Artificial Intelligence / Machine Learning / Deep Learning

COURSE OUTLINE:

This course will provide an excellent kick start in improving the level in AI/Machine Learning as a domain. The course is a well balanced between theory and hands-on lab, spread on real-world uses case studies. It will also offer a good foundation for Deep learning.


COURSE OBJECTIVES:

Upon successful completion of this course, the participant will be able to:
• Understand and analyze AI/ML opportunities, have balanced conversations with their clients on AI/ML topics.
• Identify and analyze different industry verticals for AI/ML problems by studying relevant Case studies (including UBER in NY, Netflix for content recommendations, TAG prediction and association for content, cancer diagnosis in healthcare, Malware detection in Microsoft dataset, Amazon fashion discovery engine, Chatbot concepts)
• Understand AI, ML, DL concepts and foundation for its algorithms.
• Start implementing algorithms in real world datasets
• Get the current industry status of Deep Learning
• Start thinking about how to automate things by relating to the case studies discussed in the sessions.





Pre-Requisites:
The participants should have basic knowledge of Python, Probability and Statistics. It is advised to refresh these skills to obtain maximum benefit from this workshop. If candidate don’t have any programming background, we will be covering Python fundamentals as we go along in the program. However, we also plan to offer two sessions ahead of the program (1: Python Basics and 2: Maths/Statistics Refresher), for those who want to formally brush up on these topics.
Upon successful completion of this course, the participant will:
  • be able to understand and analyze AI/ML opportunities, have balanced conversations with your clients on AI/ML topics.
  • Develop ability to understand/analyze different industry verticals for AI/ML problems by studying relevant Case studies (including UBER in NY, Netflix for content recommendations, TAG prediction and association for content, cancer diagnosis in healthcare, Malware detection in Microsoft dataset, Amazon fashion discovery engine, Chatbot concepts)
  • Understand AI,ML, DL concepts and foundation for its algorithms.
  • How to start implementing algorithms in real world datasets
  • Get the current industry status of Deep Learning
  • Start thinking about how to automate things by relating to the case studies discussed in the sessions.
Course Content
Hour 0-3
INTRODUCTION TO AI/ML ECOSYSTEMS AD AND MARKET DRIVERS
EXPLORATORY DATA ANALYSIS (EDA)
LINEAR ALGEBRA BASICS
Hour 4-10
PROBABILITY AND STATISTICS
DIMENSIONALITY REDUCTION AND VISUALIZATION
PCA (PRINCIPAL COMPONENT ANALYSIS)
Hour 11-20
(T-SNE)T-DISTRIBUTED STOCHASTIC NEIGHBOURHOOD EMBEDDING
REAL WORLD PROBLEM: PREDICT RATING GIVEN PRODUCT REVIEWS ON AMAZON
CLASSIFICATION AND REGRESSION MODELS: K-NEAREST NEIGHBORS
CLASSIFICATION ALGORITHMS IN VARIOUS SITUATIONSs
Hour 21-30
PERFORMANCE MEASUREMENT OF MODELS
NAIVE BAYES
LOGISTICREGRESSION
LINEAR REGRESSION
SOLVING OPTIMIZATION PROBLEMS
QUICKCHECK ON LOGISTIC REGRESSION AND LINEAR REGRESSION
SUPPORT VECTOR MACHINES (SVM)
CASE STUDY 1: 1A) SURVIVAL PREDICTION IN TITANIC, 1B) TENNIS MATCH PREDICTION WIMBLEDON DATA SET
Hour 31-38
DECISION TREES
FEATURIZATION AND FEATURE
UNSUPERVISED LEARNING/CLUSTERING
HIERARCHICAL CLUSTERING TECHNIQUE
DBSCAN (DENSITY BASED CLUSTERING) TECHNIQUE
RECOMMENDER SYSTEMS AND MATRIX FACTORIZATION
Hour 39-45
CASE STUDY 2: PERSONALIZED CANCER DIAGNOSIS
CASE STUDY 3:TAXI DEMAND PREDICTION IN NEW YORK CITY
CASE STUDY 4: MICROSOFT MALWARE DETECTION
CASE STUDY 5:NETFLIX MOVIE RECOMMENDATION SYSTEM
CASE STUDY 6: STACKOVERFLOW TAG PREDICTOR
Hour 46-60
CASE STUDY 7: QUORA QUESTION PAIR SIMILARITY PROBLEM
CASE STUDY 8: AMAZON FASHION DISCOVERY ENGINE
DEEP LEARNING:NEURAL NETWORKS
DEEP LEARNING: DEEP MULTI-LAYER PERCEPTRONS
DEEP LEARNING: TENSORFLOW AND KERAS.
DEEP LEARNING: CONVOLUTIONAL NEURAL NETS.
DEEP LEARNING: LONG SHORT-TERM MEMORY (LSTMS)
Hour 61-70
Deep Dive in Deep Learning
AD-Click Prediction
Human Activity Recognition using smartphones
Song similarity and genre classification
Facebook Friend Recommendation using Graph Mi
Corporate Trainer: Background And Experience Summary
  • Technical expert and a passionate trainer having a keen interest in Research & Development, Technical Training and Project Management, have a proven work record of delivering more than 100+ workshops and Technical Training in various technologies at Corporate Companies
  • Have been training students in various technical topics and core computer science subjects since 2012.
  • Worked as technical consultant for Appolo and Wockhardt Hospitals. Worked for Oracle as a Software Engineer.
  • Have been working with Vnurture Technologies in the areas of Corporate training and project consultancy.
Clients Served
  • General Electric, Philips, Reserve Bank of India, Verizon (Yahoo), Sunlife Financial, Harman, Philips, JP Morgan, CGI, HP, Samsung, Accenture, National Payment Corporation of India, Bharat Electronics Limited, Brilio and in education institutes including IITs, NITs, Business Schools like Globsyn Crystals and other premier educational organizations.
  • Delivered Data Science training for JP Morgan 2018
  • Delivered Machine Learning and Deep Learning for CGI 2018
  • Consulted Sun Life Financial for Deep Learning projects.
  • Conducting Training on Applied AI for Verizon.
  • Subject Matter Expert for Upgrade (Machine Learning program)
  • Delivered training on AI and ML to General Electric 2018
  • Delivered training for Applied AI via Python at BEL.
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