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Machine Learning & Python Programming for Data Science

“Python is the No.1 programming language in the world”
– Spectrum, IEEE July 2017.

Join Now

In-Class

Course Includes

  • Apriori.
  • Upper Confidence Bound (UCB).
  • Thompson Sampling.
  • Natural Language Processing.
  • Artificial Neural Networks.

Eligibility

Any graduate with prior knowledge of Programming and OOPS

Available Modules

Available in-class and online. Available in weekends and weekdays

Course Duration

80 hours in-class and online

Why should you learn Python?

“Python is the No.1 programming language in the world”
– Spectrum, IEEE July 2017

• The number of data science professionals in US alone would be around 700,000 by 2020
• For aspiring Data Scientists, Python is the most important language to learn because of its rich ecosystem.
• Python is among top 3 languages officially listed and used by Google.
• Python currently is used in more than 75% of all data analysis work across the world.
• Python programmers requirement including Machine Learning knowledge is expected to reach approximately 400,000 by end of 2018

What will you learn?

• The program has been designed to equip you with technology skills that are most desired by IT industry today. The curriculum of this course covers concepts
• Machine Learning With Python
• Introduction to Deep Learning

Module 1: Python for Analytics – 40 Hours
• Installing Python
• Introduction
• The Basics of Python
• Program Flow Control in Python
• Lists,Ranges & Tuples in Python
• The Binary number system
• Python Dictionaries and Sets
• Input and output (I/O) in Python
• Modules and Functions in Python
• Object Oriented Python
• Using Databases in Python
• Generators, Comprehensions and Lambda Expressions
• Packages

Module 2: Machine Learning for Data Science & Analytics
• Introduction of Data Science and Machine Leaning
• Introduction Python
• Data Structure & Data Manipulation in Python
• Statistics for Machine Learning
• Simple Linear Regression
• Multiple Linear Regression
• Polynomial Regression
• Support Vector Regression (SVR)
• Decision Tree Regression
• Random Forest Regression
• Naive Bayes
• Decision Tree Classification
• Random Forest Classification
• K-Means , Hierarchical Clustering

• Complete hands-on and practical oriented.
• Apart from classroom practical, students will get to work on project and prototype to create their own ML algorithm.
• We will help you setup the software on your laptop from the scratch for practice.
• Course will be taught by a single professional trainer with good expertise on the topic. Unlike many places where multiple trainers are involved.
• Processed flow of the program in scheduled and disciplined manner.
• Cutting edge curriculum with introduction to Deep Learning.
• Intensive hands-on with integrated use cases
• All participants will work on building their own ML algorithms based various data.

Join Now

In-Class

Course Includes

  • Apriori.
  • Upper Confidence Bound (UCB).
  • Thompson Sampling.
  • Natural Language Processing.
  • Artificial Neural Networks.

Enquire Now

Enquire Now