Machine Learning Course

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

What you'll 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

Requirements

Learner should be aware of basic python

Description

This course will cover following topics1. Basics of machine learning2. Supervised and unsuperivsed learning3. Linear regression 4. Logistic regression5. KNN Algorithm6. Naive Bayes Classifier7. Principal component analyis8. K-means clustering9. Agglomerative clustering 10. There will pratical excerscise based on Linear regression, Logistic regression,Navie Bayes,K-Means, PCA 11. There will be quiz for each topics and total 200 Questions on machine learning courseWe 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 RegressionWe will look in to Navie bais classifier which will give full details of Bayes Theorem, implemention of Navie bais in machine learning. This can be used in Spam Filtering, Text analysis, •Recommendation Systems.We will look in to KNN alogrithm which will working way of KNN alogrithm, compute KNN distance matrix, Minkowski distance, live examples of implemention 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.

Overview

Section 1: Basics of machine learning

Lecture 1 Basics of machine learning, data in machine learning

Lecture 2 Supervised learning, Unsupervised learning , advantages and disadvantages of ML

Lecture 3 ML life cycle, Exploratory data analysis , ML Challenges and libraries

Section 2: Linear Regression

Lecture 4 Linear and multiple linear regression, cost function, gradient decent method

Lecture 5 practical exercise - car price prediction model using linear regression

Lecture 6 Assumptions, Advantages and disadvantage, best practices, MAE, MAPE,MSE L regres

Section 3: Logistic regression

Lecture 7 Logistic regression

Lecture 8 pratical exerice - Heart disease analysis using logistic regression

Section 4: KNN Algorithm

Lecture 9 KNN Algorithm

Lecture 10 Practical exercise using KNN Algorithm for Tumor classification

Section 5: Naïve Bayes Algorithm

Lecture 11 Naïve Bayes Algorithm

Lecture 12 Practical excerise using Navie Bayes for SPAMs

Section 6: Random forest algorithm

Lecture 13 Random forest alorgthim

Section 7: decision tree algorithm

Lecture 14 decision tree algorithm

Anyone interested in Data Science,Data Science professionals,Machine learning engineer,Learner who want to use Machine Learning to their CV or career toolkit