Machine-Learning-Specialization ( In Progress...)

• This course is beginner friendly along with some tough challenges for intermidiate engineers in machines. Don't worry if you dont know python! Just give this a try 😄👍.

• This are my machine learning notes that I have created in my learning journey of AI and Machine with the help of some top instructor from Deep Learning AI, This will surely help beginners and intermidiate people to learn and understand machine learning. Feel free to clone, and fork 🍴.

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• B for Beginner, I for intermediate level and A for Advanced

BN for Easy Notebook, IN for Intermidiate, and T for Notebook Assignment to test what you have learned!!

Start with the basics

• B Overview of Machine Learning
• B Applications of Machine Learning
• B What is machine learning

Difference between supervised and unsupervised machine learning

• B Supervised Machine Learning Part 1
• B Supervised Machine Learning Part 2
• B Unsupervised Machine Learning Part 1
• B Unsupervised Machine Learning Part 2

Jupyter Notebooks

• Jupyter notebook is a type of IDE in machine learning which provides to many options to the developers to create and run the code. Check the below file to get started!

Note: You should first clone the project and open it in your IDE or in jupyter notebook to understand it in depth.

Jupyter Notebook

• N JupyterNotebook (Clone and Open it in your IDE)

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Superwised Machine Learning

• Regression Model

  • B Linear Regression Part 1
  • B Linear Regression Part 2
  • BN Linear Regression Model Representation (Open it in your IDE like VS Code or Jupyter Notebook)
  • B Cost Function Formula
  • B Cost Function Intuition
  • B Visualizing the cost function
  • B Visualization Examples
  • BN Cost Function Model Representation (Open it in your IDE and run the whole code by Shift+Enter)

• Training Model with Gradient Descent

  • B Gradient Descent
  • B Implement Gradient Descent
  • B Gradient Descent Intuition
  • B Learning Rate
  • B Gradient Descent with Linear Regression
  • B Running Gradient Descent
  • BN Gradient Descent Representation (Open it in your IDE and run the whole code by Shift+Enter)

• Multiple Linear Regression

  • B Multiple Features
  • B Vectorization Part 1
  • B Vectorization Part 2
  • BN Python, Numphy and Vectorization (Open it in your IDE and run the whole code by Shift+Enter)
  • B Gradient Descent for Multiple Linear Regression
  • BN Multiple Variable Linear Regression (Open it in your IDE and run the whole code by Shift+Enter)

• Gradient Descent in Practice

  • B Feature Scaling Part 1
  • B Feature Scaling Part 2
  • B Checking Gradient Descent for Convergence
  • B Choosing the Learning Rate
  • BN Feature Scaling and Learning Rate (Open it in your IDE and run the whole code by Shift+Enter)
  • B Feature Engineering
  • B Polynomial Regression
  • BN Feature Engineering and Polynomial Regression (Open it in your IDE and run the whole code by Shift+Enter)
  • BN Linear Regression with scikit-learn (Open it in your IDE and run the whole code by Shift+Enter)

• T Linear Regression Test Notebook/Lab (Clone and Open it in your IDE, follow all the instructions given and write the solution code)

• Don't take any pressure of it, hints and solution are given in the notebook as well

• Classification with Logistic Regression

  • B Classification
  • BN Classification (Open it in your IDE and run the whole code by Shift+Enter)
  • B Logistic Regression
  • BN Sigmoid Function and Logistic Regression (Open it in your IDE and run the whole code by Shift+Enter)
  • B Decision Boundary
  • BN Decision Boundary (Open it in your IDE and run the whole code by Shift+Enter)

• Cost Function for Logistic Regression

  • B Cost Function for Logistic Regression
  • BN Logistic Loss
  • B Simplified Cost Function for Logistic Regression
  • IN Cost Function for Logistic Regression

• Gradient Descent for Logistic Regression

  • B Gradient Descent Implementation
  • IN Gradient Descent for Logistic Regression
  • BN Logistic Regression with Scikit-Learn

• The Problem of Overfitting

  • B The Problem of Overfitting
  • B Addressing Overfiting
  • BN Overfitting
  • B Cost Function with Regularization
  • I Regularized Linear Regression
  • I Regularized Logistic Regression
  • IN Regularization

• T Logistic Regression Test

• Don't take any pressure of it, hints and solution are given in the notebook as well

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Advanced Learning Algorithms

• Advanced Learning Algorithms (For => What you will learn in this part)

• Neural Network Intuition

  • B Neurons and the Brain
  • I Demand Prediction
  • B Recognizing Images

• Neural Network Model

  • I Neural Network Layer
  • A More Complex Neural Networks
  • A Inference: Making Predictions
  • IN Neurons and Layers (Open it in your IDE and run the whole code by Shift+Enter)

• TensorFlow Implementation

  • I Inference in Code
  • I Data in TensorFlow
  • I Building a Neural Network
  • AN Coffee Roasting in TensorFlow (Open it in your IDE and run the whole code by Shift+Enter)

• Neural Network Implementation in Python

  • Forward prop in single layer
  • General Implementation of Forward Propogation
  • N Coffee Roasting NumPy (Open it in your IDE and run the whole code by Shift+Enter)

Note: The other files which are under development are commented, you can see the code in side it!

!! Most of the notes credit of this note goes to the great man and teacher Andrew Ng and his great education websites like Deep Learning AI and Coursera