Complete Machine Learning course

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

Ratings 4.56 / 5.00

What You Will Learn!

Basics of machine learning
Linear Regression
Logistic Regression
KNN alogrithm
Clustering
K-Means Clustering
Principal component analysis
Data preprocsseing
EDA
The Machine Learning Process
Naive Bayes Classifier
Supervised learning and unsupervised learning
Confusion Matrix
The Elbow Method
Feature Scaling
Feature Scaling
Make Predictions
Splitting your data into a Training set and a Test set
Classification
Machine Learning preparation
Ordinary Least Squares
Accuracy
Decision Tree algorithm
Random forest algorithm

Description

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. There will be quiz for each topics and total 200 Questions 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.