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

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 

Case Studies: 

Mathematical computing with Python, Data migration and visualization: Pandas and Matplotlib, Pycharm, Anaconda, Data manipulation with Pandas

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 (confusion matrix sensitivity, 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.

Introduction to Deep Neural Network, RNN, CNN ,LSTM, Deep Belief Network, semantic Hashing, Training deep neural network, introduction to Tensorflow, building deep learning models, building a basic neural network using Keras with Tensor Flow, Troubleshoot deep learning models, building deep learning project. (Alog model), Transfer Learning, Inductive, unsupervised Transductive, Deep Learning Tools & Technique

Introduction to reinforcement learning as an approximate dynamic programming problem, Overview of reinforcement learning: the agent environment framework, successes of reinforcement learning, Bandit problems and online learning, Markov decision processes, Returns, and value functions, Solution methods: dynamic programming, Solution methods for learning, Solution methods for temporal difference learning, Eligibility traces, Value function approximation Models and planning (table lookup case) 

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

Understanding Language, NLP Overview, Introduction to Language Computing, Language in Cognitive Science, Definitions of language, Language as a rule-governed dynamic system, Language and symbolic systems: Artificial language (Logical language/programming language) vs. Natural Language, Linguistics as a scientific study, And Description of different branches of Linguistics: Statistical Linguistics, Psycholinguistics, Neurolinguistics, Computational Linguistics, Sociolinguistics etc.

Language Analysis and Computational Linguistics, Semantics, Discourse, Pragmatics, Lexicology, Shallow Parsing and Tools for NLP, Deep Parsing and Tools for NLP, Statistical Approaches, NLP with Machine Learning and Deep Learning, Pre-processing, Need of Pre-processing Data, Introduction to NLTK, Using Python Scripts

Word2Vec models (Skip-gram, CBOW, Glove, one hot Encoding), Speech Processing, Machine Vision, NLP Applications, Language identification, Auto suggest/ Auto complete, Gender Prediction, Face Recognition, Robotics, chat bots.