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

•Eigen values & eigenvectors, concept, intuition, significance, how to find Principle component    analysis, concept, properties, applications

•Singular value decomposition, concept, properties, applications

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

Miscellaneous Topics

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

•Markov Chain, definition, transition matrix, stationarity

R Programming: Introduction & Installation of R, R Basics, Finding Help, Code Editors for R, Command Packages, Exploratory Data Analysis, Data Objects, Data Types & Data Structure. Viewing Named Objects, Structure of Data Items, Control Structures, Functions in R (numeric, character, statistical), working with objects, Viewing Objects within Objects, Constructing Data Objects, Non parametric Tests- ANOVA, chi-Square, t-Test, U-Test

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, Decorators, Attributes, Inheritance, Overloading, Overriding, Data hiding, Operations Exception, Exception Handling, Python Libraries, Data migration and visualization: Pandas and Matplotlib, Database Interaction in Python, Web based frameworks: Flask and Django

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

Introduction to AI, Evolution & Revolution of AI, Introduction to AI, Introduction of Applications in various Domains (Scientific including Health Sciences, Engineering, Financial Services and other industries), Ethics of AI, Structure of AI, Real world Implications, Intelligent Agents, Uninformed Search, Constraint Satisfaction Search, Combinatorial Optimization Problems, Heuristic & Meta-heuristics, Genetic Algorithms for Search, Game Trees, Supervised & Unsupervised Learning, Knowledge Representation, Propositional and Predicate Logic, Inference and Resolution for Problem Solving, Rules and Expert Systems, Artificial Life, Emergent Behavior, Genetic Algorithms

Introduction to Business Analytics using some case studies, Summary Statistics, Making Right Business Decisions based on data, Statistical Concepts, Descriptive Statistics and its measures, Probability theory, Probability Distributions (Continuous and discrete- Normal, Binomial and Poisson distribution) and Data, Sampling and Estimation, Statistical Interfaces, Predictive modeling and analysis, Bayes’ Theorem, Central Limit theorem, Data Exploration & preparation, Concepts of Correlation,  Covariance, Outliers, Regression Analysis, Forecasting Techniques, Simulation and Risk Analysis, Optimization, Linear, Nonlinear, Integer, Overview of Factor Analysis, Directional Data Analytics, Functional Data Analysis , Predictive Modelling (From Correlation To Supervised Segmentation): Identifying Informative Attributes, Segmenting Data By Progressive Attributive, Models, Induction And Prediction, Supervised Segmentation, Visualizing Segmentations, Trees As Set Of Rules, Probability Estimation; Overfitting And Its Avoidance: Generalization, Holdout Evaluation Vs Cross Validation; Decision Analytics: Evaluating Classifiers, Analytical Framework, Evaluation, Baseline, Performance And Implications For Investments In Data; Evidence And Probabilities: Explicit Evidence Combination With Bayes Rule, Probabilistic Reasoning; Business Strategy: Achieving Competitive Advantages, Sustaining Competitive Advantages

Python Libraries – Pandas, Numpy, Scipy, Scrapy, Plotly, Beautiful soup

Machine Learning in Nut shell, Supervised Learning, Unsupervised Learning, ML applications in the real world, Uses of Machine learning

Introduction to Feature engineering and Data Pre-processing: Data Preparation, Feature creation, Data cleaning & transformation, Data Validation & Modelling, Feature selection Techniques, Dimensionality reduction, Recommendation Systems and anomaly PCA,

ML Algorithms: Decision Trees, Oblique trees, Random forest, Bayesian analysis and Naïve bayes classifier, Support vector Machines, KNN,  Gradient  boosting, Ensemble methods, Bagging & 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, DBSCAN Clustering in ML, Anomaly Detection, Recommender System

Machine Learning Tools: Introduction to the basic data science toolset

Case Studies:

• Usage of ML algorithms, Algorithm performance metrics (confusion matrix sensitivity, Specificity,  ROC, AOC, F1score, Precision, Recall, MSE, MAE)

• Credit Card Fraud Analysis, Intrusion Detection system

Introduction to Deep Neural Network, RNN, CNN, LSTM, Deep Belief Network, semantic Hashing, Training deep neural network, Tensorflow 2.x, Pythorch, 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, Tuning Deep Learning Models, Trends in Deep Learning, Application of Multi Processing in DL, Deep Learning Case Studies

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), NLP Transformers, Bert in NLP Speech Processing, NLP Model Deployment Techniques using Flask, NLP Applications- Language identification, Auto suggest/ Auto complete, chat bots, Robotics

Computer Vision

Introduction to Computer Vision, Computer Vision and Natural Language Processing, The Three R's of Computer Vision, Basics of Image Processing, Low-, Mid- & High-Level Vision, Edge Detection, Interest Points and Corners, Image Classification, Recognition, Bag of Features, and Large-scale Instance Recognition, Object Detection & Transfer Learning, AlexNet, ResNet, ImageNet, Gender Prediction, Face / Object Recognition

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), Reinforcement Learning Applications, Implementing a Reinforcement Learning application

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

Apache Spark APIs for large-scale data processing: Basics of Spark, Deploying to a Cluster Spark Streaming, Spark MLlib and ML APIs, Spark Data Frames/Spark SQL, Integration of Spark and Kafka, Setting up Kafka Producer and Consumer, Kafka Connect API, Connecting DB’s with Spark

AI Compute Platforms: Deep Vision, Cloud Machine Learning Engine, Tensorflow

AI Network optimization using Intel OpenVino Toolkit

Parallel Python-Sample implementation, Architecture Details for Compute efficiency, Parallel Programming: Models, Aspects, Multi-Process and Multi Thread, Hardware for AI, Various new CPU aspects and optimizations, Compiler optimizations, AI Distributed Learning, AI latest trends and future.

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. Deployment of Models on distributed platform.AI latest trends and future.

Artificial Intelligence in Production (20hrs)

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