Admissions Open for JANUARY Batch
Learn the foundations of AI, including machine learning, NLP, and computer vision, to build skills and plan a career in artificial intelligence
Days : Sat & Sun
Duration : 2 Days
Timings: 10 AM - 1 PM IST
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Introduction to Aritificial Intelligence
Learn the foundations of AI, including machine learning, NLP, and computer vision, to build skills and plan a career in artificial intelligence
Online Live Instructor-Led Learning
2 Days
10 AM - 1 PM
Sat & Sun
By end of this course
Get stronger in
Understanding core technologies under AI
Current Capabilities of AI systems
Understanding Career Pathways in AI
Get familier with
Important concepts like like RAG, MCP
Industry Insights and latest Advancements
How AI is transforming Industroes
New Batch Starts : jan 2026
Limited seats only
Who Should Enroll?
This course is open to anyone who wants to build a strong foundation in AI, ideal for students, IT professionals, and enthusiasts eager to explore AI concepts, career paths, and industry applications.
Prerequisites
None
Experience our course risk-free
We offer a 15-day money back guarantee
Prerequisite
None
Who Should Enroll?
This course is open to anyone who wants to build a strong foundation in AI, ideal for students, IT professionals, and enthusiasts eager to explore AI concepts, career paths, and industry applications.
By the end of this course
Get Stronger in
- Understanding core technologies under AI
- Current capabilities of AI systems and advanced techniques.
- Understanding career pathways in AI
Get Familiar in
- Important concepts like RAG & MCP
- Industry insights and latest advancements
- How AI is transforming industries
Course Contents
Phase 1: Evolution, Capabilities and Industry Insights
Phase 1: Evolution, Capabilities and Industry Insights
In this section we will explores the evolution of AI from its early foundations in the 1950s, through key breakthroughs and setbacks like AI winters, to the modern era driven by the internet, mobile devices, and widespread consumer applications. Discover how these milestones fueled AI’s rapid growth and everyday impact.
In this section, you will learn the key differences between AI and automation, and uncover common misconceptions about what each can do. Discover how automation handles repetitive tasks while AI adapts, learns, and makes decisions in complex scenarios
In this section, you will explore the full spectrum of what AI can do today, from predicting outcomes and generating content to making decisions and acting autonomously through advanced agentic AI
In this section, you will get an overview of the core AI technologies: machine learning, deep learning, natural language processing (NLP), and computer vision
In this section, you will get an overview of interdisciplinary fields: data science, data analysis, and business intelligence
In this section we will understand AI powered IDE like Copilot, Windsurf and then low code platforms like make.com, n8n and AI app builders like Lovable, Bolt
In this section, you will learn about the leading companies shaping AI today and discover their key products
In this section, you will discover how AI is transforming industries improving efficiency unlocking new possibilities across fields like Banking, healthcare, manufacturing, retail, and more
In this section, you’ll explore how MNC leaders are strategizing AI adoption and equipping their entire workforce with AI capabilities, as well as how engineers and startups are leveraging latest AI technologies to boost efficiency and speed of delivery.
In this section, you will explore diverse AI tech career paths in corporate roles, academic research, freelancing opportunities, and entrepreneurship.
In this section, you will explore non-coding career opportunities in AI, such as creative roles in generative AI, AI product management, and AI governance. Learn how you can make an impact in AI without programming skills
Explore the AI hardware landscape to understand what CPUs, GPUs, FPGAs, NPUs, TPUs, DPUs, supercomputers, and quantum computers are, discover their unique use cases, and see how each drives today’s artificial intelligence applications and innovations.
In this section we will explores the evolution of AI from its early foundations in the 1950s, through key breakthroughs and setbacks like AI winters, to the modern era driven by the internet, mobile devices, and widespread consumer applications. Discover how these milestones fueled AI’s rapid growth and everyday impact.
In this section, you will learn the key differences between AI and automation, and uncover common misconceptions about what each can do. Discover how automation handles repetitive tasks while AI adapts, learns, and makes decisions in complex scenarios
In this section, you will explore the full spectrum of what AI can do today, from predicting outcomes and generating content to making decisions and acting autonomously through advanced agentic AI
In this section, you will get an overview of the core AI technologies: machine learning, deep learning, natural language processing (NLP), and computer vision
In this section, you will get an overview of interdisciplinary fields: data science, data analysis, and business intelligence
In this section we will understand AI powered IDE like Copilot, Windsurf and then low code platforms like make.com, n8n and AI app builders like Lovable, Bolt
In this section, you will learn about the leading companies shaping AI today and discover their key products
In this section, you will discover how AI is transforming industries improving efficiency unlocking new possibilities across fields like Banking, healthcare, manufacturing, retail, and more
In this section, you’ll explore how MNC leaders are strategizing AI adoption and equipping their entire workforce with AI capabilities, as well as how engineers and startups are leveraging latest AI technologies to boost efficiency and speed of delivery.
In this section, you will explore diverse AI tech career paths in corporate roles, academic research, freelancing opportunities, and entrepreneurship.
In this section, you will explore non-coding career opportunities in AI, such as creative roles in generative AI, AI product management, and AI governance. Learn how you can make an impact in AI without programming skills
Explore the AI hardware landscape to understand what CPUs, GPUs, FPGAs, NPUs, TPUs, DPUs, supercomputers, and quantum computers are, discover their unique use cases, and see how each drives today’s artificial intelligence applications and innovations.
Phase 2: Understanding AI/ML Lifecycle
Phase 2: Understanding AI/ML Lifecycle
In this section, we will compare the traditional Software Development Life Cycle (SDLC) with the AI/ML lifecycle.
In this section, using a real-world example, we will understand all stages of the AI/ML lifecycle in building an AI system.
In this section, we will compare the traditional Software Development Life Cycle (SDLC) with the AI/ML lifecycle.
In this section, using a real-world example, we will understand all stages of the AI/ML lifecycle in building an AI system.
Phase 3: Understanding Data Engineering
Phase 3: Understanding Data Engineering
In this section, you will learn what data engineering is, along with its role and significance in building and managing data infrastructure
In this section, we will explore different types of data, including structured, unstructured, spatial, time series, and more
In this section, we will explore how different storage systems are suitable for different types of data for example, SQL for structured data, cloud or file systems for unstructured data, MongoDB for semi-structured data and more
In this section, we will explore how each of these stage of the data engineering lifecycle plays a unique role – Collection, ingestion, Storage, Transformation, Integration, Monitoring.
In this section, using a real-world example, we will break down the different stages of the data engineering lifecycle and see how each stage plays a cruicial role.
In this section, we will explore the latest advancements in data engineering,
In this section, you will learn what data engineering is, along with its role and significance in building and managing data infrastructure
In this section, we will explore different types of data, including structured, unstructured, spatial, time series, and more
In this section, we will explore how different storage systems are suitable for different types of data for example, SQL for structured data, cloud or file systems for unstructured data, MongoDB for semi-structured data and more
In this section, we will explore how each of these stage of the data engineering lifecycle plays a unique role – Collection, ingestion, Storage, Transformation, Integration, Monitoring.
In this section, using a real-world example, we will break down the different stages of the data engineering lifecycle and see how each stage plays a cruicial role.
In this section, we will explore the latest advancements in data engineering,
Phase 4: Exploring AI Data Training & Validating
Phase 4: Exploring AI Data Training & Validating
In this section, we will understand the distinct roles of AI Data Trainer, Annotator, and Validator
In this section, we will understand Data Training, Annotation, and Validation with a real world example
In this section, we will explore how bias enters AI systems, such unrepresentative training data, flawed data and more
In this section, we will cover best practices and strategies to control bias in AI systems, such as using diverse and representative datasets, applying careful data preprocessing, incorporating human oversight and more
In this section, we will cover types of Data Annotation, Text, Image, Audio, Video, 3D & more.
Each of which plays a vital role in preparing data for various machine learning and AI applications
In this section, we will explore the latest advancement in AI data training/ annotation/ validation
In this section, we will understand the distinct roles of AI Data Trainer, Annotator, and Validator
In this section, we will understand Data Training, Annotation, and Validation with a real world example
In this section, we will explore how bias enters AI systems, such unrepresentative training data, flawed data and more
In this section, we will cover best practices and strategies to control bias in AI systems, such as using diverse and representative datasets, applying careful data preprocessing, incorporating human oversight and more
In this section, we will cover types of Data Annotation, Text, Image, Audio, Video, 3D & more.
Each of which plays a vital role in preparing data for various machine learning and AI applications
In this section, we will explore the latest advancement in AI data training/ annotation/ validation
Phase 5: Exploring LLM Engineering
Phase 5: Exploring LLM Engineering
In this section, we will explore what LLM engineering is, along with its core functions and purposes, such as designing, building, fine-tuning, deploying, and maintaining large language models
In this section, we will explore prompting techniques used to guide large language models: zero-shot prompting, where the model receives only instructions, one-shot prompting, with a single example, few-shot prompting, with several examples to establish patterns, and chain-of-thought prompting, which encourages the model to provide step-by-step reasoning for more complex tasks.
In this section, we will explore the differences between core AI models, large language models (LLMs), and multimodal systems.
In this section, we will explore the key challenges in building large language models, the need for massive, high-quality datasets (data), the intense computational requirements and costs for model training (compute), and operational hurdles such as deployment, scaling, safety, and monitoring.
In this section, we will explore the role of parameters and weights in AI models, including large language models and multimodal systems. These billions of learned values are essential for generating not just text, but also images, videos, audio, and other data types, directly impacting the accuracy, efficiency, and versatility
In this section, we will explore the temperature parameter in large language models, which controls the randomness and creativity of the generated output. By adjusting temperature, users can fine-tune an LLM to produce either more predictable, factual responses or more varied, creative, and exploratory output.
In this section, we will explore hallucinations in large language models, what causes LLMs to produce inaccurate or fabricated outputs, the risks these errors pose (such as misinformation and loss of trust), and best-practice strategies for reducing hallucinations
In this section, we will explore RAG a technique where large language models (LLMs) retrieve relevant data from external sources such as databases, documents, or the web before generating answers
In this section, we will explore SLMs, commonly referred to as “Small Language Models,” and their purposes
In this section, we will explore what LLM engineering is, along with its core functions and purposes, such as designing, building, fine-tuning, deploying, and maintaining large language models
In this section, we will explore prompting techniques used to guide large language models: zero-shot prompting, where the model receives only instructions, one-shot prompting, with a single example, few-shot prompting, with several examples to establish patterns, and chain-of-thought prompting, which encourages the model to provide step-by-step reasoning for more complex tasks.
In this section, we will explore the differences between core AI models, large language models (LLMs), and multimodal systems.
In this section, we will explore the key challenges in building large language models, the need for massive, high-quality datasets (data), the intense computational requirements and costs for model training (compute), and operational hurdles such as deployment, scaling, safety, and monitoring.
In this section, we will explore the role of parameters and weights in AI models, including large language models and multimodal systems. These billions of learned values are essential for generating not just text, but also images, videos, audio, and other data types, directly impacting the accuracy, efficiency, and versatility
In this section, we will explore the temperature parameter in large language models, which controls the randomness and creativity of the generated output. By adjusting temperature, users can fine-tune an LLM to produce either more predictable, factual responses or more varied, creative, and exploratory output.
In this section, we will explore hallucinations in large language models, what causes LLMs to produce inaccurate or fabricated outputs, the risks these errors pose (such as misinformation and loss of trust), and best-practice strategies for reducing hallucinations
In this section, we will explore RAG a technique where large language models (LLMs) retrieve relevant data from external sources such as databases, documents, or the web before generating answers
In this section, we will explore SLMs, commonly referred to as “Small Language Models,” and their purposes
Phase 6: Exploring Machine Learning
Phase 6: Exploring Machine Learning
In this section, we will explore the three main types of machine learning. Supervised learning, which uses labeled data to teach models to make predictions. Unsupervised learning, which finds patterns and groups in unlabeled data, and Reinforcement learning, where it learns by interacting with environment and receiving rewards or penalties to optimize decision making
In this section, we will explore essential stages in AI model development, data preprocessing Feature Engineering, train/test split and more
In this section, we will explore key machine learning algorithms and techniques such as regression, classification, clustering and more
In this section, we will explore the main evaluation metrics and validation techniques for machine learning models including accuracy, precision, recall, F1 score and more
In this section, we will understand what data drift and model drift are, illustrated with real-world examples
In this section, we will explore what MLOps is and its core functions, such as automating and managing data preparation, monitoring model performance, and more
In this section, we will explore the three main types of machine learning. Supervised learning, which uses labeled data to teach models to make predictions. Unsupervised learning, which finds patterns and groups in unlabeled data, and Reinforcement learning, where it learns by interacting with environment and receiving rewards or penalties to optimize decision making
In this section, we will explore essential stages in AI model development, data preprocessing Feature Engineering, train/test split and more
In this section, we will explore key machine learning algorithms and techniques such as regression, classification, clustering and more
In this section, we will explore the main evaluation metrics and validation techniques for machine learning models including accuracy, precision, recall, F1 score and more
In this section, we will understand what data drift and model drift are, illustrated with real-world examples
In this section, we will explore what MLOps is and its core functions, such as automating and managing data preparation, monitoring model performance, and more
Phase 7: Exploring Agentic AI
Phase 7: Exploring Agentic AI
In this section, we will compare regular chatbots rule-based systems that provide scripted answers or follow predefined flows—with AI chatbots, which use natural language processing and machine learning to understand intent, adapt to different conversations.
In this section, we will explore AI agents autonomous systems that perceive their environment, make decisions, and perform tasks with varying levels of reasoning, adaptability, and learning.
In this section, we will explore the differences between single-agent and multi-agent systems, where a single-agent setup involves one autonomous entity handling tasks independently, while multi-agent systems use multiple agents that collaborate, coordinate to solve complex problems
In this section, we will understand the process of developing an AI agent from defining the agent’s specific goal to identifying and integrating essential tools such as scrapers for data collection, entity recognition modules for extracting information, and more so the agent can perceive, process, and act intelligently in its environment.
In this section, we will understand prompt chaining, a technique in AI where a complex task is divided into a sequence of smaller, connected prompts. Each step uses the output of the previous prompt as input for the next, enabling more accurate, controllable, and context-aware results, especially for multi-step reasoning or workflow automation.
In this section, we will understand the Model Context Protocol (MCP) an open industry standard that allows AI models to securely connect with external data sources and tools, enabling them to access and exchange real-time contextual information from enterprise systems.
In this section, we will explore the most popular and efficient code-based, no-code, and AI-powered agent builders
In this section, we will cover the latest advancements in the field of Agentic AI.
In this section, we will compare regular chatbots rule-based systems that provide scripted answers or follow predefined flows—with AI chatbots, which use natural language processing and machine learning to understand intent, adapt to different conversations.
In this section, we will explore AI agents autonomous systems that perceive their environment, make decisions, and perform tasks with varying levels of reasoning, adaptability, and learning.
In this section, we will explore the differences between single-agent and multi-agent systems, where a single-agent setup involves one autonomous entity handling tasks independently, while multi-agent systems use multiple agents that collaborate, coordinate to solve complex problems
In this section, we will understand the process of developing an AI agent from defining the agent’s specific goal to identifying and integrating essential tools such as scrapers for data collection, entity recognition modules for extracting information, and more so the agent can perceive, process, and act intelligently in its environment.
In this section, we will understand prompt chaining, a technique in AI where a complex task is divided into a sequence of smaller, connected prompts. Each step uses the output of the previous prompt as input for the next, enabling more accurate, controllable, and context-aware results, especially for multi-step reasoning or workflow automation.
In this section, we will understand the Model Context Protocol (MCP) an open industry standard that allows AI models to securely connect with external data sources and tools, enabling them to access and exchange real-time contextual information from enterprise systems.
In this section, we will explore the most popular and efficient code-based, no-code, and AI-powered agent builders
In this section, we will cover the latest advancements in the field of Agentic AI.