Get updates on Eligibility, Admission, Placements Fees Structure
Quick Facts
| Medium Of Instructions | Mode Of Learning | Mode Of Delivery | Frequency Of Classes |
|---|---|---|---|
| English | Self Study, Virtual Classroom | Video and Text Based | Weekends |
Course Overview
The ‘Online Machine Learning Program - Self-Paced’ is provided by the education platform ‘Eduplusnow’ and the course is certified by the Indian Statistical Institute in Pune. The machine learning training program also helps the students to understand the methods involved in the software tools such as Python.
The students gain practical experience by applying the concepts of machine learning in the projects and assignments that are a part of the course curriculum. The course study is about the machine learning algorithms used along with Python and the scope of the machine learning principles in the real world to counter challenges in technologies.
The learners in this ‘Online Machine Learning Program - Self-Paced’ training will be able to build their business and career with the knowledge and certification gained. The course is guided by the professional faculty who provide valuable insights to become experts in machine learning.
The Highlights
- Online mode
- Interactive virtual session
- Partnership with Indian Statistical Institute
- Expert guidance
- Certificate
- Placement support
Programme Offerings
- Course Videos. Lectures
- Recordings
- Projects
- Resource Materials
- Code Templates
- Mentorship
- Sel-Study Assignments
- quizzes
- Case Studies
- Professional faculty
- Interactive Sessions
- job assistance
- Certificate
Courses and Certificate Fees
| Certificate Availability | Certificate Providing Authority |
|---|---|
| yes | edu plus now |
The ‘Online Machine Learning Program - Self-Paced’ certification course is provided by Eduplusnow and the students can access this online program after paying the course fee.
Online Machine Learning Program - Self Paced fee structure
| Head | Amount in INR |
Course Fee | Rs 15000 + GST |
Eligibility Criteria
The ‘Online Machine Learning Program - Self-Paced’ training is for anyone interested in learning about machine learning tools.
Certificate qualifying details
The students will be awarded the ‘Online Machine Learning Program - Self-Paced’ course certificate from Eduplusnow after the completion of the training.
What you will learn
The ‘Online Machine Learning Program - Self-Paced’ syllabus is framed for students to learn about the machine learning algorithms using the tools and techniques of Python. The learners will develop skills to build machine learning models and predict results precisely. This machine learning course enables students to become experts in solving real-world problems in the industry by the implementation of machine learning concepts and ideas. Online Machine Learning Program - Self Paced classes provide insights that aids in resolving business problems, improves the process of data analysis, and helps in understanding the principles of dimensionality reduction.
Who it is for
Online Machine Learning Program - Self Paced benefits any fresher or professional in the engineering field who wishes to begin a career in machine learning and for the business executives who wish to develop their business value with the help of the machine learning tools. This course is for undergraduates, postgraduate students, teachers, and researcher scholars who are interested in exploring the machine learning domain.
Admission Details
The registration for the ‘Online Machine Learning Program - Self Paced’ course is done through the Eduplusnow website as follows,
Step 1: Go to the course page on the official website using the link, https://www.eduplusnow.com/course-details/online-machine-learning-course
Step 2: Now click on the ‘Enroll Now’ found on the page.
Step 3: Enter the relevant information and complete the enrollment.
Application Details
The applicants of the ‘Online Machine Learning Program - Self-Paced’ are required to fill in the enrollment form with their name, phone number, and email address.
The Syllabus
- About Machine Learning
- Performance Of Machine Learning Models
- Types Of Machine Learning
- Algorithms And Applications Of Machine Learning
- 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 Mode.
- 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 Dimensionality 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
Instructors
Indian Statistical Institute, Pune Frequently Asked Questions (FAQ's)
The Eduplusnow in collaboration with the Indian Statistical Institute provides the ‘Online Machine Learning Program - Self Paced’ certification.
The course will take forty hours to complete in total.
The course curriculum consists of 10 projects and 12 assignments for the students to work on.
There are no mandatory prerequisites, the program is open to all interested candidates.
Yes, you will be awarded a course certificate by Eduplusnow after completing the program successfully.