R Programming:  Introduction & Installation of R, R Basics, Finding Help, Code Editors for R, Command Packages, Manipulating and Processing Data in R, Reading and Getting Data into R, Exporting Data from R, Data Objects, Data Types & Data Structure. Viewing Named Objects, Structure of Data Items, Manipulating and Processing Data in R (Creating, Accessing , Sorting data frames, Extracting, Combining, Merging, reshaping data frames), Control Structures, Functions in R (numeric, character, statistical), working with objects, Viewing Objects within Objects, Constructing Data Objects, Building R Packages, Running and Manipulating Packages, Non parametric Tests- ANOVA, chi-Square, t-Test, U-Test, Introduction to Graphical Analysis, Using Plots(Box Plots, Scatter plot, Pie Charts, Bar charts, Line Chart), Plotting variables, Designing Special Plots, Simple Liner Regression, Multiple Regression, Interactive reporting with R markdown.

 Python Programming:

Introduction to Python, Basic Syntax, Data Types, Variables, Operators, Input/output, Flow of Control (Modules, Branching), If, If- else, Nested if-else, Looping, For, While, Nested loops, Control Structure, Break, Continue, Pass, Strings and Tuples, Accessing Strings, Basic Operations, String slices, Working with Lists, Introduction, Accessing list, Operations, Function and Methods, Files, Modules, Dictionaries, Functions and Functional Programming, Declare, assign and retrieve values from Lists, Introducing Tuples, Accessing tuples, matplotlib, seaborn,

Advanced Python: Object Oriented, OOPs concept, Class and object, Attributes, Inheritance, Overloading, Overriding, Data hiding, Operations Exception, Exception Handling, Python Libraries, Data migration and visualization: Pandas and Matplotlib, Database Interaction in Python,

Hardware as AI compute platform, Parallel Programming models: Parallel Python, Scaling Learning models on HPC platform, Deep learning using HPC/Data Centre/Hadoop, Deployment of Models on distributed platform.AI latest trends and future.

Analogy, Series Completion (Number, Alphabet, Letter Series), Coding, Decoding for Number, alphabet and Letter, Blood Relations, Puzzle Test, Classification Type questions, Alphabet test, Order of words, Letter words problems, Logical sequence of words, Number, Ranking and time Sequence Test, Mathematical operations, Arithmetic reasoning, Logical reasoning, Statement Arguments, Statement Assumptions, courses of Action, Statement, Conclusions, Deriving conclusion from passages, Fraction, Number system, Partnership, Average, Percentage, Clock, Probability, Pipes and cisterns, Problem on streams, Time and work, Work and Wages, Problem on Trains, Problem on Speed and Velocity, Problem on Ages, Profit and loss, Simple Interest, Compound Interest.

            General English:

The Sentence, Subject and Predicate, Phrase and Clause, Parts of Speech, The Noun: Kinds of Nouns, The Adjective, Articles, The Verb, Mood, The Adverb, Comparison Of Adverbs, Formation Of Adverbs, Position Of Adverbs, The Preposition, Words Followed By Prepositions, The Conjunction, Some Conjunctions And Their Uses, The Interjection, The Same Word Used As Different Parts Of Speech, Composition, Analysis, Transformation and Synthesis, Analysis of Simple Sentences, Phrases, Clauses, Sentences: Simple, Compound and Complex, More about Noun Clauses, More about Adjective Clauses, More about Adverb Clauses, Analysis of Complex Sentences, Analysis of Compound Sentences, Transformation of Sentences, Transformation of Sentences, Synthesis of Sentences, Synthesis of Sentences, Synthesis of Sentences, The Sequence of Tenses, Direct and Indirect Speech, Agreement of The Verb With The Subject, Nouns and Pronouns, Adjectives, Verbs, Adverbs, Preposition, Conjunctions, Order of Words, Synonyms & Antonyms, Punctuation, Spelling Rules, The Formation of Words, Figures of Speech Exercise, Verb Patterns, Question Tags, Words of Idioms & phrases, Sentence Construction, Fill up the blanks

Introduction to Data Analytics, Descriptive Statistical Measures, Probability Distribution and Data, Sampling and Estimation, Predictive modelling and analysis, Regression Analysis, Forecasting Techniques, Simulation and Risk Analysis, Optimization, Linear, Non linear, Integer, Decision Analysis, Making Right Business Decisions based on data, Exploratory Data Analysis, Visualization and Exploring Data, Text analytics,  Social network analysis, web scrapping, Dimensionality issues, Ridge & lasso regression, bias/variance trade off, density, PCA, FA, Directional Data Analytics, Functional Data Analysis, Data Analysis & visualization – using numpy, matplotlib, scipy, Advanced python packages.

Introduction to machine learning and need, The Learning Problem, Terminology,    Canonical Learning Problems, Supervised Learning, Unsupervised Learning,     Reinforcement Learning, ML applications in the real world,  A key ML concept,           Uses of Machine learning , Introduction to feature engineering, raw data to   feature, Data Preparation ,feature creation, Data cleaning & transformation, Data      Validation & Modelling, Feature selection Techniques, Dimensionality reduction,     PCA, Ensemble methods ,Bagging & Boosting , ML Algorithms, Decision Trees,           Oblique trees, Random forest, Bayesian analysis and Naïve bayes classifier,    Support vector Machines , KNN, Gradient boosting , Association rules learning,   Apriori and FP growth algorithms, Linear and Non liner classification, linear and       logistic Regression, Clustering ,K-means ,Overview of Factor Analysis, ARIMA, ML in real time , Algorithm performance metrics , ROC ,AOC, Confusion matrix ,        F1 score, MSE, MAE.

Machine Learning Tools: introduction to the basic data science toolset

 Case Studies: 

· Usage of ML algorithms, Algorithm performance metrics (confusionmatrixsensitivity,specivity,ROC,AOC,F1score,Precision,Recall,MSE,MAE),

·  Implementation of case studies will be using R / Weka.

·  Implementation of ML algorithms using high level api like Scikit-learn.

·  Credit Card Fraud Analysis , Intrusion Detection system,

·  Implement basic gradient descent in Tensor Flow.

Mathematics

a.  Probability

Basic rules and axioms, events, sample space, frequentist approach, dependent and independent events, conditional probability, Random variables, continuous and discrete, expectation, variance, distributions- joint and conditional, Bayes’ Theorem, MAP, MLE, Popular distributions- binomial, bernoulli, poisson, exponential, Gaussian, Conjugate priors

b.  Linear Algebra

·Vectors, definition, scalars, addition, scalar multiplication, inner product (dot product), vector projection, cosine similarity, orthogonal vectors, normal and orthonormal vectors, vector norm, vector space, linear combination, linear span, linear independence, basis vectors

·Matrices definition, addition, transpose, scalar multiplication, matrix multiplication, matrix multiplication properties, hadamard product, functions, linear transformation, determinant, identity matrix, invertible matrix and inverse, rank, trace, popular type of matrices- symmetric, diagonal, orthogonal, orthonormal, positive definite matrix

·Eigenvalues & eigenvectors, concept, intuition, significance, how to find Principle component analysis, concept, properties, applications

·  Singular value decomposition, concept, properties, applications

  c.  Calculus

·Functions, Scalar derivative, definition, intuition, common rules of differentiation, chain rule, partial derivatives, Gradient, concept, intuition, properties, directional derivative

·  Vector and matrix calculus, how to find derivative of scalar-valued, vector-valued function with respect to scalar, vector}  four combinations- Jacobian

·  Gradient algorithms, local/global maxima and minima, saddle point, convex functions, gradient descent algorithms- batch, mini-batch, stochastic, their performance comparison

d.  Miscellaneous Topics

Information theory, entropy, cross entropy, KL divergence, mutual information

       Markov Chain, definition, transition matrix, stationarity.

Statistics:

Descriptive Statistics, Summary Statistics Basic probability theory, Statistical Concepts (uni-variate and bi-variate sampling, distributions, re-sampling, statistical Inference, prediction error), Probability Distribution(Continuous and discrete- Normal, Bernoulli, Binomial, Negative Binomial,   Geometric and Poisson distribution),Bayes’ Theorem, Central Limit theorem, Data Exploration & preparation, Concepts of Correlation, Regression, Covariance, Outliers etc.

Case studies: successful examples of RL systems,  simulation based methods like Q-learning.

Artificial Intelligence in Production (20hrs)

Deployment & Maintenance of AI Applications, AI application testing, AI model, interoperability, problem solving approaches.

 Case studies:

AI applications in Financial Services including Insurance banking, stock markets & other financial markets like Forex  and Artificial Economics, AI applications in Health Sciences & other Scientific Applications, AI in Cloud Environment.