COURSE LISTING

Introduction to Programming in Python. Includes Variables, Expressions, Statements, Functions, Conditionals, Recursion, Iteration, Arrays, Strings, Lists, Tuples, Dictionaries, Files and Exceptions, Classes, Objects, Inheritance. Manipulating, processing and cleaning data in Python.  Includes basics of Python libraries such as NumPy, Matplotlib, Pandas. Includes data loading, wrangling, visualization, aggregation, Advanced Numpy.

This course teaches you how to manipulate and analyze language data using key concepts from NLP, various NLP algorithms and the NLTK toolkit. Accessing text corpora and lexical resources, processing raw text, regular expressions, tokenization, POS tagging, N-grams, classifying text, decision trees, naive Bayes, MaxEnt, information extraction, NER, CFG, syntactic parsing, semantic parsing, statistical parsing, dependency grammars, NLTK..

Neural Networks, Introduction, Activation Functions, Network Architectures, CNN, RNN, LSTMs. Applications in Computer Vision, Text Processing. Deep Learning with Keras and Google Colab.

Multi-armed bandits, Finite Markov Decision Processes, Optimal policies and value functions, Dynamic Programming, Policy evaluation, Policy improvement, Monte-Carlo methods, Temporal difference learning, N-step bootstrapping, Planning and learning with tabular methods, Approximate solution methods.

 Introduction; Constants and Variables; Types, Operators, Expressions; Control flow and Iteration Statements; Functions and Program Structure; Pointers and Arrays; I/O; Files ,Structures and Unions, Bitwise operations.

Introduction, Classes and Members, Objects, Overloaded Functions, Friend Functions, Overloaded Operators, References, Constants, Type Casting, Constructors, Destructors, Static Members, Inheritance, Templates, Multiple Inheritance, Implementation of  Data Structures: Arrays, Queues, Stacks, Linked Lists.

The focus will be on algorithms, data structures and problem solving. This will be a tutorial style course with emphasis on problem solving, rather than mere description of usual textbook algorithms and data structures. The course will involve solving more than 100 problems in class.

IT Platform for a cloud based service. HDInsight, SageMaker, Data Factory, Functionalities, Architecture, Integration, Streaming: Event and Analytics. Kafka and Spark architecture, SDLC, Integration with CloudConfluent, Azure Databricks, Data Services, No-SQL, Data Lakes. NFR-Security, Performance, Scalability, Case Studies.

This course is for those  who wish to perform various machine learning tasks using open source libraries. Covers classification, linear and logistic regression, clustering, k-means, Naive Bayes, decision trees, kNN, association rules, dimensionality reduction. Applications include topic modeling, sentiment analysis, recommender systems using Python libraries such as NumPy, SciPy and Scikit -learn.

Prerequisites: Knowledge of Python Programming Fundamentals. In this course, you will learn how to build robust web applications with Python. You will go through the basics of Web Design with HTML, CSS, Javascript and MySQL. You will learn two Python Frameworks, Django and Flask. At the end of this course, you should be able to build a web application from scratch.

Arrays, Linked Lists, Trees, Heaps, Binary Search Trees, Graphs, Analysis, Asymptotic Notations, Sorting, Lower Bounds, Binary Search and variants, Binary Search Trees, Divide and Conquer, Dynamic Programming, Greedy Method, Amortized Analysis, Disjoint Sets, Graph Algorithms Studying relative hardness of problems, NP-Hard and NP-complete Problems. Reduction Proofs. Coping with NP-Completeness, Approximation algorithms, Branch and Bound Algorithms.