The Ultimate Step-by-Step AI Roadmap & Gen AI Roadmap

8 min readJun 9, 2024

Introduction

Artificial Intelligence (AI) has become a game-changer in today’s world. The rapid advancements in AI technology have opened up new opportunities and possibilities for individuals working in the tech field. Whether you have a background in machine learning, mathematics, statistics, or programming, it is now easier than ever to dive into the world of AI and start building with it.

This blog will provide you with a comprehensive roadmap to expand your AI skill set, learn the fundamentals of AI, and explore how to build with AI. Let’s get started!

Why Learn AI?

AI, machine learning, and deep learning have been around since the 1950s. However, the AI we talk about today, generative AI, is a subset of machine learning and deep learning. Generative AI has revolutionized various industries by enabling machines to write code, generate images, create music, diagnose rare conditions, and much more.

With companies looking to implement AI solutions to solve specific problems, there is a huge demand for people who have a deep understanding of AI and can build reliable and scalable AI tools. By learning AI, you can create a significant impact and contribute to solving real-world problems. Additionally, having knowledge of AI can help you avoid misconceptions and misinformation surrounding this powerful technology.

Check- 8 Best Coursera Courses for Artificial Intelligence

Getting Started with AI

When it comes to learning AI, there is no one-size-fits-all roadmap. However, there are some essential steps you can take to kickstart your AI journey.

1. Learn the Basics of Programming

Python is one of the most widely used programming languages for AI, machine learning, and deep learning. It is essential to have some coding knowledge in Python to work with AI effectively. Start by familiarizing yourself with the basics of Python, such as:

Data types
Data structures
Conditionals
Loops
Functions
Object-oriented programming

Additionally, explore essential Python libraries like NumPy for numerical computing, pandas for data wrangling, and matplotlib for data visualization. Python’s extensive open-source library ecosystem allows you to develop almost anything you want.

FREE Python Programming Course-

Introduction to Python Programming(Udacity Free Course)

2. Learn Git Version Control

Git is an open-source software for tracking changes in your projects and managing project versions. Learning Git version control is essential, especially when collaborating with others on complex projects. Git is often confused with GitHub, which is a hosting platform for Git repositories. You can use GitHub to share your projects with others and contribute to open-source projects. To get started with Git, you can install GitHub Desktop or use terminal commands for interacting with Git.

3. Master the Art of Using APIs

APIs (Application Programming Interfaces) are a way for computer programs to communicate with each other. Knowing how to use APIs opens up a world of possibilities for developing AI applications. You can make API requests to fetch data or perform model predictions. Understanding APIs will allow you to develop your own AI tools, integrate AI models into existing systems, and create innovative solutions. Explore different APIs and experiment with them to enhance your AI skills.

Theoretical Fundamentals of AI

To have a strong foundation in AI, it is essential to dive into the theoretical aspects of AI. Here are some key areas you should focus on:

1. Machine Learning

Machine learning algorithms can be categorized into supervised learning and unsupervised learning:

Supervised Learning: Involves training models with labeled data.
Unsupervised Learning: Deals with unlabeled data.

It is worth understanding the basic concepts and algorithms of machine learning, although AI today heavily relies on deep learning.

Machine Learning FREE Courses-

Machine Learning by Georgia Tech((Udacity Free Course)
Introduction to Machine Learning Course(Udacity Free Course)

2. Deep Learning

Deep learning is a subset of machine learning that involves neural networks. Neural networks excel at processing unstructured data, such as text and images. To grasp deep learning, familiarize yourself with concepts like:

Forward propagation
Backpropagation
Gradient descent
Weight updating

While understanding the underlying mathematics is beneficial, it is not necessary to dive too deep into the math to get started with deep learning. Instead, focus on practical implementations and real-world projects.

Deep Learning Courses-

Deep Learning Specialization (deeplearning.ai)
Deep Learning– Udacity

3. Computer Vision and Reinforcement Learning

Depending on your interests, computer vision and reinforcement learning are subfields of AI worth exploring:

Computer Vision: Deals with teaching machines to understand and interpret visual data.
Reinforcement Learning: Focuses on training AI agents to make decisions and take actions based on rewards and penalties.

These areas offer exciting opportunities for developing advanced AI applications.

FREE Computer Vision Courses-

Introduction to Computer Vision– Udacity
Computer Vision Basics– Coursera

Putting Theory into Practice

Learning AI is not just about understanding the theories; it’s about applying them in real-world scenarios. Here are some ways you can put your AI knowledge into action:

1. Build Small Projects

Start by building small AI projects to get hands-on experience and familiarize yourself with different AI tools and frameworks. You can:

Implement basic neural networks
Develop chatbots
Work on data analysis projects using Python and relevant libraries like TensorFlow or Keras

Building small projects will help you connect the dots and reinforce your understanding of AI concepts.

2. Document Your Projects

Documentation is essential for reference and knowledge sharing. Keep track of your AI projects and document them for your future reference. Sharing your projects, through blog posts or social media, can also help others learn and benefit from your experiences.

Also, Read- Artificial Intelligence Learning Roadmap [AI Roadmap]

3. Explore Specializations

As you progress in your AI journey, you might find specific areas that interest you. Dive deeper into those areas and develop specialization. Read books, research papers, articles, and watch videos to gain a more comprehensive understanding of AI topics beyond mainstream media coverage. Explore topics like:

Advanced prompt engineering methods
AI security
AI safety research
AI regulations

Broaden your knowledge and contribute to the AI community.

Resources for Learning AI

Intro to Artificial Intelligence– Udacity FREE Course
AI For Everyone– Coursera FREE to Audit Course
AI Foundations for Everyone Specialization– Coursera
AI Programming with Python– Udacity

Generative AI Roadmap

1. Deep Learning and Generative AI

Deep Learning uses artificial neural networks to process complex patterns. Deep learning models have multiple layers of interconnected neurons, allowing them to learn more complex patterns than traditional machine learning models. Generative AI is a subset of deep learning that focuses on creating new content based on existing data. It uses artificial neural networks to generate:

Text
Imagery
Audio
Synthetic data

Generative AI models can process both labeled and unlabeled data using supervised, unsupervised, and semi-supervised methods.

2. Generative AI Models

Generative AI models can be divided into two types: generative and discriminative:

Discriminative Models: Used to classify or predict labels for data points based on the relationship between the features of the data points and the labels.
Generative Models: Generate new data instances based on a learned probability distribution of existing data.

Generative models are capable of generating new content, such as text, images, audio, and video. They learn patterns from training data and can generate new samples that are similar to the data they were trained on.

3. Transformers and Generative AI

Transformers are a type of model architecture that revolutionized natural language processing in 2018. They consist of an encoder and decoder:

Encoder: Processes the input sequence and passes it to the decoder.
Decoder: Learns how to decode the representation for a relevant task.

Generative AI models, particularly those based on transformers, can sometimes produce hallucinations or nonsensical output. This can be caused by factors such as insufficient training data, noisy data, lack of context, or lack of constraints. Prompt design, which involves creating a specific input to control the output of a generative model, can help mitigate this issue.

4. Generative AI Applications

Generative AI has a wide range of applications across various industries:

Text-to-Text Models: Can be used for translation or other text-based tasks.
Text-to-Image and Text-to-Video Models: Generate visual content based on text input.
Text-to-Task Models: Trained to perform specific tasks based on text input, such as answering questions or making predictions.
Foundation Models: Large AI models pre-trained on vast amounts of data, fine-tuned for specific downstream tasks.

Generative AI has the potential to revolutionize industries such as healthcare, finance, and customer service by:

Detecting fraud
Providing personalized support
Enhancing creative processes

5. Generative AI Tools

Several tools and resources are available for developing and deploying generative AI models:

Google Cloud’s Generative AI Studio: Offers pre-trained models, fine-tuning tools, and deployment options.
Generative AI App Builder: Allows users to create generative AI apps without coding, using a visual editor and conversational AI engine.
PaLM API: Provides access to large language models and generative AI tools for prototyping and experimentation.

Generative AI Online Courses and Tutorials

There are many online classes and tutorials that can help you learn more about AI.

Introduction to Generative Adversarial Networks– Udacity
Generative Adversarial Networks (GANs) Specialization– Coursera
Generative Deep Learning with TensorFlow– Coursera
Deep Learning– Udacity
Introduction to Generative AI with Google Cloud– Udacity FREE Course
AWS Machine Learning Foundations Course– Udacity FREE Course
Master Generative AI: Automate Content Effortlessly with AI– Udemy
Deep Generative Models– Udemy

Roadmap to Learn Generative AI in 2024

Prerequisites

Python Programming Language (1 Month)

Complete Python
Flask
Fast API Tutorials

Basic Machine Learning & Natural Language Processing (5 Days)

Why NLP?
One-hot Encoding, Bag of Words
TF-IDF
Word2Vec, AvgWord2Vec

Basic Deep Learning Concepts (5 Days)

ANN: Working of Multi-Layered Neural Network
Forward Propagation, Backward Propagation
Activation Functions, Loss Functions
Optimizers

Advanced NLP Concepts (6+ Days)

RNN, LSTM RNN
GRU RNN
Bidirectional LSTM RNN
Encoder-Decoder, Attention Mechanisms, Seq2Seq
Transformers

Starting the Journey Towards Generative AI

OpenAI: Explore and use models like GPT-4.
Langchain Tutorials with Projects: Practical projects to deepen understanding.
Chainlit, Google Gemini: Other tools and platforms to explore.

Vector Databases

Pinecone: Basic concepts, Python API, and production deployments.
Qdrant: Concepts, Python API, Fast API.
Weaviate: Concepts, Python API, Fast API.

Langchain Framework

Langchain Python API: Concepts, Document Loaders, Text Splitters, Language Models, Chains, Memory, Agents.

Huggingface Library

Huggingface Basics: Python API, Text Classification, Sentence Pair Classification, Named Entity Recognition, Question Answering.
Huggingface for LLM: Model Loading, Model Fine-Tuning.

Tools and Frameworks

Haystack: A framework for building search systems.
Exploring Langchain UI & Components: Understanding the UI and components of Langchain.
Building LLM Projects with Langchain: Practical project implementation using Langchain.

Also, Read- How to Learn Generative AI From Scratch? [Step-by-Step]

Conclusion

The journey to learning AI, particularly generative AI, requires dedication and a structured approach. By following this roadmap, you can build a strong foundation in AI and gradually delve into more advanced topics. Remember, the key is to start small, build projects, document your progress, and continuously explore new areas of AI. With persistence and curiosity, you can master AI and contribute to innovative solutions in the tech world.

Happy learning!

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