# Machine Learning

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

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• Online Course

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### 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

### Syllabus

###### Introduction to Machine Learning
• Performance of Machine Learning Models
• Types of Machine Learning
• Algorithms and Applications of Machine Learning
###### Data Pre-processing
• Data Pre-processing
###### Installation Steps - Spyder(Python)
• Installation Details
###### Simple Linear Regression
• Introduction to Simple Linear Regression
• Simple Linear Regression Equation
• Simple Linear Regression how It works ?
• Simple Linear Regression Algorithm
• Simple Linear Regression Program
• 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
###### Multiple Linear Regression
• 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 Regression
• 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
###### Logistic Regression
• 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 Classifier
• 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
###### Kernel SVM
• Examples of Kernels in SVM
###### Naive Bayes Classifier
• 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
###### K-Means Clustering
• 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
###### Association Rule Learning
• Introduction to Association Rule Learning (ARL
• Usefulness of ARL
• Applications of ARL
• Challenges of ARL
• Merits of ARL
###### Dimentionality Reduction
• Introduction to Dimentionality Reduction
• Principal Component Analysis (PCA)
• Important Conclusions
• Implementation of PCA - Part 1
• Implementation of PCA - Part 2
###### Model Evaluation
• Types of Evaluation
• Model Accuracy & Error Rate
• Kappa Value
• Model Sensitivity and Specificity
• Model Precision and Recall and F-Measure
• ROC Curves
###### Project Ideas
• 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

### Assessment

Type Weightage %
Content 100% ### Course Fees

INR 15,000 (+GST)

##### Location

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

### Why edu plus now Learn industry-relevant skills that’ll make your resume stand out and ensure you’re ready to tackle the job market. ### Flexible Learning

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

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