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Big Data & Machine Learning courses

  • Big Data and Machine Learning Overview
    (1 full day / 2 half days)
  • Machine Learning with Python and scikit-learn
    (3 full days / 6 half days)
  • MongoDB (2 full days / 4 half days)
  • Python Essentials for Data Science
    (2 full days / 4 half days)

Who can benefit

All IT professionals, especially those with software development experience.

About the Big Data
& Machine Learning Courses

  • A basket of courses on various technologies related to Big Data and Machine Learning
  • Training delivered by leading industry experts
  • Live online training using Zoom / Microsoft Teams / similar tools
  • Multiple batches (half day as well as full day) for each subject
  • Live, interactive, hands-on sessions with Q&A in real-time with the expert trainer
  • A must-attend series for every ambitious IT professional.

Objectives:
Provide a reasonably good insight about the key concepts, terminology, technology choices with their trade-offs, architectural considerations, and applicability of Big Data and Machine Learning.

    Content: 
  • Big data concepts
  • NoSQL for structured data
  • Hadoop for mainly unstructured data
  • Machine Learning

The contents will be partly technical in nature, but unlike the other courses, there will be no hands-on work in this program. The trainer will demonstrate examples including code snippets and applications.

Who should attend:
Any IT professional who wants an overview of these technologies. Senior professionals who are not currently hands-on in technical aspects of I.T., will also benefit.

Dates & timings:
 Course outline April 29-30 (2 half days), 2:30 pm to 6 pm
May 25 (1 full day), 9:30 am to 5:30 pm
June 11-12 (2 half days), 2:30 pm to 6 pm

Objectives:
Understand the key concepts of Machine Learning, and a wide range of important algorithms such as gradient descent, linear regression, classification, and artificial neural networks with practical examples.

    Content: 
  • Key concepts and terminology
  • Understanding scikit-learn
  • Linear regression, gradient descent, and variations
  • Classification algorithms
  • Clustering
  • Artificial neural networks and deep learning
  • Modeling techniques

Who should attend:
Any IT professional aspiring to get hands-on with ML

Dates & timings:
 Course outline May 4-9 (6 half days), 9:30 am to 1 pm
May 27-29 (3 full days), 9:30 am to 5:30 pm
June 22-27 (6 full days), 2:30 pm to 6 pm

Objectives:
To get a good understanding of the leading NoSQL database solution. It is highly scalable, highly available with no single point of failure, and can store humongous volumes of data reliably and cost-economically.

    Content: 
  • Key concepts and terminology
  • Starting server and client processes
  • CRUD operations
  • Indexes
  • Aggregation framework
  • Schema design
  • Java driver for MongoDB
  • Replication and sharding concepts (no hands-on on this topic)

Who should attend:
Developers, data modelers and architects

Dates & timings:
 Course outline April 20-23 (4 half days), 2:30 pm to 6 pm
May 4-7 (4 half days), 2:30 pm to 6 pm
June 4-5 (2 full days), 9:30 am to 5:30 pm
June 15-18 (4 half days), 9:30 am to 1 pm

Objectives:
Learn to work with NumPy, pandas and Matplotlib with Python using Jupyter Notebook, and get ready to reap the maximum benefit from courses on Machine Learning.

    Content: 
  • Basics of Python including control flow and data structures
  • Lists, lambda, string manipulation
  • NumPy arrays, aggregation, indexing, comparison
  • Pandas objects, datasets, aggregation, pivot tables
  • Visualization using Matplotlib: plots, subplots, axes, text

Who should attend:
Aspiring data scientists, business analysts, application developers

Dates & timings:
 Course outline April 27-30 (4 half days); 9:30 am to 1 pm
May 25-26 (2 full days), 9:30 am to 5:30 pm
June 15-18 (4 half days), 2:30 pm to 6 pm

Other Courses

  • 1. Cassandra

    This course is designed to familiarize you with Cassandra's resilient, distributed architecture while equipping you with a thorough understanding of the Cassandra Query Language (CQL).

  • 2. Making Sense of the IT Buzzwords

    The objective of this seminar is to provide a high-level understanding of many of the IT concepts, terminology and technologies. Interesting cases, videos, and demonstrations (where possible) will be used to make the session lively and enriching.

  • 3. Neo4j

    Neo4j is a leading Graph Database, a popular tool for working with data which is highly connected, and used for a variety of intelligent applications such as recommendation engine, fraud detection, master data management, identity and access management, network and IT operations, and many more. This training program will provide an understanding of graph modeling, and a strong foundation for managing and querying data using Neo4j, including using its Java driver.

  • 4. Hadoop

    Hadoop is an open-source platform and a framework that facilitates using a network of many computers to solve problems involving massive amounts of data and computation.

  • 5. Machine Learning with Python and TensorFlow

    TensorFlow is an open-source software library for machine learning across a range of tasks. The objective is to use as a system for building and training neural networks to detect and decipher patterns and correlations, analogous to human learning and reasoning

Courses calendar

Programs Dates & Timings
Big Data and Machine Learning Overview

April 29-30 (2 half days), 2.30 pm to 6 pm

May 25 (1 full day), 9:30 am to 5:30 pm

June 11-12 (2 half days), 2:30 pm to 6 pm

Python Essentials for Data Science

April 27-30 (4 half days); 9:30 am to 1 pm

May 25-26 (2 full days), 9:30 am to 5:30 pm

June 15-18 (4 half days), 2:30 pm to 6 pm

Machine Learning with Python and scikit-learn

May 4-9 (6 half days), 9:30 am to 1 pm

May 27-29 (3 full days), 9:30 am to 5:30 pm

June 22-27 (6 full days), 2:30 pm to 6 pm

MongoDB

April 20-23 (4 half days), 2:30 pm to 6 pm

May 4-7 (4 half days), 2:30 pm to 6 pm

June 4-5 (2 full days), 9:30 am to 5:30 pm

June 15-18 (4 half days), 9:30 am to 1 pm

Cassandra

May 11-14 (4 half days), 9:30 am to 1 pm

June 22-25 (4 half days), 9:30 am to 1 pm

Neo4J

May 11-14 (4 half days), 2:30 pm to 6 pm

June 29 - July 2 (4 half days), 9:30 am to 1 pm

Machine Learning with Python and TensorFlow

May 18-23 (6 half days), 2:30 pm to 6 pm

June 1-3 (3 full days), 9:30 am to 5:30 pm

June 29 - July 4 (6 half days), 2:30 pm to 6 pm

Hadoop

May 18-23 (6 half days), 9:30 am to 1 pm

June 8-10 (3 full days), 9:30 am to 5:30 pm

Making Sense of the IT Buzzwords (half day)

April 28 (half day), 2:30 pm to 6 pm

May 16 (half day), 2:30 pm to 6 pm

May 30 (half day), 9:30 am to 1 pm

Faculty

Most of these programs will be conducted by Pradyumn Sharma, CEO of Pragati Software. He is a management graduate from the IIM, Ahmedabad, and has 36 years of experience in the IT industry. All programs on Python Essentials for Data Science, and Hadoop, and some instances of other programs will be conducted by other trainers.

How to sign-up for our courses

  • Study the programme details for the various courses offered
  • Choose one or more of the courses and the dates you prefer
  • Fill in the
    enquiry form
  • Our counsellors will call you back to confirm your registration

Course Fees
Please contact us for the fees
of the courses of interest to you.

Payment
We accept all types of credit
& debit cards and bank NEFT.

You may also call us on +91-98202 93357 or chat with us.

About us

  • One of India’s leading IT training companies
  • 30 years' experience in IT training for leading software development companies in India
  • Trained 250,000 IT professionals across about 2000 organizations
  • Training provided across 150 technologies
  • 10 high caliber in-house trainers ably supported by about 400+ consultant trainers
  • 9 state of the art training centres
  • Training programs given 5-star rating by about 85% of the participants, and 4-star by most of the others