Online Machine Learning Course

Dive deep into Machine Learning. Get the Perfect Blend of Analytical Skills & Business Knowledge.

Jointly Delivered by

  • Vijay Gaikwad

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Best Online Course For Machine Learning

This online course, designed by an expert from the Vishwakarma Institute of Technology Pune, provides a complete understanding of Machine Learning. The Certificate in Machine Learning course is designed to share the knowledge and help you learn complex theory, algorithms and coding libraries in a simple way.It will show step-by-step developments of Machine Learning algorithms that are used to solve real-world problems.

Who Should Enroll?

  • Anyone who wants to add value to their business through powerful Machine Learning tools
  • Budding engineers who want to make their career in Machine Learning
  • Teachers who are interested in Machine Learning Certification
  • Industry professionals who are interested in online Machine Learning courses
  • UG/ PG/ PhD Students who are looking for Machine Learning training


  • About Machine Learning
  • Performance of Machine Learning Models
  • Types of Machine Learning
  • Algorithms and Applications of Machine Learning
  • Data Pre-processing
  • Introduction to Simple Linear Regression
  • Simple Linear Regression Equation
  • Simple Linear Regression how It works ?
  • Simple Linear Regression Algorithm
  • Simple Linear Regression Program
  • Reading the SLR dataset
  • Dividing the SLR dataset in DV & IV
  • Preparing the training set and testing set for SLR mode
  • Training the SLR Model
  • Graphical results of SLR model
  • Introduction to Multiple Linear Regression Model
  • Equation of Multiple Linear Regression
  • How Multiple Linear Regression Is Useful ?
  • Significance of Backward Elimination and 'P- Value'
  • Algorithm for Multiple Linear Regression
  • Importing the libraries for MLR model
  • Dividing the data set into training and testing set for MLR model
  • Training the MLR model
  • Building optimal MLR model
  • Polynomial Linear Regression (PLR)
  • Comparison: SLR Vs PLR
  • Reading the libraries and dataset for PLR
  • Fitting the model onto training data set
  • Linear Regression Results on PLR dataset
  • Applying the PLR onto the data set
  • PLR Results
  • Accuracy of PLR
  • Introduction to Classification Part-I
  • Introduction to Classification Part-II
  • Logistic Regression (LR)-I
  • Logistic Regression (LR)-II
  • Algorithm for Logistic Regression (LR)
  • Develop Code of Logistic Regression -I
  • Code of Logistic Regression -II
  • Code of Logistic Regression -III
  • Feature Scaling
  • Fitting LR Module To Training Data set
  • Making The Confusion Matrix
  • Visualizing training set results
  • Support Vector Machine Introduction
  • Maximum Margin Hyper-plane
  • Algorithm for Support Vector Machine (SVM)
  • Program for Support Vector Machine (SVM) Classifier
  • Splitting Data set for Support Vector Machine (SVM)
  • Fitting the Support Vector Machine (SVM) Model to Training Set
  • Prediction using Support Vector Machine (SVM)
  • Visualizing The Support Vector Machine Results
  • Examples of Kernels in SVM
  • Naive Base Classifier- I
  • Naive Base Classifier- II
  • Problem Statement for Naive Base Classifier (NCB)
  • Bayes Theorem
  • Bayes Theorem- Examples
  • Probability Calculation Using Bayes Theorem)
  • Summery with Examples for Naive Base Classifier (NCB)
  • Program for Naive Base Classifier (NCB)
  • NBC divide data set into training set and testing set
  • Fitting Naive Base Classifier (NCB)
  • NBC Machine confusion matrix
  • NBC visualizing the training set data
  • NBC visualizing the test set data
  • Introduction to Clustering
  • K Means Clustering
  • K Means Algorithm
  • Examples for K- Means
  • K- Means Clustering Steps
  • K-Means algorithm
  • K-Means coding import library
  • K-Means elbow method
  • Fitting K-Means
  • Visualizing Clusters
  • Introduction to Association Rule Learning (ARL
  • Usefulness of ARL
  • Applications of ARL
  • Challenges of ARL
  • Merits of ARL
  • Introduction to Dimentionality Reduction
  • Principal Component Analysis (PCA)
  • Important Conclusions
  • Implementation of PCA - Part 1
  • Implementation of PCA - Part 2
  • Types of Evaluation
  • Model Accuracy & Error Rate
  • Kappa Value
  • Model Sensitivity and Specificity
  • Model Precision and Recall and F-Measure
  • ROC Curves
  • Project Ideas

Tools Covered
  • Python

Learning Outcomes

  • Make accurate predictions
  • Use for personal purpose
  • Recognize the Machine Learning model to choose for each problem
  • Understand various powerful Machine Learning models
  • Analyze data effectively
  • Apply dimensionality reduction technique to data
  • Solutions to business problems


Type Weightage %
Content 100%
Online Machine Learning Course

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Course Fees

INR 15,000 (+GST)

"If a group of four or more participants enroll with us, a concession of 10% will be awarded to each one of them. For more details Contact us."    +91 8956167195

Interested in This Program?


Vishwakarma Institute of Technology, 666, Upper Indiranagar, Bibwewadi, Pune, Maharashtra, INDIA - 411 037

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About the Instructor

Vijay Gaikwad

15 Years Of Experience

Vishwakarma Institute of Technology, Pune

Associate Professor & Head- Electronics Engineering Department


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