Executive Post Graduate Certification in Data Science & Artificial Intelligence

Python 

• Introduction to Python and IDEs – The basics of the python programming language, how you can use various IDEs for python development like Jupyter, Pycharm, etc. 
• Python Basics – Variables, Data Types, Loops, Conditional Statements, functions, decorators, lambda functions, etc.
• Object Oriented Programming – Introduction to OOPs concepts like classes, objects, inheritance, abstraction, polymorphism, encapsulation, etc.
• Hands-on Sessions And Assignments for Practice – The culmination of all the above concepts with real-world problem statements for better understanding. 

Linux

• Introduction to Linux  – Establishing the fundamental knowledge of how linux works and how you can begin with Linux OS. 
• Linux Basics – File Handling, data extraction, etc.
• Hands-on Sessions And Assignments for Practice – Strategically curated problem statements for you to start with Linux. 

SQL Basics – 

• Fundamentals of Structured Query Language
• SQL Tables, Joins, Variables 

Advanced SQL –  

• SQL Functions, Subqueries, Rules, Views
• Nested Queries, string functions, pattern matching
• Mathematical functions, Date-time functions, etc. 

Deep Dive into User Defined Functions

• Types of UDFs, Inline table value, multi-statement table. 
• Stored procedures, rank function, SQL ROLLUP, etc.

SQL Optimization and Performance

• Record grouping, searching, sorting, etc. 
• Clustered indexes, common table expressions.

Hands-on exercise: 

Writing comparison data between past year to present year with respect to top products, ignoring the redundant/junk data, identifying the meaningful data,  and identifying the demand in the future(using complex subqueries, functions, pattern matching concepts).

Statistics and Descriptive Analytics using MS Excel

• Measure of central tendency, measure of spread, five points summary, etc.
• Probability Distributions, Probability in Business Analytics.
• Probability Distributions, Binomial distribution, Poisson distribution, bayes theorem, central limit theorem.

Python for Descriptive, Diagnostic, and Inferential Statistics

• Correlation, covariance, confidence intervals, hypothesis testing, F-test, Z-test, t-test, ANOVA, chi-square test, etc.

Prescriptive Analytics

Introduction to Machine learning 

• Supervised, Unsupervised learning.
• Introduction to scikit-learn, etc.

Regression 

• Introduction classification problems, Identification of a regression problem, dependent and independent variables.
• How to train the model in a regression problem.
• How to evaluate the model for a regression problem.
• How to optimize the efficiency of the regression model.

Classification 

• Introduction to classification problems, Identification of a classification problem, dependent and independent variables.
• How to train the model in a classification problem.
• How to evaluate the model for a classification problem.
• How to optimize the efficiency of the classification model.

Clustering 

• Introduction to clustering problems, Identification of a clustering problem, dependent and independent variables.
• How to train the model in a clustering problem.
• How to evaluate the model for a clustering problem.
• How to optimize the efficiency of the clustering model.

• Linear Regression – Creating linear regression models for linear data using statistical tests, data preprocessing, standardization, normalization, etc.
• Logistic Regression – Creating logistic regression models for classification problems – such as if a person is diabetic or not, if there will be rain or not, etc.
• Decision Tree – Creating decision tree models on classification problems in a tree like format with optimal solutions.
• Random Forest – Creating random forest models for classification problems in a supervised learning approach.
• Support Vector Machine – SVM or support vector machines for regression and classification problems.
• K-Nearest Neighbors – A simple algorithm that can be used for classification problems.
• Time Series Forecasting – Making use of time series data, gathering insights and useful forecasting solutions using time series forecasting.

Performance Metrics

• Classification reports – To evaluate the model on various metrics like recall, precision, f-support, etc.
• Confusion matrix – To evaluate the true positive/negative, false positive/negative outcomes in the model.
• r2, adjusted r2, mean squared error, etc.

• K-means – The k-means algorithm that can be used for clustering problems in an unsupervised learning approach.
• Dimensionality reduction – Handling multi dimensional data and standardizing the features for easier computation.
• Linear Discriminant Analysis –  LDA or linear discriminant analysis to reduce or optimize the dimensions in the multidimensional data.
• Principal Component Analysis – PCA follows the same approach in handling the multidimensional data.

Bagging And Boosting Algorithms

• XGBoost – Extreme Gradient Boosting or XGBoost algorithm works on an ensemble approach that combines the predictions of weak models to produce a strong prediction.
• ADAboost – An ensemble machine learning algorithm that converts the weak learners to strong learners for better performance.
• Gradient Tree Boosting – One of the powerful boosting algorithms works on the principle of reducing the bias in predictions.
• Bootstrap Aggregation – A bagging technique that fits decision trees on bootstrap samples of the training data.

Other Machine Learning Algorithms

• Ordinary Least Square – A simple regression algorithm that is used to estimate the coefficient of the linear regression equation for predictions.
• Markov Chain – A stochastic approach or algorithm that describes the possible chain of events in terms of probability theory.
• Naive Bayes – Bayes theorem inspired algorithm in machine learning that is used for predictive modeling.
• Stochastic Gradient Descent – A variant of Gradient Descent that is used to optimize machine learning algorithms.
• Gaussian Mixture Model – A probabilistic model that assumes the data points are generated from a mix of gaussian distributions with unknown parameters.
• Singular Value Decomposition – Algorithm based on factorization of a real or complex matrix, that is often used to create a recommendation engine.

Predictive Analytics And Machine Learning

• Regression and Multivariate Analysis 
• Classification problems in machine learning 
• Data Multidimensionality and Linear Algebra
• Feature engineering and Feature selection 
• Hyperparameter Tuning and other optimization techniques

Cognitive Science and Analytics

• Understanding Natural Language Processing Applications (e.g. Search Engines and Social Media)
• Machine Learning Applications and Chatbots
• Social Media Analytics Advanced Text Mining like Sentiment Analysis, Topic Modelling, and Text Summarisation

Artificial Intelligence Basics 

• Introduction to keras API and tensorflow

Neural Networks

• Single Cell (perceptron)
• Multi cell perceptron Topology
• Weights & Biases
• Build a NN from scratch (using numpy)

Deep Learning 

• Use cases of DL in industry
• Difference between DS, ML, DL & AI
• Lifecycle of Deep Learning Project

RBM and DBNs & Variational AutoEncoder

• Introduction rbm and autoencoders
• Deploying rbm for deep neural networks, using rbm for collaborative filtering
• Autoencoders features and applications of autoencoders.

Object Detection using Convolutional Neural Net

• Constructing a convolutional neural network using TensorFlow
• Convolutional, dense, and pooling layers of CNNs
• Filtering images based on user queries

Generating images with Neural Style and Working with Deep Generative Models

• Automated conversation bots leveraging
• Generative model, and the sequence to sequence model (lstm).

Distributed & Parallel Computing for Deep Learning Models

• Parallel Training, Distributed vs Parallel Computing
• Distributed computing in Tensorflow, Introduction to tf.distribute
• Distributed training across multiple CPUs, Distributed Training
• Distributed training across multiple GPUs, Federated Learning
• Parallel computing in Tensorflow

Reinforcement Learning

• Mapping the human mind with deep neural networks (dnns)
• Several building blocks of artificial neural networks (anns)
• The architecture of dnn and its building blocks
• Reinforcement learning in dnn concepts, various parameters, layers, and optimization algorithms in dnn, and activation functions.

Deploying Deep Learning Models and Beyond

• Understanding model Persistence, Saving and Serializing Models in Keras, Restoring and loading saved models
• Introduction to Tensorflow Serving, Tensorflow Serving Rest, Deploying deep learning models with Docker & Kubernetes, Tensorflow Serving Docker, Tensorflow Deployment Flask.
• Deploying deep learning models in Serverless Environments
• Deploying Model to Sage Maker
• Explain Tensorflow Lite Train and deploy a CNN model with TensorFlow

• LSTM – What is LSTM?, How does LSTM work, Applications of LSTM, etc.
• Transformers – What are transformers, how does a transformer work in deep learning, applications of transformers, types of transformers, encoder-decoded, self-attention, etc.
• BERT – Language Models, What is BERT, How does BERT work, how is BERT different from LSTM, applications of BERT, etc.
• GPT – What are generative pre-trained models (GPT), how does a GPT work?, real-life examples of GPT, etc.
• LLM – NLP and Language models, what are LLMs, how does an LLM work, applications of LLM, etc.
• VAEs – Introduction to Variational Autoencoders, Architecture of VAEs, creating VAEs for image generation.
• Langchain – Intuition for Langchain, Langchain applications, Langchain architecture, How to work with Langchains, etc.

• Prompt Engineering – Science behind Prompts, Prompt Engineering Basics, Impact and usage of Prompt Engineering, Prompt Engineering tools, Effectiveness of Prompts, etc.
• Image-Based Applications of Generative AI – Image-based workflows and artifacts using Generative AI with image generation, text-image generation, etc.
• Text-Based Applications of Generative AI – Leveraging Text summarization and text generation for text-based applications using Generative AI tools.
• Audio-Based Applications of Generative AI – Text to Audio generation, Audio processing, Audio generation leveraging Generative AI tools to create end-to-end applications.

Introduction to MLOps 

• MLOps lifecycle
• MLOps pipeline 
• MLOps Components, Processes, etc

Deploying Machine Learning Models 

• Introduction to Azure Machine Learning 
• Deploying Machine Learning Models using Azure

The Data Science capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data analysis perspective. The capstone project will help you master the following verticals: 

• Extracting, loading and transforming data into usable format to gather insights. 
• Data manipulation and handling to pre-process the data.
• Feature engineering and scaling the data for various problem statements. 
• Model selection and model building on various classification, regression problems using supervised/unsupervised machine learning algorithms.
• Assessment and monitoring of the model created using the machine learning models.

• Recommendation Engine – The case study will guide you through various processes and techniques in machine learning to build a recommendation engine that can be used for movie recommendations, restaurant recommendations, book recommendations, etc.
• Rating Predictions – This text classification and sentiment analysis case study will guide you towards working with text data and building efficient machine learning models that can predict ratings, sentiments, etc.
• Census – Using predictive modeling techniques on the census data, you will be able to create actionable insights for a given population and create machine learning models that will predict or classify various features like total population, user income, etc.
• Housing – This real estate case study will guide you towards real world problems, where a culmination of multiple features will guide you towards creating a predictive model to predict housing prices.
• Object Detection – A much more advanced yet simple case study that will guide you towards making a machine learning model that can detect objects in real time.
• Stock Market Analysis – Using historical stock market data, you will learn about how feature engineering and feature selection can provide you some really helpful and actionable insights for specific stocks.
• Banking Problem – A classification problem that predicts consumer behavior based on various features using machine learning models.
• AI Chatbot – Using the NLTK python library, you will be able to apply machine learning algorithms and create an AI chatbot.