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Course Duration:100 hours Live Training + Assignments + Actual Project Based Case Studies
MODULES COVERED IN THIS TRAINING:
Unit 1: Programming in python and r:
Below topics are covered in this Python essentials module for Data Science.
Python & R basics.
Conditional and loops.
String and list objects.
Functions & OOPs concepts.
Exception handling.Database programming.
* Sessions on R are not live, but self-paced.
Unit 2: Data Wrangling
This module will help users to work on messy, incomplete or complex data to make it usable using the below techniques.
Reading CSV, JSON, XML and HTML files using Python.NumPy & pandas for working on large multidimensional arrays and matrices and for Data manipulation and analysis.Scipy libraries to provide mathematical algorithms and convenience functions built on the Numpy extension of Python.Loading, cleaning, transforming, merging, and reshaping data. Data scientists spend 80% of their time on cleaning and manipulating data, and only 20% of their time analyzing it. This project will equip you with all the skills you need to clean your data in Python, from learning how to diagnose your data for problems to dealing with missing values and outliers.
Unit 3: Statistics & Probability
Descriptive statistics Inferential statistics Hypothesis testing Statistical concepts in Python
Unit 4: Machine Learning
Learn Regression, Classification, Clustering, Time Series, Dimensionality reduction and boosting Techniques using below Machine Learning algorithms.Linear and logistics regression Decision trees Support vector machines (SVMs)Random forestsXGBoostK nearest neighbor & hierarchical clustering vPrincipal component analysisText analytics and time series forecasting
Unit 5: Deep Learning
Introduction to deep learning Understanding neural network through Tensor Flow Convolution & recurrent neural networks
Unit 6: Image classification project
Unit 7: Image classification TV script generation
Unit 8: Big Data
Introduction to Big Data & SparkRDD’s in Spark, data frames & Spark SQLSpark streaming, MLib & GraphX
Unit 9: Capstone Projects
The capstone project will enable students to create a usable/public data product using real-world problems that can be used to show your skills to potential employers. Entire Data Science life cycle has to be implemented in the solution for the capstone project.
KUMAR – TRAINER FOR Data Science
– 12 years of experience in Business Operations Management, a Proven track record of developing new business with the latest AI technology.
– Holds a Bachelor’s degree in Mechanical Engineering and a Masters degree in Business Administration from UK
Deployed various AI technologies using Machine learning and deep Learning in Sales and Marketing Domain.
Worked with top MNC’s like Infosys, Bosch etc., as a Manager and Technological Officer.
Also a Marketing Technology Consultant for various AI startups as a freelancer.
Participated and presented papers on AI/ML/DL/NLP in various conferences and Digital
Certified Sales and Marketing Professional from Hubspot.
Association for Artificial Intelligence and Law.
Conducted workshop sessions for L&T technology services, Bangalore.
Working as a AI/DL/Machine Learner Trainer at iMarticus, Systems Domain for the end client such as IBM, TCS, etc, Conducting various sessions for corporates on weekend and weekdays on AI/ML/DL.
Currently a research intern at center for Computational Brain Research CCBR at IIT Madras and PhD candidate in AI at IIT Madras.
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