Kigyan School of Data Science

Professional Certificate Course in
Machine Learning



Admission open now

Admission open now

Select Class - Mode

Combo Course
Online / Classroom

Learning Objectives

Kigyan introduces students to integrated blended learning, making them experts in Artificial Intelligence and Data Science. The program in consideration with current industry requirement for Machine Learning and Data Science job roles.

Upon completion of this Certificate Program, you will receive the certificates from Kigyan in the Artificial Intelligence courses on the learning path. These certificates will testify to your skills as an expert in Machine Learning.

This program is designed for the students or professionals who has a minimal or no knowledge of computer programming and want to build their career in the field of Machine Learning.

The program starts from introducing the Business analytics and refreshing your knowledge on mathematical and statistics concepts needed for Analytics and then data management using Microsoft Excel and SQL programming and also covering the basics of Database and data warehousing concepts, introduction to Big data management and basics of python programming.

This program will make you industry-ready with the business domain master data and business process training and to work on 20+ projects

Learning Objectives

The Certification program in Machine Learning course will furnish you with in-depth knowledge of the various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning.

What Skills you will Learn

    This training program will enable you to:

  • Understand Business Analytics, Data science, Machine learning
  • Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing.
  • Install the required Python environment and other auxiliary tools and libraries
  • Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
  • Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
  • Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO and Weave
  • Perform data analysis and manipulation using data structures and tools provided in the Pandas package
  • Gain expertise in machine learning using the Scikit-Learn package
  • Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline
  • Use the Scikit-Learn package for natural language processing
  • Use the matplotlib library of Python for data visualization
  • Extract useful data from websites by performing web scrapping using Python
  • Building machine learning models with 20+ industry demand algorithms
Frequently asked Questions
1. Who should attend this course?

There is a booming demand for skilled data scientists and artificial intelligence developers across all industries that make this course suited for participants at all levels of experience. We recommend this program particularly for the following professionals:

  • Analytics professionals who want to become an Machine Learning Expert
  • Software professionals looking to get into the field of Machine Learning
  • IT professionals interested in pursuing a career in analytics
  • Graduates looking to build a career in analytics, data science and Machine Learning
  • Experienced professionals who would like to harness data science in their fields
  • Anyone with a genuine interest in the field of Machine Learning

Prerequisites: There are no prerequisites for this program. But having knowledge on any one programming language or databases will be an added advantage

The Python basics course included with this program provides additional coding guidance.

2. What type of Jobs we can expect after this training?

Upon completion of this Program, you will have the skills required to help you land your dream job, including:

  • IT Professionals
  • Data Scientists
  • Data Engineers
  • Data Analysts
  • Artificial Intelligence Engineer
  • Machine Learning Engineer
  • Project Managers
  • Program Managers
3. What are the Pre-requisites for this Training?

There are no prerequisites for learning this course. However, knowledge of any one programming language and SQL will be beneficial

4. Who will provide the certification?

Upon successful completion of the Machine Learning certification training, you will be awarded the course completion certificate from Kigyan

5. What are my system requirements?

The tools you will need to attend training are:

  • Windows: Windows XP SP3 or higher
  • Mac: OSX 10.6 or higher
  • Intel i3 with minimum 8 GB RAM
  • Internet speed: Preferably 512 Kbps or higher for online training
  • Headset, speakers, and microphone: you will need headphones or speakers to hear instructions clearly, as well as a microphone to talk to others. You can use a headset with a built-in microphone, or separate speakers and microphone
6. What are the training modes offered for this course?

We offer this training in the following modes:

  • Live Classroom training in our Training Centre
  • Live Virtual Classroom or Online Classroom: Attend the course remotely from your desktop via video conferencing to increase productivity and reduce the time spent away from work or home
7. Any Group Discount offered in this classroom training?

Yes, we have group discount options for our training programs. Contact us using the form on the right of any page on the website or send am mail with your requirement and the student count to Our customer service representatives can provide more details.

8. What payment options are available?

Payments can be made using any of the following options. You will be emailed a receipt after the payment is made.

  • Any Credit or Debit Card
  • Bank Transfer using NEFT / RTGS
  • Direct payment in our centre through cash / cheque
  • Online payment wallet like Google Pay, PayTM….
DataScience With Python
Mathematical Computing with NumPy
  • NumPy Overview
  • Basics of NumPy
  • NumPy fundamental objects
  • Create and Print a NumPy array
  • Basic operators in NumPy
  • Shape manipulation & copy methods
  • Linear algebraic function
  • Writing Programs using NumPy
Scientific Computation with SciPy
  • Introduction to SciPy
  • Characteristics of SciPy
  • Sub Packages of SciPy
    • Optimization
    • Integration
    • Linear Algebra
    • Statistics
    • Weave
    • IO
Data Manipulation with Pandas
  • Introduction to Pandas
  • Data Structures of Pandas
  • Creating Series and Data Frames
  • Accessing elements form data structures
  • Vectorised Operations
  • Handling Missing Values
  • Analysing Data
  • Pandas SQL operations
Machine Learning with Sciket Learn
  • Introduction to Machine Learning
  • The machine Learning approach
  • Introduction to Sciket learn
  • Technologies for understanding a dataset
  • Supervised learning models
  • Unsupervised Learning models
  • Algorithms
    • Regression
    • Classification
    • Clustering
    • Directionality reduction
Natural Language Processing (NLP) with Sciket Learn
  • Introduction to NLP
  • How NLP is helpful
  • Modules to load contents and category
  • Feature extraction techniques
  • Approaches of NLP
Data Visulization with MATPLOTLIB
  • Introduction to Data Visualization & its inference
  • Why Python
  • Python libraries
  • Steps for Plotting
    • Line plot
    • 2D plot
    • Multiple plots
    • Sub plots
  • Types of Plots
  • Seaborn
Web scrapping with Beautiful Soup
  • Introduction to Web scrapping
  • Web Scrapping Process
  • Introduction to Beautiful Soup
    • The Parsers
    • Objects
    • Tree
  • Various Operations on Tree
    • Searching
    • Modifying
    • Navigating
  • Parsing a Part of Document
  • Output- Formatting and Printing
  • Encoding
Python Integration with Hadoop
  • Hadoop Steaming – Python API
  • Mapper in Python
  • Reducer in Python
  • PySpark – Python API for Spark

Machine Learning
Introduction to Data Science
  • What is Data Science?
  • What does Data Science involve?
  • Life cycle of Data Science
  • Tools of Data Science
  • Introduction to Python
Data Extraction, Wrangling & Visualization
  • Data Analysis Pipeline
  • Types of Data
  • Data Wrangling
  • Exploratory Data Analysis
  • Visualization of Data
Introduction to Machine Learning with Python
  • Python Revision (numpy, Pandas, scikit learn, matplotlib)
  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Categories
  • Linear regression
  • Introduction to Neural Network and Deep Learning
Supervised Learning
  • What is Classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Confusion Matrix
  • What is Random Forest?
Unsupervised Learning
  • What is Clustering & its Use Cases?
  • What is K-means Clustering?
  • How K-means algorithm works?
Reinforcement Learning
  • What is Reinforcement Learning
  • Why Reinforcement Learning
  • Elements of Reinforcement Learning
Time Series Analysis
  • What is Time Series Analysis?
  • ARMA model
  • ARIMA model
Model Selection and Boosting
  • What is Model Selection?
  • What is Boosting?
  • How Boosting Algorithms work?

  • Building End to End ML Engine