This notebook is made to understand the Machine learning concepts from the basic level.

The Digital Book contains the folder simple Linear Regression which has a notebook Simple Linear Regression.ipynb that describes simple Linear Regression from scratch along with theory and Python code.

The Digital Book contains the folder Logistic Regression which has notebook Logistic Regression or Classification.ipynb which explains Logistic Regression from scratch along with theory and Python code.

The Digital Book contains the folder Linear Regression With Multiple Variables that have notebook Linear Regression With Multiple Variable .ipynb which explains Linear Regression With Multiple Variable from scratch along with theory and Python code.

The Digital Book contains the folder Polynomial Regression that has a notebook Polynomial Regression.ipynb which explains the important and basic concept of the Polynomial Regression along with theory and the python code.

The Digital Book contains the folder Probability Theory 1 which has notebook Probability Theory 1.ipynb which explains basic concepts of the Probability Theory Required For Machine learning along with python code.

The Digital Book contains the folder Probability Theory 2 which has notebook Probability Theory 2.ipynb which is continuation Probability Theory 1 explains basic concepts of the Probability Theory Required For Machine learning along with python code.More emphasis on Gaussian Distribution.

The Book contains the folder Naive Bayes Classification which has notebook Naive Bayes Classification.ipynb where I tried my best to explain the intuition behind the Naive Bayes algorithm and lastly use some data-set(given in reference) taken from Kaggle to implement the algorithm. Even you will get initial glims of Natural language processing (NLP) while going through the text classification module in this chapter.

This book has folder Prerequisites for SVM which has notebook Prerequisites for SVM-Support Vector Machine .ipynb where chapter we will try to clear some fundamental idea how the machine learning problem is evolved by diving deep into mathematics but not that deep,but yes up to the level where we get the intuition.While going through the theory of this we touch at last the VC-dimension and Perceptron learning related to SVM and its limitation. Definitely in the chapter on neural network we will learn more about the Perceptron.

The book has folder The Perceptron which has notebook The Perceptron .ipynb where we move one more step ahead to understand the SVM, this chapter again is Prerequisites for SVM-Support Vector Machine. The PLA i.e Perceptron Learning Algorithm is explained with mathematics and then implemented in python. In this chapter, we are also taking one step to Neural Network.

The book has folder SVM-(Support Vector Machines ) which has notebook The SVM Optimization Problem.ipynb where you will learn what is Support Vector Machines in depth. You will get to know about the concept of the Hard Margin.

The book has folder Soft Margin SVM-and Kernels which has notebook Soft Margin SVM and Kernels.ipynb where we are continuing the SVM. You will get to know about the concept of the Soft Margin & Different Kernels.

The upcoming chapter is:

PCM-Principal Component analysis.

Neural Network and Back-propagation algorithm.