Programming (OOP) in Python
  • Classes and Objects
  • Inheritance and Polymorphism
  • Encapsulation and Abstraction

Project: Building applications using OOP concepts in Python

Week 4: File Handling and Modules
  • Reading and Writing Files
  • Working with CSV and JSON data
  • Creating and Using Python Modules

Project: File manipulation and data processing using Python modules

Month 2: Advanced Python Concepts
Week 1: Advanced Python Techniques
  • Functional Programming in Python
  • Decorators and Generators
  • Error Handling and Debugging

Project: Implementing advanced Python programming techniques

Week 2: Web Scraping and APIs in Python
  • Introduction to Web Scraping
  • Using BeautifulSoup for Parsing HTML
  • Working with RESTful APIs

Project: Building a web scraper or interacting with APIs using Python

Month 3-5: Machine Learning with Python
Week 1: Introduction to Machine Learning
  • Machine Learning Basics
  • Supervised vs Unsupervised Learning
  • Introduction to Scikit-Learn

Project: Implementing basic machine learning algorithms using Scikit-Learn

Week 2: Supervised Learning - Regression
  • Linear Regression
  • Logistic Regression
  • Evaluation Metrics for Regression Models

Project: Build regression models to predict continuous variables

Week 3: Supervised Learning - Classification
  • Decision Trees and Random Forests
  • Support Vector Machines (SVM)
  • Model Selection and Hyperparameter Tuning

Project: Implement classification algorithms for binary and multi-class classification problems

Week 1: Unsupervised Learning - Clustering
  • K-Means Clustering
  • Hierarchical Clustering
  • DBSCAN and Density-Based Clustering

Project: Apply clustering algorithms to group data points based on similarity

Week 2: Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • t-SNE for Non-linear Dimensionality Reduction
  • Applications of Dimensionality Reduction

Project: Perform dimensionality reduction on datasets using PCA and t-SNE

Week 1: Introduction to Deep Learning with TensorFlow
  • Introduction to TensorFlow and Keras
  • Building Neural Networks
  • Image Classification with Convolutional Neural Networks (CNNs)

Project: Develop a CNN model for image classification

Week 2: Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM)
  • Understanding RNNs and LSTM
  • Text Generation with RNNs
  • Time Series Prediction with LSTM

Project: Implement RNNs and LSTM for sequential data analysis

Week 3: Advanced Deep Learning Techniques
  • Generative Adversarial Networks (GANs)
  • Introduction to Reinforcement Learning
  • Deep Reinforcement Learning with OpenAI Gym

Project: Explore advanced deep learning concepts and frameworks