Live online sessions
Multiple start dates
Q & A
Low introductory prices
Free webinars available
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
or
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+91-98202 93357
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.
Corporate Enquiries
Send us an enquiry
Preeti Sharma,
Executive Director
+91-98202 93357
A. Agile Software Development
Objectives:
In this beginner to intermediate level training program, we will give a better understanding of the set of frameworks and practices based on the values and principles expressed in the Manifesto for Agile Software Development and the 12 Principles behind it.
Content:
Principles behind agile software development
The complete life cycle of Scrum
Working with small iterations
Estimation and planning
Inspect and adapt mechanisms in Scrum
Ways of adopting and adapting Scrum
This four half days workshop provides an in-depth understanding of Scrum, one of the most prominent agile methodologies. With the help of case studies and simulations, it helps them understand and experience the various activities in a software project using Scrum.
Who should attend:
Software Engineer, IT Managers, IT project Managers, Senior level IT professionals.
Course outline
B. Machine Learning using Python and scikit-learn
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.
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
C. MongoDB
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
Course outline
D. Python Essentials for Data Science
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
Course outline
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
Jan 16-17 (2 half days), 2.30 pm to 6 pm
Feb 01 (1 full day), 9:30 am to 5:30 pm
March 01-02 (2 half days), 2:30 pm to 6 pm
Python Essentials for Data Science
Jan 18-21 (4 half days); 9:30 am to 1 pm
Feb 03-04 (2 full days), 9:30 am to 5:30 pm
March 15-18 (4 half days), 2:30 pm to 6 pm
Agile Software Development
Jan 23-26 (4 half days) 2:30 pm to 6:00 pm
Machine Learning with Python and scikit-learn
Jan 23-28 (6 half days), 9:30 am to 1 pm
Feb 06-08 (3 full days), 9:30 am to 5:30 pm
March 20-25 (6 full days), 2:30 pm to 6 pm
MongoDB
Jan 09-12 (4 half days), 2:30 pm to 6 pm
Feb 13-16 (4 half days), 2:30 pm to 6 pm
March 27-28 (2 full days), 9:30 am to 5:30 pm
Cassandra
Jan 16-19 (4 half days), 9:30 am to 1 pm
Feb 20-23 (4 half days), 9:30 am to 1 pm
Neo4J
Jan 23-26 (4 half days), 2:30 pm to 6 pm
Feb 24-28 (4 half days), 9:30 am to 1 pm
Machine Learning with Python and TensorFlow
Jan 06-11 (6 half days), 2:30 pm to 6 pm
Feb 1-3 (3 full days), 9:30 am to 5:30 pm
March 06-11 (6 half days), 2:30 pm to 6 pm
Hadoop
Jan 12 (half day), 2:30 pm to 6 pm
Feb 04 (half day), 2:30 pm to 6 pm
March 13 (half day), 9:30 am to 1 pm
Faculty
Most of these programs will be conducted by Pradyumn Sharma, CEO of Pragati Software. He has 38 years of experience in the IT industry. He is a leading trainer, coach and mentor in the fields of Machine Learning, Big Data, Agile Software Development, Software Architecture, and Writing Good Programs. To know more about him, kindly click here
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.
Pragati Software bank details:
Account Holder: Pragati Software Pvt Ltd
Bank: ICICI Bank
Account No: 104405000277
IFSC Code: ICIC0001044
Account Type: Current account
Branch: Marol
You may also call us on +91-98202 93357 or
About us
One of India’s leading IT training companies
32 years' of experience in IT training for leading software development companies in India
Trained 7,50,000 IT professionals across about 2000 organizations
Training provided across 200 technologies
40 high caliber in-house trainers ably supported by about 400+ consultant trainers
Training programs given 5-star rating by about 85% of the participants, and 4-star by most of the others