Complete machine learning course

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

Course Description

We will look first into linear  Regression, where we will learn to predict continuous variables and this will include 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 Naïve bias classifier which will give full details of Bayes Theorem, and implementation of Naïve 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.

A 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, and Agglomerative clustering which will be part of unsupervised learning.

Along all parts 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.


  • Learner should be able to learn below mentioed topics of machine learning with live examples.
  • Basics of machine learning
  • Linear Regression
  • Logistic Regression
  • KNN alogrithm
  • Clustering
  • K-Means Clustering
  • Principal component analysis
  • Data preprocessing
  • EDA
  • The Machine Learning Process
  • Naive Bayes Classifier
  • Confusion Matrix
  • Make Predictions
  • Splitting your data into a Training set and a Test set
  • Classification
  • Decision Tree algorithm
  • The person should have a good understanding with making ML models and he should be able to work as ML Engieer.


  • A computer installed with Jupitor notebook
  • Wireless adapter with Monitor Mode support
  • Minimum of 8GB RAM
  • Internect connection
  • Leaner should aware of basic programming skills of Python
  • The learning should be techincal graduate
Show More


  • Basics of machine learning, data in machine learning
  • Supervised learning, Unsupervised learning , advantages and disadvantages of ML
  • ML life cycle, Exploratory data analysis , ML Challenges and libraries
  • No Feedbacks Posted Yet..!
Complete machine learning course
This Course Includes
  • 6 hours
  • 19 Lectures
  • Completion Certificate Sample Certificate
  • Lifetime Access Yes
  • Language English
  • 30-Days Money Back Guarantee

Sample Certificate

Use your certification to make a career change or to advance in your current career. Salaries are among the highest in the world.

We have 30 Million registered users and counting who have advanced their careers with us.


Sample Certificate

Talk to us