Machine Learning using Scikit Learn

Application fee : 1000 INR


Location: On-campus,Online
Type: Certificate course
Coordinator: Mr. Ritin Joshi
Language: English
Course fee: 15000 INR
GST: 18%
Total course fee: 17700 INR
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Course Details

“Data is a precious thing and will last longer than the systems themselves.”

– Tim Berners-Lee, inventor of the World Wide Web.


  • basic knowledge about the key algorithms and theory that form the foundation of machine learning and computational intelligence
  • A practical knowledge of machine learning algorithms and methods so that they will be able to explain the principles, advantages, limitations such as over fitting and possible applications of machine learning
  • Identify and apply the appropriate machine learning technique to Regression, Classification, Clustering problems.


Do you have data and wonder what it can tell you?  Do you need a deeper understanding of the core ways in which machine learning can improve your business?  Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems?

Machine learning is the heart of Data Science. Data science is mainly focused on the use of machine learning for solving problems. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including Regression, Classification, and Clustering.

Why with Scikit-learn???

Scikit-learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python. It is licensed under a permissive simplified BSD license and is distributed under many Linux distributions, encouraging academic and commercial use. The library is built upon the SciPy (Scientific Python) that must be installed before you can use scikit-learn.

This stack that includes:

NumPy: Base n-dimensional array package
SciPy: Fundamental library for scientific computing
Matplotlib: Comprehensive 2D/3D plotting
IPython: Enhanced interactive console
Sympy: Symbolic mathematics
Pandas: Data structures and analysis

Extensions or modules for SciPy care conventionally named SciKits. As such, the module provides learning algorithms and is named scikit-learn.


In this course, you will get hands-on experience with machine learning from a series of practical case-studies. Through hands-on practice, you will be able to apply machine learning methods in a wide range of domains. Machine learning is the key skill for the jobs of Data scientist, Data Analysis. After completion of the course, candidate will be eligible for the above mention jobs.


Teaching Methodology consists of theory and practical with interactive discussions on every topic. Presentation and board teaching on smart board make it easy to understand for the candidate. After class, assessment would be in the form MCQs, assignments etc.


Fundamentals of programming, Statistics and Probability, Basic knowledge of Python and libraries like numpy, Pandas, matplotlib.


  1. Introduction
    • Data Science, Big data
    • Predictive Analysis, Machine Learning, Data Mining, Soft computing
    • Neural Network, Deep learning
    • Introduction to Machine learning: Machine Learning Basics, how machines learn, Machine learning in practice, Types of Machine Learning.
  2. Data Preparation
    • Data Collection
    • Outlier Detection and Treatment
    • Missing Value/NA Imputation Techniques
    • Dividing Data into Training, Testing and Validation Sets
  3. Regression: Model Building and Validation of Model
    • Which kind of the problems can be solved using this family of algorithms
    • How to interpret the output.
    • Simple Linear Regression(SLR), Model building with SLR, Validation of Model
    • Multiple Linear Regression(MLR), Model building with MLR, Validation of Model
    • Gradient Descent Algorithm, Linear Discriminant Analysis, Factor Analysis
  4. Classification: Model Building and Validation of Model
    • Which kind of the problems can be solved using this family of algorithms
    • How to interpret the output/ validation matrix
    • Logistic Regression(LR) for Classification, Model building with LR, Validation of Model
    • Decision Trees and Random Forest, Model building with it, Validation of Model
    • K-Nearest Neighbours Classifier, Model building with it, Validation of Model
    • Naïve Bayes Classifier, Model building with it, Validation of Model
    • Support Vector Machine Classifier, Model building with it, Validation of Model
    • How to interpret output or validation matrix
  5. Clustering: Model Building and Validation of Model
    • Which kind of the problems can be solved using this family of algorithms
    • How to interpret the output/ validation matrix
    • Clustering as Machine Learning Task
    • K-means Clustering Algorithm
    • Model building with K- Means and Validation of Models.
  6. Model Selection and Deployment
    • Cross validation
    • Comparing Results of different Algorithm and selecting the best Algorithm.
  7. Time Series Analysis
  8. Recommendation System