AI Training

for developers

instructor-led live AI training courses through interactive hands-on practice

Online or onsite

Course Catalogue

Dive into Predictive AI

Predictive AI is the art and science of forecasting future events using data.

Aimed at beginner-level Data Science / IT professionals who wish to grasp the fundamentals of Predictive AI.

Python Prog Fundamentals

It’s particularly useful for machine learning, data Science & analysis

Aimed at beginner-level developers and data analysts who wish to learn Python prog from scratch using Google Colab

Explore GenAI & LLMs

Generative AI & Large Language Models (LLMs) are latest tech transforming every profession

GenAI & LLMs have achieved impressive results on various natural language tasks such as text summarization & generation, q & a and more.

Prompt Engineering for AI

AI text and image generation, enabling the creation of hyper-realistic content

Harness the power of prompts to generate impressive and realistic text and images.

Machine Learning with Python

Get ready to dive into the world of Machine Learning (ML) by using Python!

This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning.

AI driven software development

Leverage AI for the software development lifecycle using various tools and algorithms

Leverage AI-powered tools and algorithms to improve the efficiency of software development processes.

AI driven Product Development

AI harnessed in product development to enhance ideation, user research, prototyping, and customer support.

Aimed at product development professionals who wish to leverage AI to streamline their workflows, improve collaboration, and create better products.

AIASE

AI-Augmented Software Engg (AIASE) is the application to enhance and automate tasks within the software engineering process.

Understand the role of AI and machine learning in automating software development tasks. Implement AI tools to generate code, tests, and documentation.

AI TRiSM

Introduction to AI Trust, Risk, and Security Management (AI TRiSM)

Grasp the key concepts and importance of AI TRiSM as an emerging field that addresses the need for trustworthiness, risk management, and security in AI systems.

Dive into Predictive AI World

Overview

Course Outline

Requirements

Predictive AI is the art and science of forecasting future events using data.

This instructor-led, live training (online or onsite) is aimed at beginner-level IT professionals who wish to grasp the fundamentals of Predictive AI.

By the end of this training, participants will be able to:

Understand the core concepts of Predictive AI and its applications.
Collect, clean, and preprocess data for predictive analysis.
Explore and visualize data to uncover insights.
Build basic statistical models to make predictions.
Evaluate the performance of predictive models.
Apply Predictive AI concepts to real-world scenarios.

Format of the Course

Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.

DURATION

Duration of the workshop is 2 DAYS

Introduction

Defining Predictive AI
Historical context and evolution of predictive analytics
Basic principles of machine learning and data mining

Data Collection and Preprocessing

Gathering relevant data
Cleaning and preparing data for analysis
Understanding data types and sources

Exploratory Data Analysis (EDA)

Visualizing data for insights
Descriptive statistics and data summarization
Identifying patterns and relationships in data

Statistical Modeling

Basics of statistical inference
Regression analysis
Classification models

Machine Learning Algorithms for Prediction

Overview of supervised learning algorithms
Decision trees and random forests
Neural networks and deep learning basics

Model Evaluation and Selection

Understanding model accuracy and performance metrics
Cross-validation techniques
Overfitting and model tuning

Practical Applications of Predictive AI

Case studies across various industries
Ethical considerations in predictive modeling
Limitations and challenges of Predictive AI

Hands-On Project

Working with a dataset to create a predictive model
Applying the model to make predictions
Evaluating and interpreting the results

Summary and Next Steps

An understanding of basic statistics
Experience with any programming language
Familiarity with data handling and spreadsheets
No prior experience in AI or data science required

Participants

IT professionals
Data analysts
Technical staff

Python Prog Fundamentals

Overview

Course Outline

Requirements

Python is a versatile and widely-used programming language. Google Colab is an interactive cloud-based platform that allows users to write and execute Python code through their browser. It’s particularly useful for machine learning, data analysis, and education.

This instructor-led, live training (online or onsite) is aimed at beginner-level developers and data analysts who wish to learn Python programming from scratch using Google Colab.

By the end of this training, participants will be able to:

Understand the basics of Python programming language.
Implement Python code in Google Colab environment.
Utilize control structures to manage the flow of a Python program.
Create functions to organize and reuse code effectively.
Explore and use basic libraries for Python programming.

Format of the Course

Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.

DURATION

Duration of the workshop is 2 DAYS

Course Outline

Introduction to Python and Google Colab

Setting up Google Colab
Understanding the Python programming environment
Writing and executing your first Python script

Variables and Data Types

Introduction to variables
Different data types in Python
Operations on numbers and strings

Control Structures

Conditional statements
Loops: for and while
Controlling program flow with decisions

Functions and Modules

Defining and calling functions
Scope and lifetime of variables
Importing and using modules

Working with Collections

Lists and tuples
Dictionaries and sets
Iterating through collections

Basic Libraries in Python

Introduction to libraries like NumPy and Matplotlib
Basic data manipulation with Pandas
Simple data visualization

Final Project

Applying learned concepts to a small project
Best practices for writing and organizing Python code
Debugging and troubleshooting

Summary and Next Steps

Familiarity with web browsing and simple mathematical concepts
Experience with any programming language
Familiarity with data handling and spreadsheets
No prior experience in AI or data science required

Participants

IT professionals
Data analysts
Technical staff

Explore Generative AI & LLMs

Overview

Course Outline

Requirements

Generative AI is a subset of artificial intelligence focused on creating new content, whether it be text, images, or other data types, that is similar to but not identical to the content it has learned from.

Large Language Models (LLMs) are deep neural network models that can generate natural language texts based on a given input or context. They are trained on large amounts of text data from various domains and sources, and they can capture the syntactic and semantic patterns of natural language. LLMs have achieved impressive results on various natural language tasks such as text summarization, question answering, text generation, and more.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.

By the end of this training, participants will be able to:

Set up a development environment that includes a popular LLM.
Create a basic LLM and fine-tune it on a custom dataset.
Use LLMs for different natural language tasks such as text summarization, question answering, text generation, and more.
Debug and evaluate LLMs using various tools and Datasets.

Format of the Course

Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.

DURATION

Duration of the workshop is 2 DAYS

Course Outline

Introduction to Generative AI

What is Generative AI?
History and evolution of Generative AI
Key concepts and terminology
Overview of applications and potential of Generative AI

Creating Text with Generative AI

Generating text from text prompts
Using transformer-based models to create text with context and coherence
Using text summarization to create concise summaries of long texts
Using text paraphrasing to create different ways of expressing the same meaning

Introduction to LLMs

What are Large Language Models (LLMs)?
LLMs vs traditional NLP models
Overview of LLMs features and architecture
Challenges and limitations of LLMs

Understanding LLMs

The lifecycle of an LLM
How LLMs work
The main components of an LLM: encoder, decoder, attention, embeddings, etc.

Getting Started

Setting up the Development Environment
Installing an LLM as a development tool, e.g. Google Colab, Hugging Face

Working with LLMs

Exploring available LLM options
Creating and using an LLM
Fine-tuning an LLM on a custom dataset

Text Summarization

Understanding the task of text summarization and its applications
Using an LLM for extractive and abstractive text summarization
Evaluating the quality of the generated summaries using metrics such as ROUGE, BLEU, etc.

Question Answering

Understanding the task of question answering and its applications
Using an LLM for open-domain and closed-domain question answering
Evaluating the accuracy of the generated answers using metrics such as F1, EM, etc.

Text Generation

Understanding the task of text generation and its applications
Using an LLM for conditional and unconditional text generation
Controlling the style, tone, and content of the generated texts using parameters such as temperature, top-k, top-p, etc.

Integrating LLMs with Other Frameworks and Platforms

Using LLMs with PyTorch or TensorFlow
Using LLMs with Flask or Streamlit
Using LLMs with Google Cloud or AWS

Troubleshooting

Understanding the common errors and bugs in LLMs
Using TensorBoard to monitor and visualize the training process
Using PyTorch Lightning to simplify the training code and improve the performance
Using Hugging Face Datasets to load and preprocess the data

Summary and Next Steps

An understanding of natural language processing and deep learning
Experience with Python and PyTorch or TensorFlow
Basic programming experience

Participants

Developers
NLP enthusiasts
Data scientists

Prompt Engg for AI

Overview

Course Outline

Requirements

Prompt engineering is a vital skill in AI text and image generation, enabling the creation of hyper-realistic content.

This instructor-led, live training (online or onsite) is aimed at AI practitioners and enthusiasts who wish to harness the power of prompts to generate impressive and realistic text and images.

By the end of this training, participants will be able to:

Have a solid understanding of prompt engineering concepts.
Write accurate and effective prompts for ChatGPT, Google Gemini, Anthropic Claude and many more
Generate hyper-realistic text and images using the latest tools and techniques in prompt engineering.
Use AI-powered prompt engineering tools to automate prompt generation.
Apply prompt engineering to various use cases.
Incorporate prompt engineering into their own projects and workflows.

Format of the Course

Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.

DURATION

Duration of the workshop is 1 DAY

Course Outline

Introduction

What is Prompt Engineering?
Importance and applications of prompt engineering
Overview of tools and platforms

Foundations of AI Text and Image Generation

Understanding AI models for text and image generation
Training data and fine-tuning models
Exploring different architectures and their capabilities

Advanced Prompt Engineering Techniques

Crafting effective prompts for realistic text and image generation
Leveraging conditional and context-aware prompts
Designing prompts for style transfer and creativity

Exploring Advanced AI Tools and Techniques

Deep dive into ChatGPT, Stable Diffusion, DALL-E 2, Leonardo AI, and MidJourney
Experimenting with pre-trained models and custom datasets
Image-to-image translation and manipulation techniques

Hyper-Realistic Text Generation

Generating text with desired attributes using prompts
Fine-tuning text generation models for specific contexts
Controlling text generation for coherent and diverse outputs

Hyper-Realistic Image Generation

Generating highly realistic images using prompt engineering
Fine-tuning image generation models for specific styles
Manipulating image attributes through prompts

Case Studies and Real-World Applications

Examining successful use cases of prompt engineering
Applying prompt engineering to various domains (e.g., art, design, advertising)
Ethical considerations and responsible use of prompt engineering

Integrating Prompt Engineering into Workflows

Strategies for incorporating prompt engineering into projects
Optimizing performance and efficiency
Best practices for prompt engineering in real-world scenarios

Future Developments and Latest Trends in Prompt Engineering

Staying up-to-date with advancements in prompt engineering
Emerging tools, models, and techniques
Forecasting the future of AI text and image generation

Summary and Next Steps

No programming expertise required

Participants

Beginners in prompt engineering
Experienced AI practitioners
AI enthusiasts interested in hyper-realistic text and image generation

Machine Learning with Python

Overview

Course Outline

Requirements

Machine Learning with Python

The aim of this course is to provide general proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.

Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.

DURATION

Duration of the workshop is 2 DAYS

Course Outline

Introduction to Applied Machine Learning

Statistical learning vs. Machine learning
Iteration and evaluation
Bias-Variance trade-off

Supervised Learning and Unsupervised Learning

Machine Learning Languages, Types, and Examples
Supervised vs Unsupervised Learning

Supervised Learning

Decision Trees
Random Forests
Model Evaluation

Machine Learning with Python

Choice of libraries
Add-on tools

Regression

Linear regression
Generalizations and Nonlinearity
Exercises

Classification

Bayesian refresher
Naive Bayes
Logistic regression
K-Nearest neighbors
Exercises

Cross-validation and Resampling

Cross-validation approaches
Bootstrap
Exercises

Unsupervised Learning

K-means clustering
Examples
Challenges of unsupervised learning and beyond K-means

Neural networks

Layers and nodes
Python neural network libraries
Working with scikit-learn
Working with PyBrain
Deep Learning

Knowledge of Python programming language. Basic familiarity with statistics and linear algebra is recommended.

Participants

Experienced IT professionals with Python
Prospective Data Scientists

Software Development using Generative AI

Overiew

Course Outline

Requirements

Generative AI is transforming the field of Software Engineering, making it a crucial skills for Developers to have in their toolkit. This course is designed to provide you with a comprehensive understanding of how generative AI techniques can be applied to enhance software development processes.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level software developers who wish to integrate generative AI into their software development lifecycle (SDLC).

By the end of this training, participants will be able to:

Understand the role and capabilities of generative AI in software development.
Utilize AI tools for coding, debugging, and code reviews.
Apply AI techniques for efficient root cause analysis.
Implement AI features to enhance the software development process.
Evaluate ethical considerations and best practices for AI in development.

Format of the Course

Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.

DURATION

Duration of the workshop is 2 DAYS

Course Outline

Introduction to Generative AI in Software Development

Understanding Generative AI
Leveraging Generative AI in Software Development Lifecycle
Overview of AI-driven development tools

AI-Assisted Coding

Predictive coding with AI
Code generation and autocompletion tools
Enhancing code quality with AI insights

Debugging with AI

Automated error detection
AI in static code analysis
Dynamic analysis with AI support

AI in Code Reviews

Automating code review processes
AI for code optimization suggestions
Ensuring code standards with AI

Root Cause Analysis with AI

Data-driven approach to problem-solving
AI algorithms for identifying issues
Predictive analytics for preventing future errors

Case Studies

Real-world examples of AI in the SDLC
Success stories and lessons learned
Future trends in AI for software development

Hands-On Workshops

Interactive sessions with AI coding tools
Group projects on AI-assisted debugging
Peer reviews using AI-generated insights

Ethical Considerations and Best Practices

Ethical use of AI in software development
Best practices for integrating AI into the SDLC
Balancing human expertise with AI capabilities

Assignments

Hands-On Lab: Generate Database Design with ChatGPT
Hands-on Lab: Get a Solution to the given Coding Problem
Hands-on Lab: Generate Test-cases for Specific Use-case using Generative AI
Hands-on Lab: Software Documentation Using Generative AI
Hands-on Lab: Code Translation Using Generative AI
Hands-on Lab: Review Code with Generative AI

Plugins

Popular Tools of Generative AI for Software Development
Tokens in Generative AI
Hands-on Lab: OpenAI Account Setup and API Key Generation
Managing Legacy Code with Gen AI
Useful prompts for software design and development
Setting up your own AI development environment with Gen AI tools

An understanding of basic software development concepts
Experience with any programming language
Familiarity with software development tools and environments

Participants

Experienced IT professionals
Software developers
Technical team leads
Product managers

AI driven Product Development

Overview

Course Outline

Requirements

Generative AI tools can be harnessed in product development to enhance ideation, user research, prototyping, and customer support.

This instructor-led, live training (online or onsite) is aimed at product development professionals who wish to leverage Generative AI to streamline their workflows, improve collaboration, and create better products.

By the end of this training, participants will be able to:

Understand the fundamentals of Generative AI and its applications in product development.
Use Generative AI to generate innovative product ideas and conduct user research.
Implement Generative AI in prototyping and iterative design processes.
Enhance customer support and user engagement with Generative AI.

Format of the Course

Interactive lectures and discussions.
Hands-on exercises and demonstrations.
Collaborative group projects.
Practical implementation in a live-lab environment.

DURATION

Duration of the workshop is 2 DAYS

Course Outline

Introduction

What is Generative AI tools and its relevance in product development?
Overview of Generative AI tools for product development

Ideation and User Research

Generating creative product ideas with Generative AI tools
Conducting user research and feedback collection with Generative AI tools

Prototyping and Iterative Design

Integrating Generative AI tools in prototyping tools and workflows
Iterative design improvement using Generative AI tools-generated insights

Customer Support and User Engagement

Leveraging Generative AI tools for personalized customer support
Enhancing user engagement through interactive chat interfaces

Collaboration and Teamwork

Facilitating collaboration and knowledge sharing with Generative AI tools
Using Generative AI tools as a virtual assistant for project management and communication

Best Practices and Ethical Considerations

Effective strategies for utilizing Generative AI tools in product development
Ethical considerations and responsible use of Generative AI tools

Future Trends and Developments

Emerging trends in AI and product development
Opportunities and challenges for Generative AI tools in the product development landscape

Basic computer experience
Familiarity with product development concepts and processes

Participants

Product managers
Product designers
Software engineers
UI/UX designers

AI-Augmented Software Engineering (AIASE)

Overview

Course Outline

Requirements

AI-Augmented Software Engineering (AIASE) is the application of artificial intelligence to enhance and automate tasks within the software engineering process.

This instructor-led, live training (online or onsite) is aimed at intermediate-level software professionals who wish to leverage AI and machine learning to improve efficiency and innovation in software development.

By the end of this training, participants will be able to:

Understand the role of AI and machine learning in automating software development tasks.
Implement AI tools to generate code, tests, and documentation.
Apply AI techniques for code optimization, quality assurance, and debugging.
Address ethical considerations and challenges in AI-augmented software engineering.

Format of the Course

Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.

DURATION

Duration of the workshop is 2 DAYS

Course Outline

Introduction to AIASE

Overview of AI in software engineering
History and evolution of AIASE
Key concepts and terminology

AI Technologies in Software Development

Machine learning basics
Natural language processing (NLP) for code
Neural networks and deep learning models

Automating Software Development with AI

AI tools for generating boilerplate code
Automated code refactoring and optimization
Functional and unit test code generation
AI-assisted test case design and optimization

Enhancing Code Quality with AI

AI for bug detection and code reviews
Predictive analytics for software maintenance
AI-powered static and dynamic analysis tools
Automated debugging techniques
AI-driven fault localization and repair

AI for Documentation and Knowledge Management

Automated generation of docstrings and documentation
Knowledge extraction from codebases
AI for code search and reuse

Ethical Considerations and Challenges

Bias and fairness in AI tools
Intellectual property and licensing issues
Future of AI in software engineering

Hands-On Projects and Case Studies

Working with popular AI tools in software engineering
Case studies of AIASE in industry
Capstone project: Developing an AI-augmented software application

An understanding of software development processes and methodologies
Experience with programming
Basic knowledge of machine learning concepts

Participants

Software developers
Software engineers
Technical leads and managers

AI TRiSM

Overview

Course Outline

Requirements

AI TRiSM is an emerging field that addresses the need for trustworthiness, risk management, and security in AI systems.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level IT professionals who wish to understand and implement AI TRiSM in their organizations.

By the end of this training, participants will be able to:

Grasp the key concepts and importance of AI trust, risk, and security management.
Identify and mitigate risks associated with AI systems.
Implement security best practices for AI.
Understand regulatory compliance and ethical considerations for AI.
Develop strategies for effective AI governance and management.

Format of the Course

Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.

DURATION

Duration of the workshop is 1 DAY

Course Outline

Understanding AI TRiSM

Introduction to AI TRiSM
The importance of trust and security in AI
Overview of AI risks and challenges

Foundations of Trustworthy AI

Principles of AI trustworthiness
Ensuring fairness, reliability, and robustness in AI systems
AI ethics and governance

Risk Management in AI

Identifying and assessing AI risks
Mitigation strategies for AI-related risks
AI risk management frameworks

Security Aspects of AI

AI and cybersecurity
Protecting AI systems from attacks
Secure AI development lifecycle

Compliance and Data Protection

Regulatory landscape for AI
AI compliance with data privacy laws
Data encryption and secure storage in AI systems

AI Model Governance

Governance structures for AI
Monitoring and auditing AI models
Transparency and explainability in AI

Implementing AI TRiSM

Best practices for implementing AI TRiSM
Case studies and real-world examples
Tools and technologies for AI TRiSM

Future of AI TRiSM

Emerging trends in AI TRiSM
Preparing for the future of AI in business
Continuous learning and adaptation in AI TRiSM

An understanding of basic AI concepts and applications
Experience with data management and IT security principles is beneficial

Participants

IT professionals and managers
Data scientists and AI developers
Business leaders and policymakers