Introduction to AI, Evolution & Revolution of AI, Ethics of AI, Structure of AI, Responsible 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

  

1. Linear Algebra (30 Hrs)

• Vectors, definition, scalars, addition, scalar multiplication, inner product (dot product), vector projection, cosine similarity, orthogonal vectors, normal and ortho-normal vectors, vector norm, vector space, linear combination, linear span, linear independence.
• Matrices definition, addition, transpose, scalar multiplication, matrix multiplication, matrix multiplication properties, hadamard product, functions, determinant, identity matrix, invertible matrix and inverse, rank, trace, symmetric, diagonal, orthogonal, ortho-normal.
• Eigen values & eigen vectors, concept, intuition, significance, how to find Principle components , concept, properties, applications
• Singular value decomposition, concept, properties, applications

1. Calculus (20 Hrs)

• Function 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

   JDK, JRE, JVM overview, Working with Data Types, Operators, Arrays, Strings, Constructors, Classes and Objects, Object Oriented Concepts, Exception Handling, Generics & Collections overview, Java APIs (java. Lang, java. util) Functional Programming, Functional Interfaces, Introduction to Streams, Threads, Reflection in Java, Introduction to Node.js, Introduction to Spring Framework.

  

  

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, Pearson Correlation, Outliers, Regression Analysis, Forecasting Techniques, Simulation and Risk Analysis, Optimization, Linear, Nonlinear, Integer, Overview of Factor Analysis, Directional Data Analytics, Functional Data Analysis , Hypothesis Techniques, Z-Test, chi-Square Test, Skewness.

Predictive Modeling (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; Over fitting 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; Intro to KNIME tool.

Python Libraries –Numpy, Scipy, Pandas

  

Machine Learning in Nut shell, Supervised Learning, Unsupervised Learning, ML applications in the real world. 

Introduction to Feature engineering and Data Pre-processing: Data Preparation, Feature creation, Data cleaning & transformation, Data Validation & Modelling, Feature selection Techniques, Dimensionality reduction, PCA, t-SNE.

ML Algorithms: ML Algorithms: , Linear and Nonlinear classification, Regression Techniques, Support vector Machines, KNN, K-means , Decision Trees, Oblique trees, Random forest, Bayesian analysis and Naive Bayes classifier, Gradient boosting, Ensemble methods, Bagging & Boosting , Association rules learning, Apriori algorithms, Clustering, Overview of Factor Analysis, ARIMA, ML in real time, Algorithm performance metrics, ROC, AOC, Confusion matrix, F1score, MSE, MAE, DBSCAN Clustering in ML, Anomaly Detection with isolation forest, Recommender Systems.

Self-Study:

• 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, Pytorch, building deep learning models, building a basic neural network using Keras with Tensor Flow, Troubleshoot deep learning models, building deep learning project. (A log 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

  

Natural Language Processing: 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, 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, spaCy 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, Building NLP Application from scratch 

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, Image Net, Gender Prediction, Face / Object Recognition, Introduction to object detection Algorithms - RCNN ,Fast RCNN, Faster RCNN, Mask RCNN,YOLO.

  

Apache Spark

• Apache Spark APIs for large-scale data processing: Basics of Spark,Deploying to a Cluster Spark Streaming, Spark ML lib 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, spark session, spark context, spark data frames, ETL jobs using spark.
• AI Future Trends

DevOps for AI/ML

• Git/Github: Introduction to Version control systems, Creating Github repository, Using Git – Introduction to git commands.
• Introduction to containers: Introduction to DevOps, Introduction to Containers, Advantages of using container based applications, Managing containers – Logs / Resources
• Introduction to Kubernetes, Need for Kubernetes, Introduction to Kubernetes cluster ,Working with Kubernetes Cluster – Creating deployment, Exposing Deployment as a service, Managing your applications. Rolling application updates etc.
• CI/CD with Jenkins: Introduction to CI/CD, Using Jenkins to build a CI/CD pipeline.

Cloud Computing:

• Cloud Computing Basics, Understanding Cloud Vendors (AWS/Azure/GCP), Definition, Administering & Monitoring cloud services, Cloud Pricing, Compute Products and Services, Elastic Cloud Compute, Dashboard.
• Exploring cloud services for AI/ML

Self-Study: 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.

   Aptitude: Percentage, Profit & Loss, Ratio & Proportion, Average, Mixture & Allegation, Simple Interest & Compound Interest, Number Systems, Series, Cyclicity & Remainders, Data Interpretation, Syllogism, Coding & Decoding, Blood Relations, Seating Arrangements (Linear & Circular), Ages, Puzzles, Time, Speed & Distance, Trains, Boats & Streams, Time & Work, Wages (Man days), Pipes & Cisterns, Clocks, Permutations & Combinations, Probability, Calendar.

  

Artificial Intelligence in Production (20hrs)

Deployment & Maintenance of AI Applications, AI application testing, 

AI model, interoperability, problem solving approaches.