M A C H I N E - L E A R N I N G

Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Because of new computing technologies, machine learning today is to produce reliable, repeatable decisions and results. Most industries working with large amounts of data have recognised the value of machine learning technology to work more efficiently or gain an advantage over competitors.

  • Data Science Toll Kits
  • Data Analytics
  • N L P
  • Deep Learning
  • Data Science
  • Big-Data Analytics
  • Artificial Intelligence
  • Project & Internship
I-SMART @ Machine Learning

I- S M A R T @ Machine Learning Objectives

Machine learning helps to discover patterns in the user data and then make predictions based on these and intricate patterns for answering business questions and solving business problems.

  • To discover patterns in the user data and solving business problems.
  • To help companies to generate profits & predict future from the past data
  • To help enterprises to promote products & make accurate sales forecasts.
  • To develop customized software solutions & meet standards of global clients.
  • To understand learner’s potential and overcome learning challenges.

I- S M A R T @ Machine Learning Overview

Machine Learning will provide a foundational understanding of machine learning models as well as demonstrate these models can solve complex problems in a variety of industries. Machine Learning designed practice exercises that will give you hands-on experience implementing data science models and to implement machine learning algorithms.

Machine earning course provides a broad introduction to machine learning, data-mining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). The course will also draw from numerous case studies and applications, so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.

  • Module 1: Data Science Tool kit
  • Module 2: Statistics & Exploratory Data Analytics
  • Module 3: Machine Learning-1
  • Module 4: Machine Learning-2
  • Module 5: Natural Language Processing
  • Module 6: Deep Learning
  • Module 7: Reinforcement Learning
  • Module 8: Capstone Project

I M R SMART SOLUTION  PVT. LTD. 

YouTube - Training Video