Machine Learning
Dive deep into Machine Learning. Get the Perfect Blend of Analytical Skills & Business Knowledge.
Created by Vijay Gaikwad
30,000
Description
This shortterm course is designed by a professional from the Vishwakarma Institute of Technology, Pune to share the knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. This online, selfpaced Machine Learning course will show stepbystep developments of Machine Learning algorithms that are used to solve realworld problems.
Who can 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

Industry professionals who are interested in online Machine Learning courses

UG/ PG/ PhD Students who are looking for Machine Learning training

Teachers who are interested in Machine Learning Certification
Learning Outcomes/By the end of the course
 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
95 Lessons
 07:06:23
 About Machine Learning 00:03:34
 Performance of Machine Learning Models 00:02:21
 Types of Machine Learning 00:04:31
 Algorithms and Applications of Machine Learning 00:03:14
 Data pre  processing 00:03:02
 Introduction to Simple Linear Regression 00:02:30
 Simple Linear Regression Equation 00:03:27
 Simple Linear Regression how It works ? 00:02:48
 Simple Linear Regression Algorithm 00:02:16
 Simple Linear Regression Program 00:02:49
 Training Simple Linear Regression 00:13:32
 Prediction using Simple Linear Regression 00:04:40
 Data Visualization using Simple Linear Regression 00:08:02
 Introduction to Multiple Linear Regression Model 00:03:25
 Equation of Multiple Linear Regression 00:02:41
 How Multiple Linear Regression Is Useful ? 00:02:50
 Significance of Backward Elimination and 'P Value'. 00:03:49
 Algorithm for Multiple Linear Regression 00:04:57
 Multiple Linear Regression Program 00:04:13
 Importing the data set for Multiple Linear Regression 00:04:13
 Encoding Categorical Data 00:07:11
 Avoiding Dummy Variable Trap 00:02:22
 Splitting the Data set 00:05:35
 Fitting Multiple Linear Regression to Training Set 00:06:46
 Multiple Linear Regression Model Ready 00:13:04
 Polynomial Linear Regression (PLR) 00:03:53
 Comparison: SLR Vs PLR 00:03:13
 Code of Polynomial Regression 00:07:37
 Model of Polynomial Regression 00:02:27
 Object Of Polynomial Regression Class 00:06:16
 Visualizing The Linear Regression Results 00:05:30
 Visualizing The Polynomial Regression Results( For Higher Resolution & Smooth Curve) 00:05:44
 Prediction a new result with linear regression 00:04:18
 Visualizing The Polynomial Regression Results 00:05:27
 Introduction to Classification PartI 00:04:04
 Introduction to Classification PartII 00:02:16
 Logistic Regression (LR)I 00:02:03
 Logistic Regression (LR)II 00:03:50
 Algorithm for Logistic Regression (LR) 00:01:41
 Develop Code of Logistic Regression I 00:00:53
 Code of Logistic Regression II 00:04:20
 Code of Logistic Regression III 00:06:06
 Feature Scaling 00:03:44
 Fitting LR Module To Training Data set 00:05:07
 Making The Confusion Matrix 00:05:13
 Visualizing training set results 00:02:19
 Support Vector Machine Introduction 00:03:24
 Maximum Margin Hyperplane 00:03:24
 Algorithm for Support Vector Machine (SVM) 00:05:26
 Program for Support Vector Machine (SVM) Classifier 00:06:53
 Splitting Data set for Support Vector Machine (SVM) 00:07:53
 Fitting the Support Vector Machine (SVM) Model to Training Set 00:05:59
 Prediction using Support Vector Machine (SVM) 00:03:02
 Visualizing The Support Vector Machine Results 00:05:11
 Examples of Kernels in SVM 00:04:40
 Naive Base Classifier I 00:01:11
 Naive Base Classifier II 00:01:55
 Problem Statement for Naive Base Classifier (NCB) 00:01:56
 Bayes Theorem 00:01:53
 Bayes Theorem Examples 00:01:53
 Probability Calculation Using Bayes Theorem) 00:03:47
 Summery with Examples for Naive Base Classifier (NCB) 00:02:09
 Program for Naive Base Classifier (NCB) 00:09:24
 Fitting Naive Base Classifier (NCB 00:07:04
 NBC divide data set into training set and testing set 00:07:48
 NBC Machine confusion matrix 00:03:56
 NBC visualizing the training set data 00:05:30
 NBC visualizing the test set data 00:03:07
 Introduction to Clustering 00:01:41
 K Means Clustering 00:02:02
 K Means Algorithm 00:04:26
 Examples for K Means 00:02:45
 K Means Clustering Steps 00:04:31
 KMeans algorithm 00:04:15
 KMeans coding import library 00:06:35
 KMeans elbow method 00:09:26
 Fitting KMeans 00:02:57
 Visualizing Clusters 00:08:40
 Introduction to Association Rule Learning (ARL) 00:04:23
 Usefulness of ARL 00:03:44
 Applications of ARL 00:03:27
 Challenges of ARL 00:02:04
 Merits of ARL 00:07:42
 Introduction to Dimentionality Reduction 00:04:41
 Principal Component Analysis (PCA) 00:05:33
 Important Conclusions 00:02:09
 Implementation of PCA  Part 1 00:04:41
 Implementation of PCA  Part 2 00:03:44
 Types of Evaluation 00:06:03
 Model Accuracy & Error Rate 00:03:59
 Kappa Value 00:05:15
 Model Sensitivity and Specificity 00:07:21
 Model Precision and Recall and FMeasure 00:03:52
 ROC Curves 00:04:54
Program Fee
30,000
(Incl.Taxes)
What You Benefit from This Program
Course Brochure
 Vijay Gaikwad
 15 Years Of Experiance
 Vishwakarma Institute of Technology, Pune
Certificate you will Earn after this course
Complete the course successfully to obtain this prestigious recognition from eduplusnow