Maharishi University Online The best online learning platform

Machine Learning Applications

Machine Learning is the shining star of today’s world. Studying this course will open various opportunities in developing cutting-edge machine learning applications in numerous verticals.

KEY HIGHLIGHTS OF OUR PROGRAM:

Industry-driven comprehensive curriculum

One-on-One Mentoring

Real-world Projects & Case Studies

An awareness program for students & professionals

Personalized Mentorship from 3D Animation Industry Experts

Get LIVE Classes with ample hands-on practice

WHY YOU SHOULD DO AN ONLINE MACHINE LEARNING APPLICATIONS

Give your computer the power of making decisions.
Build a foundation for the most in-demand programming language of the 21st century.
This module covers concepts of the CRISP-DM framework for business problem-solving.
Build a strong network for life with opportunities to connect to Machine Learning Industry experts & your experienced fellow learners.

THE MAHARISHI UNIVERSITY ONLINE ADVANTAGE : WHY YOU SHOULD CHOOSE US

Course
  • Mentorship
  • Networking & Maharishi University Online Alumni Club
  • Career Services
  • Placement Assistance With Maharishi University Online Virtual Job Fair
Course
  • Real Human Support
  • Dedicated Student Success Managers
  • Live Doubt Clearing Sessions
  • Live Interaction With Industry Leaders
Course
  • Learning opportunity from all our programs at the comfort of your home.
  • Flexible to learners needs
  • Live classes by award-winning academicians, activities, discussion forums, communications etc.

CAREER IMPACT: HOW WE HELP YOU BUILD YOUR DREAM CAREER

24X7 Career Mentors
Live Virtual Job Fairs
Holistic Career Services
Profile Building With Real World Projects

WHAT YOU COULD SEE YOURSELF DOING AFTER A MAHARISHI UNIVERSITY ONLINE MACHINE LEARNING APPLICATIONS

ELIGIBILITY CRITERIA

For Students & working professionals who want to upskill and excel.

FOR WHOM

  • Working professionals who want to upskill and excel
  • Also, for Freshers eager to learn Machine learning.

COURSE CURRICULUM

Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed.

• Matrices and Vectors
• Addition and Scalar Multiplication
• Matrix Vector Multiplication
• Matrix Multiplication Properties
• Inverse and Transpose

• Multiple Features
• Gradient Descent for Multiple Variables
• Gradient Descent in Practice - Feature Scaling
• Features and Polynomial Regression
• Normal Equation
• Access to MATLAB Online and the Exercise Files for MATLAB Users
• Installing Octave on Windows
• Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks and Later)
• Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)
• Installing Octave on GNU/Linux7m
• More Octave/MATLAB resources
• Multiple Features
• Gradient Descent For Multiple Variables
• Features and Polynomial Regression 3m
• Normal Equation Non Invertibility

• Basic Operations
• Moving Data Around
• Computing on Data
• Plotting Data
• Control Statements: for, while, if statement
• Vectorization

• Classification
• Hypothesis Representation
• Decision Boundary
• Cost Function
• Simplified Cost Function and Gradient Descent
• Advanced Optimization
• Multiclass Classification: One-vs-all

• The Problem of Overfitting
• Cost Function
• Regularized Linear Regression
• The Problem of Overfitting
• Regularization

• Non-linear Hypotheses
• Neurons and the Brain
• Model Representation
• Examples and Intuitions
• Multiclass Classification

• Cost Function
• Backpropagation Algorithm
• Backpropagation Intuition
• Implementation Note: Unrolling Parameters
• Gradient Checking
• Random Initialization
• Putting It Together
• Autonomous Driving
• Cost Function

• Deciding What to Try Next
• Evaluating a Hypothesis
• Model Selection and Train/Validation/Test Sets
• Diagnosing Bias vs. Variance
• Regularization and Bias/Variance
• Learning Curves
• Deciding What to Do Next Revisited

• Prioritizing What to Work On
• Error Analysis
• Error Metrics for Skewed Classes
• Trading Off Precision and Recall
• Data For Machine Learning

• Optimization Objective
• Large Margin Intuition
• Mathematics Behind Large Margin Classification
• Kernels
• Using An SVM

• Unsupervised Learning: Introduction
• K-Means Algorithm
• Optimization Objective
• Random Initialization
• Choosing the Number of Clusters

• Principal Component Analysis Problem Formulation
• Principal Component Analysis Algorithm
• Reconstruction from Compressed Representation
• Choosing the Number of Principal Components
• Advice for Applying PCA

• Problem Motivation
• Gaussian Distribution
• Algorithm
• Developing and Evaluating an Anomaly Detection System
• Anomaly Detection vs. Supervised Learning
• Choosing What Features to Use
• Multivariate Gaussian Distribution
• Anomaly Detection using the Multivariate Gaussian Distribution

• Problem Formulation
• Content Based Recommendations
• Collabourative Filtering
• Collabourative Filtering Algorithm
• Vectorization: Low Rank Matrix Factorization
• Implementational Detail: Mean Normalization

• Learning With Large Datasets
• Stochastic Gradient Descent
• Mini-Batch Gradient Descent
• Stochastic Gradient Descent Convergence
• Online Learning
• Map Reduce and Data Parallelism

• Problem Description and Pipeline
• Sliding Windows
• Getting Lots of Data and Artificial Data
• Ceiling Analysis: What Part of the Pipeline to Work on Next

FEES:

Frequently Asked Questions

The course is for working professionals who want to upskill and excel. Also, it is for everyone who is eager to learn Machine learning.

Yes, you can attend the course online, from the comfort of your home or office.

Every student and professional who completes the course will be awarded with an International Dual Certification.

You'll need an internet connection or anyone of the following digital devices: smart phone, tablet, desktop or laptop.

You can apply through our online campus and Apply Now.

No prerequisites required.