Eligibility

  • Students who wants to learn about it
  • Working Professionals who explorer this new domain

What is this course about?

With the next generation software development, it is the evolution of Machine Learning in Artificial Intelligence to make software more intuitive. With Increasing CPU & GPU capacities, it is time for software to be intuitive to the next level. Thus with such demand & with jobs being replaced with Deep Learning Bots, this course provides you an edge to unleash your intuitive skills to competitive software. To bring the change, to develop & solve challenges with the power of Machine Learning! This course walk-throughs from the very fundamentals to the theoretical to the very commercial aspects of Machine Learning. It provides a broad introduction to machine learning, data analytics, natural language processing & statistical pattern recognition.

FREE COUNSELLING

 
 

What will Course Contain?


  • Core Fundamentals of Machine Learning
  • Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks)
  • Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning)
  • Introduction to Python Packages of Machine Learning & Toolkits

Cloud_Computing

Core Fundamentals of Machine Learning

  • Introduction
  • Data Preprocessing
  • Basic Prediction Model
  • Performance Measures
  • Introduction of Supervised & Unsupervised Learning

Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks)

  • Nearest Neighbors
  • Linear Regression
  • Kernels and Regularisation
  • Support Vector Machines
  • Gaussian Processes
  • Decision Trees
  • Ensemble Learning
  • Sparsity Methods
  • Multi-task Learning
  • Proximal Methods
  • Semi-supervised Learning
  • Neural Networks
  • Matrix Factorization
  • Online Learning
  • Statistical Learning Theory

Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).

  • Clustering
  • Dimensionality Reduction
  • Numerical optimization
  • Principal component analysis and factor analysis
  • Sparse coding and dictionary learning
  • Pattern recognition

Introduction to Python Packages of Machine Learning & Toolkits

The course will focus on the real life cases and applications with the contents from unstructured data to the pseudorandom patterns of detection. Where you will learn to apply the above concepts to build machine learning bots than mere statistical analysis. A healthy combination of theory, analysis, and practical application.

Pre-requisites: Computer programming fundamentals. Understanding of Software Development. Programming skills in Python would be helpful. Experience in Computer software & mathematical & algorithmic alignment. Open to professionals & technical graduates.