Machine Learning

Machine Learning is the science of getting computers to learn and act like humans do, and improve their learning over time in autonomous fashion, by feeding them data and information in the form of observations and real-world interactions.The first and most important thing we focused on is giving the course a robust structure. Machine Learning is very broad and complex and to navigate this maze you need a clear and global vision of it.Every practical tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means.

Course Fees:- INR 9999

Duration:- 15 Days


Why Choose Us?

  • Accessible Trainers - Enhance your learning Experience by Directly Interacting with Trainers.
  • Flexible Timings - Customize the learning schedule to your needs
  • Linguistic Diversity - Course offered in regional indian languages
  • Repeating the course to improve your skills? - Get 30% Discount!
  • Profile listing at AICRA for Industrial Placement
  • Introductory Scholarship Offer 30% OFFs

Those who are having basic knowledge of programming & mathematics are eligible for this program. And who do not have knowledge of AI but know programming and they wish to learn, can also do this program.

ML developer, junior ML developer, Machine learning Engineer.

As it is a part of Artificial intelligence, the technology is being used to bring down labour costs, reduce product defects, shorten unplanned downtimes, improve transition times, and increase production speed.

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

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