💻

Siddharthan's Complete Machine Learning Course

Jul 12, 2024

Siddharthan's Complete Machine Learning Course

Course Overview

  • Duration: 60 hours long course
  • Parts: 5 parts, each 12 hours long
  • Content: Conceptual topics and hands-on Python coding
  • Platform: Content available on Siddharthan's YouTube channel
  • Projects: Numerous use cases and projects included in each part
  • Resources: GitHub repository with code and Jupyter notebooks
  • Time Stamps: Provided for all individual topics for easier navigation

Part 1: Machine Learning Basics

Modules Overview

  1. Machine Learning Basics

    • Difference between AI, Machine Learning, and Deep Learning
    • Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
    • Applications of Deep Learning

  2. Python Basics for Machine Learning

    • Using Google Collaboratory
    • Python Data Types (List, Dictionary, Tuple, etc.)
    • Loops and Functions in Python

  3. Important Libraries for Machine Learning

    • Numpy: Mathematical operations, Numpy arrays
    • Pandas: DataFrames, Data manipulation
    • Matplotlib & Seaborn: Data visualization

  4. Data Collection and Pre-processing

    • Reliable data sources
    • Handling Missing Values
    • Imbalance Data Handling
    • Train-Test Split, Label Encoding
    • Handling Textual Data

Hands-on Use Cases Covered

  1. Rock vs Mine Prediction

    • Predict whether an object is a rock or mine using sonar data

  2. Diabetes Prediction

    • Predict if a person has diabetes using medical data

  3. Spam Mail Prediction

    • Identify spam emails using textual analysis

Part 2: Advanced Machine Learning Topics (Upcoming)

  • Advanced Machine Learning Models
  • Cross Validation
  • Hyperparameter Tuning
  • More Complex Topics and Projects

Key Concepts Covered in Machine Learning Basics (Detailed)

AI, Machine Learning, and Deep Learning

  • Artificial Intelligence (AI): Building intelligent systems
  • Machine Learning (ML): Subset of AI, systems learn from data
  • Deep Learning (DL): Subset of ML, uses artificial neural networks

Types of Machine Learning

  • Supervised Learning: Labeled data (e.g., Classification, Regression)
  • Unsupervised Learning: Unlabeled data (e.g., Clustering, Association)
  • Reinforcement Learning: Agents learning from actions

Applications of AI and Machine Learning

  • Medical Diagnostics: Disease prediction
  • Autonomous Vehicles: Self-driving cars
  • Natural Language Processing (NLP): Chatbots, Voice Assistants

Python Basics for Machine Learning

  • Google Collaboratory: Setup, features, and usage
  • Data Types and Structures: Integers, Floats, Strings, Lists, Dictionaries, Tuples
  • Control Flow: Loops and Conditionals
  • Functions: Creating and using functions in Python

Important Libraries for Machine Learning

  • Numpy: Mathematical operations, creating Numpy arrays
  • Pandas: Working with DataFrames, Data manipulation
  • Matplotlib & Seaborn: Visualization techniques

Data Collection and Pre-processing

  • Fetching Reliable Data: Using Kaggle, UCI repositories
  • Handling Missing Values: Imputation, Dropping
  • Handling Imbalanced Data: Over-sampling, Under-sampling
  • Train-Test Split: Splitting data for validation
  • Label Encoding: Converting categorical data to numbers
  • Feature Extraction: TF-IDF for text data

Hands-on Projects Summary

  1. Rock vs Mine Prediction

    • Tools: Google Collaboratory, Numpy, Pandas, Scikit-learn
    • Model: Logistic Regression
    • Process: Data cleaning, Splitting, Training, Evaluation

  2. Diabetes Prediction

    • Tools: Google Collaboratory, Numpy, Pandas, Scikit-learn
    • Model: Support Vector Machine (SVM)
    • Process: Data standardization, Splitting, Training, Evaluation

  3. Spam Mail Prediction

    • Tools: Google Collaboratory, Numpy, Pandas, Scikit-learn
    • Model: Logistic Regression
    • Process: Text pre-processing, Feature extraction (TF-IDF), Splitting, Training, Evaluation

Additional Resources

  • GitHub Repository: Includes code and Jupyter notebooks used in the lectures
  • YouTube Channel: All videos available individually
  • Timestamped Topics: Easy navigation to specific areas of interest

Upcoming Topics in Future Parts

  • Advanced and complex techniques in Machine Learning
  • Cross-validation methods
  • Techniques for hyperparameter tuning
  • More detailed and diverse projects

Siddharthan's course is designed to provide comprehensive understanding and practical experience in Machine Learning, combining theoretical knowledge with hands-on coding exercises and diverse real-world projects.

Full transcript