YOU’VE MADE A BRAVE DECISION, WELCOME FOR UPCOMING BATCHES

Machine Learning, AI & Deep Learning Training Content:

COURSE INTRODUCTION: Machine learning methods are used for data analysis, this is where they are similar to data mining, but the main goal of machine learning is to automate decision models. Algorithms are the heart and soul of machine learning and they help computers to find hidden insights. The training aims at providing the participants with latest and general purpose machine learning algorithms. At the same time the training aims to deliver some common threads or a common knowledge base which can be used in future for learning a wide range of algorithms.
PRE-REQUISITES: Computer fundamentals, Programming fundamentals and knowledge of statistics.
DURATION: 6 Days (48 HRS)

CERTIFICATON: At the end of the course you will get the certificate for completing the training.

Course Content:

CONCEPT OF MACHINE LEARNING

What are the components of the machine learning area

    • Share resources for future and current learning
    • What kinds of problems can be solved by machine learning
    • Appreciate different kinds of data

INTRODUCTION TO R, PYTHON AND SAS.

  • Cover basic of R to make participants comfortable on the tool
  • R is the primary tool for data manipulation and machine learning algorithms.

BASIC STATISTICS

    • Participants need to be clear with basic statistical concepts such as probability distributions, hypothesis testing>
    • Bring all participants to a level where they are comfortable with statistics which is a mandatory component of machine

learning.

REGRESSION- LINEAR AND NON LINEAR

  • Algos- MLR, Logistics and nonlinear regression
  • Make participants hands on with predicting with regression models

CLASSIFICATION

Algos- SVM, decision trees, boosted decision trees, Naïve bayes

  • Classification is the most used class of algos in real business.
  • Participants should be able to choose the correct algo and use it.

QUALITY OF CLASSIFICATION

  • Concepts of ROC, hit rate, kappa statistics and K-S statistics
  • Participants would be able to know how good a classification model has been fitted.

FEATURE SELECTION

How to select useful variables out of substantial number of variables

  • Learn feature selection methods for regression- Ridge and LASSO
  • Feature selection methods for classification methods- Information value based, filter based and wrapper based.

ALGORITHMS AND TECHNIQUES FOR MARKETING ANALYTICS

  • How consumers make decisions and value attributes
  • Conjoint analysis

HIDDEN MARKOV MODELS

  • How to know the consumer is about to leave you
  • Hidden Markov Models for churn analysis.

DEEP LEARNING AND AI

How to use different neural nets for Deep learning

  • Boltzman machines
  • Convolution networks
  • Recurrent neural networks

NATURAL LANGUAGE PROGRAMMING FOR AI

Different components of NLP

  • Parts of Speech
  • Text Similarity

DEBRIEFING

  • Consolidating the learning

 CASE STUDIES and Use-cases