Executive Post Graduate Certification in AI and Machine Learning

1.1 Introduction to Linux

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

1.2 Linux Basics

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

1.3 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.

1.4 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.

1.5 Object-Oriented Programming (OOP) in Python

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

1.6 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.

2.1 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.

2.2 Utilizing Pandas for Data Handling

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

2.3 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.

2.4 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.

3.1 Introduction to SQL

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

3.2 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

3.3 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

3.4 SQL for Optimized Performance

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

3.5 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

4.1 Essentials of Descriptive Statistics

• Understanding Measures of Central Tendency and Measures of Spread
• Comprehensive Overview of the Five Point Summary and Related Concepts

4.2 Foundations of Probability

• In-depth Study of Probability Distributions
• Exploration of Bayes’ Theorem and the Central Limit Theorem

5.1 Utilizing MS Excel for Statistics and Descriptive Analytics

• Comprehensive Analysis of Central Tendency and Spread Measures
• Detailed Exploration of the Five Point Summary
• In-depth Study of Probability Distributions, including Applications in Business Analytics
• Examination of Specific Probability Distributions: Binomial, Poisson
• Understanding Bayes Theorem and the Central Limit Theorem

5.2 Applying Python for Comprehensive Statistical Analysis

• Detailed Examination of Correlation and Covariance
• Mastery in Setting and Evaluating Confidence Intervals
• Advanced Techniques in Hypothesis Testing, including F-test, Z-test, T-test
• Proficiency in Analytical Methods such as ANOVA and the Chi-Square Test

6.1 Foundations of Machine Learning

• An Overview of Machine Learning Types: Supervised and Unsupervised Learning
• Introduction to Machine Learning Tools: Scikit-Learn, Keras, and More

6.2 Regression Analysis

• Exploring the Basics of Regression: Understanding Classification vs. Regression
• Identifying Regression Problems: Recognizing Dependent and Independent Variables
• Training Techniques: How to Effectively Train Regression Models
• Evaluation Strategies: Methods for Assessing Regression Model Performance
• Optimization Approaches: Enhancing the Efficiency of Regression Models

6.3 Diving into Classification Models

• Introduction to the Concept of Classification Problems
• Identifying Key Elements of Classification: Dependent and Independent Variables
• Training Methodologies: Best Practices for Training Classification Models
• Evaluative Measures: Techniques for Assessing Classification Model Accuracy
• Model Efficiency: Strategies for Boosting Classification Model Performance

6.4 Mastering Clustering Techniques

• Unveiling Clustering Concepts: Understanding Clustering Problems
• Identifying Clustering Scenarios: Understanding Variables in Clustering
• Training Approaches: How to Effectively Train Clustering Models
• Evaluative Techniques: Assessing the Performance of Clustering Models
• Optimization Strategies: Enhancing the Effectiveness of Clustering Models

7.1 Linear Regression Models

• Building and refining linear regression models using statistical analysis, data preparation, normalization, and standardization.

7.2 Logistic Regression Applications

• Developing logistic regression models for binary classification tasks, such as predicting medical conditions or weather events.

7.3 Decision Tree Implementation

• Constructing decision tree models for classification issues, structuring data in a tree format to derive optimal solutions.

7.4 Random Forest Strategy

• Crafting random forest models to tackle classification challenges within a supervised learning framework.

7.5 Utilization of Support Vector Machines (SVM)

• Employing SVMs for both regression and classification tasks, addressing various types of data.

7.6 K-Nearest Neighbors Approach

• Implementing the K-Nearest Neighbors algorithm, a straightforward method for resolving classification problems.

7.7 Time Series Forecasting Techniques

• Leveraging time series data for insightful forecasting, utilizing specialized methods for time series analysis.

Assessing Model Performance

• Generating Classification Reports: Evaluating models based on metrics such as recall, precision, and F1-score.
• Constructing Confusion Matrices: Analysis of true positives, true negatives, false positives, and false negatives.
• Calculating Performance Metrics: Including R-squared, adjusted R-squared, mean squared error, and others for comprehensive model evaluation.

8.1 K-means Clustering

• Implementation of the K-means algorithm for sorting data into clusters in an unsupervised learning context.

8.2 Dimensionality Reduction Techniques

• Techniques for managing and simplifying multi-dimensional data, including standardization for more straightforward computational analysis.

8.3 Linear Discriminant Analysis (LDA)

• Utilizing LDA for the reduction and optimization of dimensions in multi-dimensional datasets.

8.4 Principal Component Analysis (PCA)

• Applying PCA to efficiently handle and reduce dimensions in multi-dimensional data.

9.1 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.

9.2 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.

9.3 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.

9.4 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.

10.1 Getting Started with Deep Learning

• Easy-to-Understand Introduction to Deep Learning and AI Concepts
• Exploration of Deep Learning Applications in Various Industries
• Clarifying the Differences Among Data Science, Machine Learning, Deep Learning, and Artificial Intelligence
• Life Cycle of Deep Learning Projects

10.2 Basics of Neural Networks

• Understanding the Evolution of Deep Learning from the Human Nervous System
• Introduction to Perceptron (Single Cell)
• Structure and Function of Multi-Cell Perceptron Topologies
• In-depth Look at Weights and Biases in Neural Networks
• Building a Neural Network from the Ground Up Using Numpy
• The Role and Impact of Learning Rate in Neural Networks
• Creating Neural Networks with Learning Rate Adjustments
• Implementation and Significance of Activation Functions

10.3 Neural Network Frameworks Overview

• Comparison and Contrast of Various Neural Network Frameworks
• Comprehensive Introduction to TensorFlow
• Detailed Introduction to Keras, the Official API for TensorFlow
• The Concept and Practice of Sequential Modeling

10.4 Introduction to Fully Connected Neural Networks

• Basics of Image Processing with cv2
• Creation and Understanding of Fully Connected Neural Networks (FCNN)
• Developing a Single Hidden Layer Neural Network Using Keras (MNIST Dataset)
• Insights into Neural Network Topology and Parameter Configurations

10.5 Dive into Convolutional Neural Networks (CNN)

• Introduction to Kaggle and Its Role in Machine Learning
• Understanding the Challenges of Data Flattening
• Detailed Introduction to Convolutional Neural Networks
• The Mathematics Behind Filters/Kernels in CNNs
• The Concept and Importance of Pooling, Batch Normalization, and Dropout in CNNs

10.6 Post-Modeling Activities in Neural Networks

• The Process of Augmentation in Deep Learning
• Testing and Evaluating Neural Network Models
• Techniques for Saving and Registering Models
• Strategies for Making Predictions Using Trained Models
• Training Large Image Models in Batches
• Utilizing TensorBoard for Performance Monitoring of Large Models

10.7 Learning from Existing Models

• Fundamental Principles of Transfer Learning
• Exploring the Architecture of VGG Models
• Practical Application of VGG16 for Transfer Learning
• Comparing Transfer Learning with Fine-Tuning Techniques

10.8 Exploring Recurrent Neural Networks

• Addressing the Limitations of CNNs
• Understanding the Concept of “Context” in Neural Networks
• Detailed Study of Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) Networks

10.9 Foundations of Natural Language Processing (NLP)

• Introduction to Text Processing in Deep Learning
• Overview of NLTK and Spacy for Text Analysis
• Methods for Text Pre-Processing
• Developing a Sentiment Analysis Detector Using NLP and LSTM Techniques

11.1 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

11.2 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

11.3 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

11.4 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

12.1 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

12.2 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

12.3 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

12.4 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

12.5 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

13.1 Long Short-Term Memory (LSTM) Networks

• Introduction to LSTM: Definition and Working Mechanism
• Exploring Various Applications of LSTM in Deep Learning and Beyond

13.2 Transformers in Deep Learning

• Understanding Transformers: Their Function and Role in Deep Learning
• Detailed Examination of Transformer Architectures: Encoder-Decoder Models, Self-Attention Mechanisms
• Diverse Applications and Types of Transformers in AI

13.3 Bidirectional Encoder Representations from Transformers (BERT)

• Overview of BERT as a Language Model
• Operational Insights: How BERT Functions and Its Distinction from LSTM
• Exploring the Wide Range of BERT Applications in Natural Language Processing

13.4 Generative Pre-trained Transformer (GPT) Models

• Insight into GPT: The Essence of Generative Pre-Trained Models
• Understanding the Functionality of GPT Models
• Real-World Examples and Applications of GPT in Various Domains

13.5 Large Language Models (LLM)

• Introduction to Large Language Models in NLP
• Detailed Analysis of How LLMs Operate
• Exploring the Broad Spectrum of Applications for LLMs in AI and NLP

13.6 Variational Autoencoders (VAEs)

• What are VAEs?
• What are autoencoders?
• Working of VAE 
• Applications of VAE
• VAE examples and implementation

13.7 Pre-Trained Models

• Leveraging pre-trained LLMs and Object Detection models to create text-based Generative AI applications.
• Leveraging pre-trained LLMs and Object Detection models to create image-based Generative AI applications.

13.8 Langchain

• Introduction to Langchain
• How does langchain work?
• Using Langchain to create an end-to-end application

14.1 Prompt Engineering Basics

• Introduction to Prompt Engineering
• Examples and use cases of prompt engineering
• Limitations and Applications of Prompt Engineering

14.2 Art with Generative AI

• Generative AI tools for generating images
• Leveraging the Generative AI tools to create an end-to-end application based on prompts to generate art in real time.

14.3 Audio Processing Using Generative AI

• Generative AI audio generation tools
• Building an end-to-end application for music composition in real-time.

14.4 Leveraging Generative AI for Product Design 

• Product design support in generative AI
• An application to generate an end-to-end product design in real-time.

14.5 Generative AI in Security

• Leveraging Generative AI for cybersecurity
• Creating an end-to-end application for incident reporting.

14.6 Text And Image Generation in Generative AI

• End-to-end application for Text and Image Content Generation in real-time

15.1 Non-Relational Data Stores and Azure Data Lake

• Exploration of Various Non-Relational Data Stores: Document, Columnar, Key/Value, Graph, Time Series, Object Stores, and External Indexes
• Understanding the Role and Advantages of NoSQL Databases
• Comprehensive Overview of Azure Data Lake Storage: Key Components, Data Storage Methods, and Data Lake Architecture

15.2 Data Lakes and Azure Cosmos DB

• Fundamental Concepts of Data Lakes
• In-depth Analysis of Azure Cosmos DB and its Benefits
• Overview of Azure Blob Storage and Its Importance
• Data Partitioning Strategies: Horizontal, Vertical, and Functional Partitioning
• Understanding Consistency Levels in Azure Cosmos DB

15.3 Relational Data Storage Systems

• Introduction to Relational Data Storage Solutions
• Detailed Study of Azure SQL Database: Deployment Models and Service Tiers
• Significance of SQL Database Elastic Pool

15.4 Azure SQL Database

• Exploring Azure SQL Security Features
• High-Availability Models in Azure SQL Database: Standard and Premium Models
• Azure Database Solutions for MySQL, PostgreSQL, and MariaDB
• Understanding PolyBase and its Applications
• Azure Synapse Analytics: SQL Analytics, SQL Pool, and Architecture Components

15.5 Azure Batch Processing

• Introduction to Azure Batch and its Functions
• Analysis of Workloads Suitable for Azure Batch
• Additional Features and Working Mechanism of Azure Batch

15.6 Azure Data Factory

• Workflow Process in Azure Data Factory
• Reasons for Using Azure Data Factory
• Integration Runtime and Data Mapping in Azure Data Factory

15.7 Azure Databricks

• Introduction and Benefits of Azure Databricks
• Insights into the Azure Spark-Based Analytics Platform
• Integration of Apache Spark in Azure Databricks

15.8 Azure Stream Analytics

• Functionality and Key Features of Azure Stream Analytics
• Windowing Functions in Stream Analytics: Tumbling, Hopping, Sliding, and Session Windows

16.1 Fundamentals of MLOps

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

16.2 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

17.1 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.

17.2 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.

17.3 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.

17.4 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.

17.5 Cyber Threat Detection Using NLP

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

17.6 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.

17.7 Sentiment Analysis

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

17.8 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.

18.1 Getting Started with KNIME

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

18.2 Data Management in KNIME

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

18.3 Loop Structures in KNIME

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

18.4 Advanced Features in KNIME

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

18.5 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.