Data Science, Machine Learning and Deep Learning with Python

Learn how to use Numpy, Pandas, MatplotLib, Seaborn, Scikit-Learn, Tensorflow and more!

  • (5.0)

Course Overview

Complete Data Science, Artificial Intelligence, Machine Learning, Deep Learning and Business Analytics Training

Learn how to build an AI

Learn how to create Machine Learning algorithms in Python.

What are the requirements?

  • Basic knowledge of Maths & Statistics (High School level)

What am I going to get from this course?

  • To Build AI and ML Models in Practice
  • To Solve Complex problems.
  • To Make Accurate Decision & Predictions on Real-World Business Problems

What is the target audience?

  • Anyone in the world interested in Outstanding Opportunities and Growth in Artificial Intelligence, Machine Learning & Deep Learning Easily and Quickly
  • Students who want to learn how to boost their career in Data Science
  • Professionals who want to know it all on how to become a Data Scientist

About the Author

• Data Scientist, Professor, Speaker, Author, Corporate Trainer & Mentor with over 20 years of hardcore industry experience on IT & Management

• Executed Mission Critical IT Solutions in the United States, Europe, and the Asia Pacific for Govt. of India, Corporate & Multinational Organisations

• Master Corporate Trainer, Coach & Consultant with proven expertise in Data Science, Artificial Intelligence, Machine Learning, Deep Learning & Business Analytics at Engineering & Medical Colleges, Universities, Govt. of India, and MNC’s.

• Special invitee, Visiting faculty and Keynote speaker at 50+ premier National and International Technical institutes and Universities in India, US, and UK like IITs, IIMs, University of Texas, University of Cambridge and many more

• Mentoring Top Executives of 100+ Corporates including TCS, Hitachi, Broadcom, Wells Fargo, Deloitte, Cognizant, Fidelity, Vodafone, Accenture, JP Morgan, Philips, Mahindra, HCL and many more

• Certified Data Scientist and Cyber Security Expert from Govt. of India

• Trained more than 50,000+ Professionals & Students


Course Curriculum

Python and Statistics for Data Science
4 Document Lectures | 10 Video Lectures | 03:27:46

  • Python Introduction
    25:51
     
  • Python Practical Task
    4 Page
  • Numerical Python
    30:18
     
  • Numerical Python Practical Session
    13:56
     
  • Numerical Python Practical Task
    3 Page
  • Pandas Data Analysis Part 1
    23:19
     
  • Pandas Data Analysis Part 2
    19:43
     
  • Pandas Data Analysis Practical Task
    7 Page
  • Matplotlib Data Visualization Part 1
    21:50
     
  • Matplotlib Data Visualization Part 2
    11:22
     
  • Matplotlib Data Visualization Practical Session
    12:29
     
  • Matplotlib Data Visualization Practical Task
    3 Page
  • Statistics for Data Science Part 1
    25:57
     
  • Statistics for Data Science Part 2
    23:01
     

Beginner’s - Artificial Intelligence, Machine Learning
4 Document Lectures | 10 Video Lectures | 03:03:14

  • Business Analytics
    29:43
     
  • Introduction to Machine Learning
    08:31
     
  • How to Work in the Cloud Practical Session
    14:11
     
  • Machine Learning Techniques Part 1
    30:46
     
  • Machine Learning Techniques Part 2
    27:32
     
  • How to Clean Data Practical Session
    05:52
     
  • Sports Analytics Project Session
    10:31
     
  • Sports Analytics Project Task
    2 Page
  • HR Salary Analytics Project Session
    08:30
     
  • HR Salary Analytics Project Task
    2 Page
  • Survival Analytics Project Session
    30:00
     
  • Survival Analytics Project Task
    4 Page
  • Health Care Analytics Project Session
    17:38
     
  • Health Care Analytics Project Task
    4 Page

Advanced - Artificial Intelligence and Machine Learning
8 Document Lectures | 16 Video Lectures | 04:28:16

  • Decision Tree and Random Forest Algorithm
    31:30
     
  • Plant Analytics Project Session Part 1
    11:15
     
  • Plant Analytics Project Task Part 1
    2 Page
  • Naïve Bayes and KNN Algorithm
    32:22
     
  • Support Vector Machine Algorithm
    19:11
     
  • Plant Analytics Project Session Part 2
    12:02
     
  • Plant Analytics Project Task Part 2
    2 Page
  • How to Calculate Feature Importance Practical Session
    15:35
     
  • Ensemble Learning Techniques
    19:42
     
  • Ensemble Learning Project Session
    15:05
     
  • Ensemble Learning Project Task
    4 Page
  • Data Mining
    17:59
     
  • Web Scraping using Python Beautiful Soup
    17:12
     
  • Web Scraping using Beautiful Soup Practical Session
    08:58
     
  • Web Scraping using Beautiful Soup Practical Task
    2 Page
  • Time Series Analysis
    07:19
     
  • Time Series Analysis Practical Session
    14:42
     
  • Time Series Analysis Practical Task
    3 Page
  • Bank Loan (Credit Risk) Project Session
    15:52
     
  • Bank Loan (Credit Risk) Project Task
    3 Page
  • Cyber Security Anomaly Detection Project Session
    12:19
     
  • Cyber Security Anomaly Detection Project Task
    3 Page
  • eCommerce Sales Project Session
    17:13
     
  • eCommerce Sales Project Task
    3 Page

Deep Learning and Computer Vision
4 Document Lectures | 6 Video Lectures | 01:13:11

  • Natural Language Processing (NLP) and Text Mining
    21:25
     
  • Sentiment Analysis using Text Blob Practical Session
    05:09
     
  • Sentiment Analysis using Text Blob Practical Task
    1 Page
  • Natural Language Processing using NLTK Practical Session
    08:25
     
  • Natural Language Processing using NLTK Practical Task
    3 Page
  • Market Basket Analysis
    09:02
     
  • Market Basket Analysis Practical Session
    06:33
     
  • Market Basket Analysis Practical Task
    1 Page
  • Recommendation System Project Session
    22:37
     
  • Recommendation System Project Task
    3 Page

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