# Machine Learning

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

Jointly Delivered by

- Vijay Gaikwad

Online Course

What's included

Mobile Support

*code* Online Compiler

Discussion Forum

Quiz

Course Certificate

### Description

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

### Industry Trends

##### Job Roles

- Data Analyst
- Data Engineer
- Data Scientist
- Business Analyst
- Machine Learning Engineer

### Syllabus

- About Machine Learning
- Performance of Machine Learning Models
- Types of Machine Learning
- Algorithms and Applications of Machine Learning

- Data Pre-processing

- Installation Details

- 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

### 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

### Assessment

Type | Weightage % |
---|---|

Content | 100% |

### Course Fees

INR 15,000 (+GST)

##### Interested in This Program?

##### Location

edu plus now , 34 A/1 Suyog Centre, 7th Floor, Market Yard Rd, Gultekdi, Pune, Maharashtra 411037

### Why edu plus now

### Industry-Ready Courses

Learn industry-relevant skills that’ll make your resume stand out and ensure you’re ready to tackle the job market.

### Flexible Learning

Access online learning resources anywhere, anytime to gain valuable skills and transform your life in meaningful ways.

### Qualified Instructors

Connect with experts and qualified instructors from reputed universities to stay on top of the ever-evolving future of work.

### Adavanced study plans

Learn complex technical skills with videos, quizzes and assignments to develop your career and build towards a degree.

### Focus on target

Select the best online courses in India that are not only informative and helpful to your long-term career goals but also help close the skill gap in the industry.

### Knowledge Platform

Take advantage of a complete in-built environment for programming and get hands-on experience to solve real-world problems practically.

### About the Instructor

### Vijay Gaikwad

15 Years Of Experience

#### Vishwakarma Institute of Technology, Pune

Associate Professor & Head- Electronics Engineering Department