Programme Curriculum
Duration

45 hours (5 weeks)

Fees

Rs. 5000/-

Learning Outcome

After completion of this course, participants are expected to be able to:

  1. Understand the basis of python programming.
  2. Understand the different types of machine learning concepts in detail
  3. Can understand Data Processing.
  4. To build the model for supervised learning techniques.
  5. Can able to build the model for Unsupervised learning techniques.

Target Learners

1. Most certificate courses require applicants to have a minimum educational qualification, such as a high school diploma or an undergraduate degree. Some courses may also specify a particular field of study or academic background that is preferred.

Course Overview

A complete introduction to Python machine learning will be given to you in this course. You'll learn how to prepare data for features, train your models, evaluate performance, and adjust parameters for better outcomes as you add to your existing Python programming skill set. You'll also learn how to execute unsupervised learning. This course also covers other topics including pre-processing for machine learning, cluster analysis, and tree-based machine learning models. After finishing, you'll be comfortable using Python for machine learning, working with real data sets, and more.


Machine Learning Algorithm using Python

        • Introduction to Data Science with Python
        • Python Basics: Basic Syntax, Data Structures
        • NumPy Package
        • Pandas Package

      • Machine learning model overview
      • Machine learning algorithms
      • Data Preparation
      • Metrics for classification

      • Linear Regression
      • Error in Linear Regression
      • Linear Regression Case Study
      • Polynomial Regression
      • Logistic Regression
      • Naive Bayes

      • Support Vector
      • Decision Tree
      • Random Forest
      • K-nearest neighbour (KNN)

      • K means Clustering
      • DBSCAN – Density based clustering
      • Fuzzy Clustering
      • Hierarchical clustering 

Learners will learn from articles, case studies, PPT, eLearning, videos, practical activities, Assignments, webinars, and live sessions as well as discussions with fellow learners and mentors.

Certificate of Completion will be awarded to those who obtain a minimum score of 50% in all quizzes.