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AI+ Developer™

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Introduction

Course Introduction

Lab Instructions

Audio Book: Introduction AI+ Developer

Module 1: Foundations of Artificial Intelligence (AI)

Module 1: Foundations of Artificial Intelligence

Audio Book: Foundations of Artificial Intelligence

Podcast: Foundations of Artificial Intelligence (AI)

1.1 History

1.2 What is AI?

Activity: Timeline

1.3 Artificial Intelligence Based on Capabilities

1.4 Artificial Intelligence Based on Functionalities

Activity: Drag & Drop

1.5 Branches of AI: Method-Based and Application Based

1.6 Case Study 1: Healthcare

1.7 Case Study 2: Retail

1.8 Case Study 3: Finance

1.9 Case Study 4: Marketing

Activity: Flip Card

Activity: Lets Check Your Knowledge

Quiz

Tools Walkthrough - Jupyter Notebooks

Module 2: Mathematical Concepts of AI

Module 2: Mathematical Concepts for AI

Audio Book: Mathematical Concepts for AI

Podcast: Mathematical Concepts of AI

2.1 Vectors, Matrices, and Their Operations

2.2 Eigenvalues, Eigenvectors, and Linear Transformations

2.3 Determinants

Activity: Carousel

2.4 Derivatives, Partial Derivatives, and Gradients

2.5 Optimization Techniques

2.6 Integration

Activity: True or False

2.7 Probability Distributions

2.8 Hypothesis Testing

2.9 Bayesian Inference

Activity: Drag & Drop

2.10 Sets and Logic

2.11 Graph Theory

2.12 Combinatorics

Activity: Text Entry

Activity: Lets Check Your Knowledge

Quiz

Tools Walkthrough - Pandas

Module 3: Python for Developer

Module 3: Python for Developer

Audio Book: Python for Developer

Podcast: Python for Developer

3.1 Getting Started with Python: History and Setup

3.1.1 Lab: Python Fundamentals: Part 1

3.1.2 Lab: Python Fundamentals: Part 2

3.2 Programming Fundamentals: Basic Syntax

3.3 Programming Fundamentals: Control Flow

3.3.1 Lab: Understanding Python Control Flow: Mastering Else, Elif, and Nested Statements.

3.3.2 Lab:Python Loops: An Iterative Solutions

3.3.3 Lab: Unlocking Python Functions: Mastering Arguments and Return Values

3.4 Programming Fundamentals: Data Structure

3.4.1 Lab: Python Data Structures: Lists, Tuples, Dictionaries, Sets, and Nested StructuresPython Data Structures: Lists, Tuples, Dictionaries, Sets, and Nested Structures

3.5 Code Organization: Modules and Packages

3.5.1 Lab: Organizing Python Code: Mastering Modules, Packages, and Namespaces

Activity: Tab

3.6 NumPy: The Numerical Workhorse

3.7 Pandas: The Data Wrangler

3.8 Matplotlib and Seaborn

3.8.1 Lab: Harnessing the Power of Python Libraries: Numpy, Pandas, Matplotlib, and Seaborn for Data Analysis and Visualization

Activity: Quiz

Activity: Lets Check Your Knowledge

Quiz

Lab Practice 3.1

Lab Practice 3.2

Lab Practice 3.3

Lab Practice 3.4

Lab Practice 3.5

Lab Practice 3.6

Lab Practice 3.7

Python File Download (As Per The Lab Practices)

Python File Download (As Per The Lab Video)

Tools Walkthrough - NumPy

Module 4: Mastering Machine Learning

Module 4: Mastering Machine Learning

Audio Book: Mastering Machine Learning

Podcast: Mastering Machine Learning

4.1 Machine Learning: Past and Present

4.2 Scope and Types of Machine Learning

4.3 Terminologies, and Lifecycle

Activity: Hotspot

4.4 Regression and its types

4.4.1 Lab: Predicting Stock Prices Using Random Forest Regressor

4.5 Classification: Logistic Regression, Support Vector Machines, and Random Forests

4.5.1 Lab: Image Classification for Cats vs. Dogs Using Supervised Machine Learning Algorithms

4.5.2 Lab: Spam email detection

4.5.3 Lab: Sentimental Analysis

Activity: Tab

4.6 Clustering

4.6.1 Lab: Hands-on: Customer segmentation

4.6.2 Lab: Hands-on: Market Research

4.6.3 Lab: Hands-on: Anomaly Detection

4.7 Dimensionality Reduction

4.7.1 Lab: Visualizing high-dimensional data for easy analysis

Activity: True or False

4.8 Metrics and Validation for ML Models

4.8.1 Lab: Evaluating and choosing the best model for your project

Activity: Sequence

Activity: Lets Check Your Knowledge

Quiz

Lab Practice 4.1

Lab Practice 4.2

Lab Practice 4.3

Lab Practice 4.4

Lab Practice 4.5

Lab Practice 4.6

Lab Practice 4.7

Lab Practice 4.8

Lab Practice 4.9

Lab Practice 4.10

Python File Download (As Per The Lab Practices)

Python File Download (As Per The Lab Video)

Tools Walkthrough - Scikit-learn

Module 5: Deep Learning

Module 5: Deep Learning

Audio Book: Deep Learning

Podcast: Deep Learning

5.1 Building Blocks of Artificial Neural Networks

5.2 Deep Learning Frameworks and its Applications

5.2.1 Lab: Building a Simple Neural Network to Classify Handwritten Digits

5.2.2 Lab: Image Classification for a Custom Dataset using Transfer Learning with VGG16.

Activity: Carousel

5.3 CNN: Introduction, Layers, and Architecture

Activity: Hotspot

5.4 Understanding RNNs: Architecture, LSTM, and GRU

5.5 RNNs, LSTM, and GRU: Comparison, Selection, and Applications

5.5.1 Lab: Text Generation, Sentiment Analysis, and Machine Translation using a Recurrent Neural Network

5.5.2 Lab: Sentiment Analysis using LSTM

5.5.3 Lab: Machine Translation using T5 Model

Activity: True or False

Activity: Lets Check Your Knowledge

Quiz

Lab Practice 5.1

Lab Practice 5.2

Lab Practice 5.3

Lab Practice 5.4

Lab Practice 5.5

Python File Download (As Per The Lab Practices)

Python File Download (As Per The Lab Video)

Tools Walkthrough - Gradio

Module 6: Computer Vision

Module 6: Computer Vision

Audio Book: Computer Vision

Podcast: Computer Vision

6.1 Image Representation, Filtering, and Transformations

6.1.1 Lab: Image manipulation and enhancement

Activity: Drag and Drop

6.2 Object Detection: Overview, Process and Techniques

6.2.1 Lab: Implementation of YOLO for Object Detection

6.3 Region Proposal Methods

6.4 Single Shot MultiBox Detector (SSD)

6.4.1 Lab: Implementation of SSD (Single Shot MultiBox Detector) for Object Detection.

6.4.2 Lab: Object Detection using OpenCV with MobileNetV2 SSD on COCO Dataset

Activity: Carousel

6.5 Image Segmentation: Definition and Types

Activity: Text Entry

6.6 Architecture, Training, and Applications

6.6.1 Lab: Autonomous Driving with U-Net for Medical Image Segmentation

Activity: Flip the Card

Activity: Lets Check Your Knowledge

Quiz

Lab Practice 6.1

Lab Practice 6.2

Lab Practice 6.3

Lab Practice 6.4

Lab Practice 6.5

Python File Download (As Per The Lab Practices)

Python File Download (As Per The Lab Video)

Tools Walkthrough - Streamlit

Module 7: Natural Language Processing

Module 7: Natural Language Processing

Audio Book: Natural Language Processing

Podcast: Natural Language Processing

7.1 Natural Language Processing and Text Preprocessing and Representation

7.1.1 Lab: Cleaning and Preparing Text Data for NLP Tasks

Activity: Popup

7.2 Components and Challenges of Text Classification

7.3 Sentiment Analysis, Topic Modelling, and Spam Detection

7.3.1 Lab: Sentiment Analysis Using Naive Bayes Classifier

7.3.2 Lab: Building a Sentiment Analyzer for Social Media Posts

Activity: Multipe Choice Questions

7.4 Named Entity Recognition: Components, Techniques, and Applications

7.5 Identifying People, Places, Organizations, etc.

7.6 Named Entity Recognition (NER) Process

Activity: Tab

7.7 BERT and T5: Transforming Question Answering in NLP

7.8 BERT Question-Answering Systems

7.9 T5 (Text-To-Text Transfer Transformer)

7.9.1 Lab: BERT Implementation for Question-Answering

7.9.2 Lab: T5 Implementation for Text Summarization

Activity: True or False

Activity: Lets Check Your Knowledge

Quiz

Lab Practice 7.1

Lab Practice 7.2

Lab Practice 7.3

Lab Practice 7.4

Python File Download (As Per The Lab Practices)

Python File Download (As Per The Lab Video)

Tools Walkthrough - Keras

Module 8: Reinforcement Learning

Module 8: Reinforcement Learning

Audio Book: Reinforcement Learning

Podcast: Reinforcement Learning

8.1 Introduction to Reinforcement Learning

8.2 Agents, Environments, Rewards, Actions, and States

8.3 Types of Reinforcement Learning (RL)

8.4 Applications for Reinforcement Learning

8.5 Challenges for Reinforcement Learning

8.6 RL vs. Supervised Learning: A Comparison and the Future of RL

8.6.1 Lab: Building a Simple Game Environment for Reinforcement Learning Experimentation.

Activity: Sequence

8.7 Introduction to Q-Learning and Deep Q-Networks (DQNs)

8.8 Value-Based RL, Q-Tables, Function Approximation

8.8.1 Lab: Deep Q-Networks (DQNs) Implementation

8.8.2 Lab: Implementation of Q-Learning Algorithm with Epsilon-Greedy Exploration Strategy.

Activity: Quiz

8.9 Policy Gradient Method: Policy-based RL

8.10 Policy Gradient Method: Reinforce Algorithm

8.11 Policy Gradient Method: Actor-Critic Methods

Activity: Tab

Activity: Lets Check Your Knowledge

Quiz

Lab Practice 8.1

Lab Practice 8.2

Lab Practice 8.3

Python File Download (As Per The Lab Practices)

Python File Download (As Per The Lab Video)

Tools Walkthrough - spaCy

Module 9: Cloud Computing in AI Development

Module 9: Cloud Computing in AI Development

Audio Book: Cloud Computing in AI Development

Podcast: Cloud Computing in AI Development

9.1 Cloud and AI: Transforming Innovation and Scalability

9.2 Cloud Computing in AI: Definition and Importance

9.3 Key Cloud Service Models and Deployment Models

9.4 Benefits and Popular Platforms (AWS, Azure, GCP)

9.4.1 Lab: Simulating EC2 Instance Launch and Stock Market Data Analysis using AWS CLI and RandomForestClassifier.

9.5 Cloud-AI Integration: Scalability, Elasticity, and Case Studies of Success

9.6 Challenges and Solutions in Cloud Computing for AI Development

9.7 Use Cases and Applications of Cloud Computing in AI Development

9.8 Best Practices for Cloud-Based AI Development

9.9 Cloud-Based AI Security: Identity and Access Management (IAM)

9.10 Compliance Standards and Certifications in Cloud-Based AI

Activity: Accordion

9.11 Pre-trained Models

9.12 AutoML

Activity: Multipe Choice Questions

Activity: Lets Check Your Knowledge

Quiz

Lab Practice 9.1 - AWS Version

Lab Practice 9.1 - Azure version

Lab Practice 9.1 - GCP Version

Lab Practice 9.2 - AWS Version

Lab Practice 9.2 - Azure Version

Lab Practice 9.2 - GCP Version

Python File Download (As Per The Lab Video)

Tools Walkthrough - OpenCV

Module 10: Large Language Models

Module 10: Large Language Models

Audio Book: Large Language Models

Podcast: Large Language Models

10.1 Key Components and Architecture

10.2 Training LLMs

10.3 Applications for LLMs

10.4 Large Language Models (LLMs) Variants

10.5 Bias and Fairness in LLMs

10.6 Privacy & security in LLMS

10.7 Requirement Resources for LLMs

10.7.1 Lab: Generating Text Using GPT-2 Language Model

Activity: True or False

10.8 Creative Text Formats and Language Translation

10.9 Multimodal Large Language Models (LLMs)

10.9.1 Lab:Generating Text Styles and Translating Languages Using Pre-trained Models

Activity: Tab

10.10 Information Retrieval and Knowledge Base Construction

Activity: Multipe Choice Questions

Activity: Lets Check Your Knowledge

Quiz

Lab Practice 10.1

Lab Practice 10.2

Lab Practice 10.3

Python File Download (As Per The Lab Practices)

Python File Download (As Per The Lab Video)

Tools Walkthrough - MLflow

Module 11: Cutting-Edge AI Research

Module 11: Cutting-Edge AI Research

Audio Book: Cutting-Edge AI Research

Podcast: Cutting-Edge AI Research

11.1 Fundamentals and Integration of Neuro-Symbolic AI

11.2 Explainable AI (XAI)

11.3 Interpreting AI Models and Building Trust

Activity: Flip a Card

11.4 Federated Learning: Introduction and Privacy-Preserving Collaboration

11.4.1 Lab: Federated Learning with Hybrid Symbolic-Neural Models

11.5 Meta-Learning

11.6 Few Shot Learning

11.6.1 Lab: Implementing and Evaluating a Few-Shot Learning Model using a Convolutional Neural Network (CNN)

Activity: Text Entry

Activity: Lets Check Your Knowledge

Quiz

Python File Download (As Per The Lab Video)

Module 12: AI Communication and Documentation

Module 12: AI Communication and Documentation

Audio Book: AI Communication and Documentation

Podcast: AI Communication and Documentation

12.1 Presenting to Technical and Non-Technical Audiences

Activity: Flip a Card

12.2 Introduction to Documenting AI Systems

12.3 Code Documentation

12.4 Model Explanations

Activity: Tab

12.5 Bias, Fairness, Transparency, and Accountability

Activity: Problem Statement

Activity: Lets Check Your Knowledge

Quiz

Additional Module - AI Agents For Developer

AI Agent Developer

1.1 AI Agents: Revolutionizing Software Development

1.2 AI Agents for Developers: How They Work, Applications, and Emerging Trends

1.3 Core Characteristics and Importance of AI Agents

1.4 Types of AI Agents

1.5 AI Agents in Developer Tools

1.6 Case Study: Enhancing Developer Productivity with GitHub Copilot

Graded Assessment- 5 Questions

Resources

AI+ Developer Blueprint

AI+ Developer Detailed Curriculum

AI+ Developer Tools

AI+ Developer Reference Videos and Links

AI CERTs Exam Guidelines

AI CERTs Exam Guidelines

AI+ Developer Examination

AI+ Developer Examination

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