Certificate Course on
Advanced Machine Learning



Admission open now

Admission open now

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Short - Term
Online / Classroom

Learning Objectives
Machine Learning

Machine Learning course will make you an expert in machine learning, a form of artificial intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You'll master machine learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modelling to develop algorithms and prepare you for the role of a Machine Learning Engineer.

  • Machine learning is taking over the world, and with that, there is a growing need among companies for professionals to know the ins and outs of machine learning
  • The machine learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period
Machine Learning

This Machine Learning online course will provide you with insights into the vital roles played by machine learning engineers and data scientists. Upon completion of this course, you will be able to uncover the hidden value in data using Python programming for futuristic inference. You will work with real-time data across multiple domains including e-commerce, automotive, social media and more. You will learn how to develop machine learning algorithms using concepts of regression, classification, time series modelling and much more.

What Skills you will Learn

    This Machine Learning Certification course will enable you to:

  • Master the concepts of supervised and unsupervised learning, recommendation engine, and time series modelling
  • Gain practical mastery over principles, algorithms, and applications of machine learning through a hands-on approach that includes working on four major end-to-end projects and 25+ hands-on exercises
  • Acquire thorough knowledge of the statistical and heuristic aspects of machine learning
  • Implement models such as support vector machines, kernel SVM, naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-means clustering and more in Python
  • Validate machine learning models and decode various accuracy metrics. Improve the final models using another set of optimization algorithms, which include Boosting & Bagging techniques
  • Comprehend theoretical concepts and how they relate to the practical aspects of machine learning
Frequently asked Questions
1. Who should attend this course?

There is an increasing demand for skilled machine learning engineers across all industries, making this Machine Learning certification course well-suited for participants at the intermediate level of experience. We recommend this Machine Learning training course for the following professionals in particular

  • Developers aspiring to be data scientists or machine learning engineers
  • Analytics managers who are leading a team of analysts
  • Business analysts who want to understand data science techniques
  • Information architects who want to gain expertise in machine learning algorithms
  • Analytics professionals who want to work in machine learning or artificial intelligence
  • Graduates looking to build a career in data science and machine learning
  • Experienced professionals who would like to harness machine learning in their fields to get more insights

Prerequisites: Participants in this Machine Learning certification course should have:

  • Familiarity with the fundamentals of Python programming
  • Fair understanding of the basics of statistics and mathematics.

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

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

  • IT professionals
  • Data scientists
  • Data engineers
  • Data analysts
  • Project managers
  • Program managers
3. What are the Pre-requisites for this Training?
Participants in this Machine Learning certification course should have:
  • Familiarity with the fundamentals of Python programming
  • Fair understanding of the basics of statistics and mathematics.
4. Who will provide the certification?

Upon successful completion of the Data Science with Python 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….
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?
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  • I'm very happy to be the student of this institution, and the guide we got is very experienced and they trying to teach us the things from ground level.

  • More complex things are made to solve in an easier and understandable ways by Kiran Sir...Iam extremely happy to be the part of the internship...Thank you!

  • Internship was really good and we learnt many new things of current technology.