Machine Learning

Machine learning (ML) is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.

Continued research into deep learning and AI is increasingly focused on developing more general applications. Today's AI models require extensive training in order to produce an algorithm that is highly optimized to perform one task. But some researchers are exploring ways to make models more flexible and able to apply context learned from one task to future, different tasks.This internship provides an overview to understand Machine learning and how it is developing with time. The guide aims at introducing the fundamentals of Machine Learning its practical applications and working. The student will gain knowledge through hands-on session, under the direction of Industry expert Trainers.

Internship Batch Date:

6th Jun 2019 13th Jun 2019 20th Jun 2019
27th Jun 2019 4th Jul 2019 18th Jul 2019

Information:

Registration Amount 12999 /- INR
Internship Type Virtual / Classroom Program

Curriculum

  • What is Machine Learning
  • Install Python and Anaconda.
  • Installing packages: numpy, pandas, matplotlib, sklearn)
  • Introduction to Python
  • Flow Control (If, for, while) Statements
  • Data Structures
    • Numbers
    • Lists
    • Tuples
    • Dictionary
    • Strings
  • Functions and classes in Python
  • Ndarray Object
  • Data Types in Numpy
  • Array Attributes and Manipulation in Numpy
  • Indexing & Slicing
  • Regression
    • Simple Linear Regression
    • Multiple Linear Regression
    • Support Vector Regression
    • Decision Tree Regression
    • Random Forest Regression
  • Classification
    • Support Vector Classification(SVM)
    • K - Nearest Neighbour Algorithm(KNN)
    • Naive Bayes Classification
    • Decision Tree Classification
    • Random forest Classification
  • Clustering
    • K-means Clustering Algorithm
    • Hierarchical clustering