AI Preparation Roadmap

Overall Learning Duration:

• Total Duration: Approximately 6-8 months (given your pace of 30 hours per week).
  • Foundations (2 months)
  • Machine Learning (2-3 months)
  • Deep Learning & Neural Networks (2-3 months)
  • Advanced Topics (Reinforcement Learning, NLP, GANs, etc.) (1-2 months)

1. Foundations (2 Months) – Building the Basics

This section focuses on understanding the necessary mathematics, programming, and algorithms that form the bedrock of AI.

1.1 Mathematics for AI

Mathematics is crucial for understanding how models work at a deep level. Focus on the following topics:

• Linear Algebra
• Calculus
• Probability and Statistics

Resources:

• Book: “Linear Algebra Done Right” by Sheldon Axler
• Book: “Calculus: Early Transcendentals” by James Stewart
• Course: Mathematics for Machine Learning (Coursera, by Imperial College London)
• YouTube: Khan Academy

1.2 Programming (Python)

Python is the language of choice for AI. You’ll need to be proficient in Python and libraries like Numpy, Pandas, and Matplotlib.

Resources:

• Book: “Python Crash Course” by Eric Matthes
• Course: Python for Everybody (Coursera, University of Michigan)
• Practice: LeetCode for coding practice

1.3 Data Structures & Algorithms

Knowing algorithms will help you understand the internal workings of AI models, optimization, and problem-solving.

Resources:

• Book: “Introduction to Algorithms” by Cormen, Leiserson, Rivest, Stein (commonly known as CLRS)
• Course: Algorithms Specialization (Coursera, Stanford University)

2. Machine Learning (2-3 Months) – The Core of AI

Here, you’ll dive into supervised and unsupervised learning, understanding how different models are built and how to train them.

2.1 Core Concepts of Machine Learning

Focus on algorithms like Linear Regression, Logistic Regression, Decision Trees, SVM, and clustering methods. You’ll learn to train models, validate them, and understand overfitting, bias, variance trade-off, etc.

Resources:

• Book: “Pattern Recognition and Machine Learning” by Christopher M. Bishop
• Course: Machine Learning by Andrew Ng (Coursera, Stanford)
• Book: “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron (for practical, hands-on experience)

2.2 Deepening Machine Learning Knowledge

Learn how to handle missing data, model selection, regularization, cross-validation, and how to assess your models using various metrics.

Resources:

• Course: Advanced Machine Learning Specialization (Coursera, National Research University Higher School of Economics)
• Practical Course: Kaggle Learn (Hands-on practice with real datasets)

2.3 Project Work

Apply what you’ve learned on real datasets:

• Build a binary classifier (e.g., predicting churn in a telecom company).
• Work with regression models for predicting continuous variables (e.g., predicting house prices).

3. Deep Learning & Neural Networks (2-3 Months)

Deep learning is essential for modern AI, and this section will teach you to build complex models like neural networks and work with frameworks such as TensorFlow and PyTorch.

3.1 Neural Networks and Backpropagation

Learn about perceptrons, backpropagation, and the architecture of neural networks.

Resources:

• Book: “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
• Course: Deep Learning Specialization (Coursera, Andrew Ng)
• Course: Practical Deep Learning for Coders (Fast.ai, Practical approach with PyTorch)

3.2 Convolutional Neural Networks (CNNs)

Focus on image data and the architecture behind image recognition models like CNNs.

Resources:

• Course: CS231n: Convolutional Neural Networks for Visual Recognition (Stanford, free course materials)
• Practical: Use Kaggle to participate in computer vision competitions.

3.3 Recurrent Neural Networks (RNNs) and LSTMs

Learn about sequence data, especially in applications like Natural Language Processing (NLP) and time-series prediction.

Resources:

• Course: Sequence Models (Coursera, Deep Learning Specialization)
• Book: “Neural Networks and Deep Learning” by Michael Nielsen (for understanding theory)

3.4 Projects

• Image Classification: Build a CNN model for classifying images (e.g., CIFAR-10 dataset).
• Text Generation or Sentiment Analysis: Use an RNN/LSTM for text generation or NLP tasks.

4. Advanced Topics (1-2 Months)

4.1 Reinforcement Learning (RL)

RL is key to AI applications like game-playing, robotics, and autonomous systems.

Resources:

• Book: “Reinforcement Learning: An Introduction” by Richard S. Sutton and Andrew G. Barto
• Course: CS50’s Introduction to Artificial Intelligence with Python (Harvard, free course materials)

4.2 Generative Models (GANs)

Learn how to create data with GANs, useful in creative applications like image synthesis and data augmentation.

Resources:

• Book: “Deep Learning with Python” by François Chollet (contains GANs content)
• Course: Generative Adversarial Networks (GANs) Specialization (Coursera)

4.3 Natural Language Processing (NLP)

Learn how to work with language data—text classification, named entity recognition, and language generation.

Resources:

• Book: “Speech and Language Processing” by Daniel Jurafsky & James H. Martin
• Course: Natural Language Processing Specialization (Coursera)

4.4 Advanced Projects

• Reinforcement Learning: Build an agent to play games (e.g., OpenAI Gym, Atari games).
• GANs Project: Create synthetic images or videos.
• NLP Application: Build a chatbot or a text summarization model.

5. Mastery Level (Ongoing)

By now, you’ll be capable of building and deploying models on your own. Continue deepening your understanding by:

1. Contributing to Open-Source AI Projects: Engage with repositories on GitHub to refine your skills.

   • Example: Contribute to TensorFlow or PyTorch open-source projects.

2. Building End-to-End AI Applications: Work on end-to-end projects where you gather data, preprocess it, build models, and deploy them (e.g., deploying a model on AWS or Google Cloud).

   • Course: AI For Everyone (Andrew Ng’s course on AI deployment)

3. Staying Up-to-Date:

   • Read Papers: Engage with papers on arXiv, Google Scholar, and other AI journals.
   • Follow AI Researchers: Keep up with the latest trends and research papers by following key figures in AI (e.g., Yann LeCun, Geoffrey Hinton, Ian Goodfellow).

Final Resources Summary:

Books:

1. “Deep Learning” by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

   • A comprehensive textbook that covers the theory and practice of deep learning, essential for anyone serious about mastering AI.

2. “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron

   • Practical guide to machine learning with a focus on implementing models using Python libraries like Scikit-Learn, Keras, and TensorFlow.

3. “Pattern Recognition and Machine Learning” by Christopher M. Bishop

   • An in-depth textbook on probabilistic graphical models and statistical pattern recognition techniques. Great for building a deeper theoretical foundation.

4. “Reinforcement Learning: An Introduction” by Richard S. Sutton and Andrew G. Barto

   • The definitive text for learning reinforcement learning from scratch. Covers both theory and practical algorithms.

5. “Speech and Language Processing” by Daniel Jurafsky & James H. Martin

   • A key resource for understanding natural language processing (NLP), focusing on both theory and practical applications in AI systems.

Online Courses:

1. Machine Learning by Andrew Ng (Coursera)

   • This foundational course is a must for beginners, covering core machine learning algorithms like regression, classification, and clustering, as well as practical implementation.

2. Deep Learning Specialization (Coursera, Andrew Ng)

   • An excellent series that dives deep into neural networks, CNNs, RNNs, and more, with hands-on coding using TensorFlow and Keras.

3. Practical Deep Learning for Coders (Fast.ai)

   • A highly practical, hands-on deep learning course that teaches you to build state-of-the-art models with PyTorch, ideal for those who want to quickly apply deep learning techniques.

4. Advanced Machine Learning Specialization (Coursera, National Research University HSE)

   • A more advanced series that goes deep into complex ML topics, including unsupervised learning, deep reinforcement learning, and deploying AI systems.

5. CS231n: Convolutional Neural Networks for Visual Recognition (Stanford)

   • An in-depth course on CNNs, focusing on computer vision tasks like image recognition, object detection, and segmentation.

6. AI For Everyone (Coursera, Andrew Ng)

   • A non-technical course that introduces the fundamentals of AI, including its societal impact and deployment challenges, great for understanding AI’s broader context.

7. Reinforcement Learning Specialization (Coursera, University of Alberta)

   • A comprehensive course series dedicated to reinforcement learning, covering algorithms like Q-learning, policy gradient methods, and practical RL applications.

Additional Resources:

1. Kaggle:

   • A platform to practice your machine learning and data science skills with real-world datasets and competitions.

2. Fast.ai Documentation:

   • A great resource for deep learning practitioners, particularly those working with PyTorch.

3. Google AI Blog:

   • Stay updated with the latest advancements in AI directly from one of the leading research labs in the field.

4. arXiv:

   • The go-to repository for cutting-edge AI research papers. Read and keep up with the latest developments in the AI field.

5. CS50’s Introduction to Artificial Intelligence with Python (Harvard)

   • A beginner-friendly course that covers foundational AI concepts and practical applications, using Python as the primary language.