Complete Machine Learning course

Basics of machine learning,Linear Regression,Logistic Regression, Naïve Bayes ,KNN alogrthim , K-means, PCA, Custering,

This course will cover following topics

1. Basics of machine learning

2. Supervised and unsupervised learning

3. Linear regression

4. Logistic regression

5. KNN Algorithm

6. Naive Bayes Classifier

7.  Random forest  Algorithm

8. Decision Tree Algorithm

7. Principal component analysis

8. K means clustering

9. Agglomerative clustering

10. There will practical exercise based on Linear regression, Logistic regression ,Naive Bayes, KNN algorithm, Random forest, Decision tree, K Means, PCA .

11.  Quiz (MCQ on machine learning course)

We will look first in to linear  Regression, where we will learn to predict continuous variables and this will details of  Simple and Multiple Linear Regression, Ordinary Least Squares, Testing your Model, R Squared and Adjusted R Squared.

We will get  full details of  Logistic Regression, which is by far the most popular model for Classification. We will learn all about Maximum Likelihood, Feature Scaling, The Confusion Matrix, Accuracy Ratios and you will build your very first Logistic Regression

We will look in to Naive bias classifier which will give full details of Bayes Theorem, implementation of Naive bias in machine learning. This can be used in Spam Filtering, Text analysis, Recommendation Systems.

Random forest algorithm can be used in regression and classification problems. This gives good accuracy even if

data is incomplete.

Decision Tree is a Supervised learning technique that can be used for both classification and Regression problems, but mostly it is preferred for solving Classification problems.

We will look in to KNN algorithm which will working way of KNN algorithm, compute KNN distance matrix, Makowski distance, live examples of implementation of KNN in industry.

We will look in to PCA, K means clustering, Agglomerative clustering which will be part of unsupervised learning.

Along all part of machine supervised and unsupervised learning , we will be following data reading , data prerprocessing, EDA, data scaling, preparation of training and testing data along machine learning model selection , implemention and prediction of models.