Executive Post Graduate Certification in AI and Machine Learning

Introduction to Linux

• Linux Fundamentals: Understanding the basics of the Linux operating system.
• Getting Started: Setting up and navigating Linux.

Linux Basics

• File Handling in Linux: Learning essential file operations in Linux.
• Data Extraction: Extracting and manipulating data using Linux commands.

Introduction to Python and IDEs

• Python Overview: Introduction to the Python programming language.
• IDEs for Python: Exploring various Integrated Development Environments for Python such as Jupyter.

Python Basics

• Variables and Data Types: Understanding Python variables and different data types.
• Control Flow: Learning about loops and conditional statements in Python.
• Functions and Lambda Functions: Exploring functions,, and lambda functions in Python.
• File Handling and Exception Handling: Managing files and handling exceptions in Python.

Object-Oriented Programming (OOP) in Python

• OOP Concepts: Introduction to Object-Oriented Programming concepts like classes, objects, inheritance, abstraction, polymorphism, and encapsulation.

Hands-on Sessions and Assignments

• Practical Application: Applying the knowledge gained in both Linux and Python through hands-on sessions and assignments.
• Real-World Problem Solving: Solving real-world problem statements to reinforce learning in both Linux and Python.

Managing Data with NumPy

• Operations with NumPy Arrays, including Create, Read, Update, Delete (CRUD) operations.
• Fundamentals of Linear Algebra: Operations like Matrix Multiplication, Matrix Inversion, Transposition, Determining Matrix Rank and Determinant, working with Scalars, Vectors, and Matrices.

Utilizing Pandas for Data Handling

• Techniques for Data Importation, Utilizing DataFrames and Series, Executing CRUD Operations, Segmenting Data.

Preprocessing of Data

• Conducting Exploratory Data Analysis, Implementing Feature Engineering Techniques, Scaling Features, Normalizing and Standardizing Data.
• Approaches for Null Value Treatment, Outlier Identification and Resolution, Variance Inflation Factor (VIF) Analysis, Balancing Bias-Variance, Employing Cross-Validation Techniques, and Splitting Data into Training and Testing Sets.

Visualizing Data

• Creating Visual Representations like Bar Charts, Scatter Plots, Count Plots, Line Plots, Pie Charts, and Donut Charts using Python’s Matplotlib Library.
• Developing Regression and Categorical Plots, Area Plots, and more with Python’s Seaborn Library.

Introduction to SQL

• Essential Concepts in Structured Query Language
• Understanding SQL Tables, Joins, and Variables

Proficiency in Advanced SQL

• Exploration of SQL Functions, Subqueries, Rules, and Views
• Techniques in Nested Queries, String Functions, and Pattern Matching
• Utilization of Mathematical and Date-Time Functions

Mastering User-Defined Functions in SQL

• Varieties of User-Defined Functions (UDFs), including Inline Table-Valued and Multi-Statement Table Functions
• Implementation of Stored Procedures, Rank Function, and SQL ROLLUP

SQL for Optimized Performance

• Strategies for Effective Record Grouping, Searching, and Sorting
• Utilization of Clustered Indexes and Common Table Expressions for Enhanced Performance

Practical SQL Application: Hands-on Exercise

• Crafting Comparative SQL Queries to Analyze Data Trends Over Time
• Focusing on Significant Data by Filtering Out Redundancies
• Predictive Analysis: Identifying Future Demands using Advanced SQL Techniques including Complex Subqueries, Functions, and Pattern Matching Concepts

• Linear and regularised regression
• Logistic regression
• Decision trees and random forests
• K-Nearest Neighbours (KNN)
• Support Vector Machines (SVM)

Industry Projects:

• Customer Churn Prediction

Bagging and Boosting Techniques

• XGBoost: Implementation of Extreme Gradient Boosting, an ensemble method that enhances predictions by combining multiple weaker models.
• ADAboost: Utilization of an ensemble algorithm that transforms weak learning models into stronger ones, improving overall performance.
• Gradient Tree Boosting: A sophisticated boosting technique focused on minimizing bias in model predictions.
• Bootstrap Aggregation: Application of a bagging strategy, employing decision trees on bootstrap samples from the training data.

Diverse Machine Learning Methodologies

• Ordinary Least Square (OLS): A fundamental regression technique for estimating linear regression equation coefficients.
• Markov Chain: A probability-based algorithm used to model sequential events or states.
• Naive Bayes: Implementation of a predictive model inspired by Bayes’ theorem, widely used in machine learning.
• Gaussian Mixture Model: A probabilistic model assuming data generation from various Gaussian distributions with unknown parameters.
• Singular Value Decomposition: A matrix factorization technique, often employed in creating recommendation systems.

Predictive Analytics in Machine Learning

• Exploring Regression and Multivariate Analysis in predictive modeling.
• Tackling Classification problems with innovative machine learning approaches.
• Managing Data Multidimensionality and employing Linear Algebra techniques.
• Advanced Feature Engineering and Selection methodologies.
• Hyperparameter Tuning and other critical optimization strategies.

Cognitive Science Applications in Analytics

• Understanding the application of Natural Language Processing in various domains like search engines and social media.
• Exploring Machine Learning applications in areas like chatbots.
• Advanced Social Media Analytics including sentiment analysis, topic modeling, and text summarization.

Comprehensive Exploration of Text Mining and Pre-processing

• Techniques and Tools for Text Mining: Utilizing Various Tokenizers, Tokenization Methods, and Frequency Distribution Analysis
• Delving into Stemming, Part-Of-Speech (POS) Tagging, and Lemmatization
• Advanced NLP Concepts: Exploring Bigrams, Trigrams, N-grams, and Entity Recognition

Advanced Text Classification and Sentiment Analysis

• A Holistic View of Machine Learning in NLP: Exploring Words, Term Frequency, and Document Frequency
• Implementing Countvectorizer and Inverse Document Frequency for Text Conversion
• Analyzing Text Classification: Using Confusion Matrix and Naive Bayes Classifier for Sentiment Analysis

Sentence Structure and Language Modeling

• Detailed Study of Language Modeling and Sequence Tagging
• Techniques for Sequence Tasks: Predicting Sequence of Tags and Understanding Syntax Trees
• Exploring Context-Free Grammars, Chunking, and Techniques for Automatic Paraphrasing of Texts

Development of AI Chatbots and Recommendation Systems

• Practical Application of NLP Concepts: Building an AI-Powered Chatbot and a Recommendation Engine
• Integrating NLP Techniques for Enhanced User Interaction and Personalized Recommendations

Restricted Boltzmann Machine (RBM), Deep Belief Networks (DBNs), and Variational AutoEncoders

• Introductory Concepts: Understanding RBM and AutoEncoders
• Application of RBM in Deep Neural Networks and Collaborative Filtering
• Exploring the Features and Applications of AutoEncoders

Object Detection with Convolutional Neural Networks (CNN)

• Building Convolutional Neural Networks Using TensorFlow
• Components of CNN: Convolutional Layers, Dense Layers, and Pooling Layers
• Image Filtering Techniques: Utilizing CNNs for User-Specific Image Queries

Neural Style Transfer and Deep Generative Models

• Integrating Automated Conversation Bots with Generative Models
• Understanding and Implementing Sequence to Sequence Models (LSTM) for Image Generation

Distributed and Parallel Computing in Deep Learning

• Exploring the Differences Between Parallel and Distributed Computing
• Utilizing Distributed Computing in TensorFlow with tf.distribute
• Implementing Distributed Training Across Multiple CPUs and GPUs
• Introduction to Federated Learning and Parallel Computing in TensorFlow

Reinforcement Learning in Deep Neural Networks

• Analyzing the Human Mind’s Correlation with Deep Neural Networks
• Core Components of Artificial Neural Networks and Their Architecture
• In-Depth Study of Reinforcement Learning Concepts, Parameters, Layers, and Optimization Algorithms in Deep Neural Networks

Fundamentals of MLOps

• Comprehensive Introduction to MLOps: Understanding the Lifecycle and Pipeline
• In-depth Exploration of MLOps Components and Processes

Implementation of Machine Learning Models

• Getting Started with Azure Machine Learning: An Introductory Overview
• Strategies and Techniques for Deploying Machine Learning Models Using Azure Services

American Sign Language Recognition

• This case study focuses on generating and utilizing data for image recognition to identify American Sign Language. It includes training custom object detection models with pre-trained models.

Gesture Recognition

• Learn how to employ pre-trained model weights to develop a real-time gesture recognition classifier using deep learning and computer vision techniques.

Shark Species Classification

• A basic classification case study designed to provide insights into image classification using TensorFlow. It involves the use of deep learning for shark species identification.

Stock Market Forecasting

• This study addresses the use of historical data coupled with deep learning techniques, like LSTM, to forecast stock market trends and prices.

Cyber Threat Detection Using NLP

• Explore the application of Natural Language Processing and deep learning in detecting cybersecurity threats within text data.

Text Summarization Using NLP and Deep Learning

• Dive into the process of summarizing text using advanced deep learning and generative models, emphasizing the role of NLP in extracting key information.

Sentiment Analysis

• Understand the implementation of various NLP and deep learning techniques for sentiment analysis, classifying emotions or opinions expressed in text data.

Development of a Recommendation Engine

• This case study guides through the creation of a recommendation engine using deep learning, suitable for applications like movie, restaurant, or book recommendations.

Getting Started with KNIME

• Introduction to KNIME: Understanding its capabilities in data analytics and workflow creation.

Data Management in KNIME

• Learning the essentials of managing data in KNIME: Creating and executing workflows, and effective data loading techniques.

Loop Structures in KNIME

• Exploring loop mechanisms in KNIME for enhanced efficiency in data transformation processes.

Advanced Features in KNIME

• Delving into feature selection and hyperparameter optimization in KNIME for refining machine learning models.

Practical Application: KNIME Case Study

• Hands-on case study: Building comprehensive machine learning models in KNIME, utilizing algorithms such as linear regression, logistic regression, decision trees, and random forests.

The capstone project focuses on establishing a strong hold of analyzing a problem and coming up with solutions based on insights from the data. 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.
• End-to-end deep learning project lifecycle to build various applications using deep learning frameworks.
• Assessment and monitoring of the project using advanced MLOps techniques to implement feedback loops.