AI Learning roadmap

Artificial intelligence is going to be here to stay. Unlike other evolution in software industry it is changing the fundamental structure of software domain. While it makes few roles redundant and at the same time it creates a new whole world of opportunity.

Software industry is keep evolving. There is continuos disruption like main frame to client and server architecture, from there to mobile technology, from there to cloud technology. But there is a small difference between previous evolution to AI. In previous advancements the role remains the same but they need to reskill them in new technology like a software engineer instead of AS400 he/she needs to work on Java, as system engineer instead of managing the local system he/she needs to manage the systems in cloud. The tools and languages are different but the role was intact or to be precises the industry wanted more such people. The automation resulted from these advancements increased the productivity of end user. The disruption is at the end customer side and the software side flurished.

Introduction ML.

But AI on the otherway is disrupting both the sides consumer and software developers. Whether in the given model is it sustainable is a different side. I have my own doubts. Let’s keep it aside. As said earlier lets assume AI is going to stay in one or other form.

Based on my past few years of working in AI and the way how I reskilled myself in this technology I would say it is bit different from other technologies. It creates three distinct roles.

  • Data Analyst
  • Data Engineer
  • Data Scientist

Even though each of the role has it’s own primary function but in all practical means very similar to fullstack developers the same person play multi hat on this roles. The roadmap I have given here is very generic for any person to work comfortably in a AIML project.

There are huge number of materials already available in the market to enable a person in AIML. But still when I started learning this technology there is no single place where I was able to get the end to end view in a coherant manner. I need to jumb between multiple websites, youtube videos etc and it became very tedious at one point of time to keep track of all.

I have structure this roadmap in such a waythat the concept is realized quickly with an working example. By hands-on exercise most of the doubts will get cleared. When you complete the entire roadmap you yourself will see a repeated pattern in any AIML project. Thats the crux of the enture journey.

I suggest AI driven development instead of memorizing each and every functions and APIs. You can use Gemini / ChatGPT / Claude or any other AI tool of choice.

For a better working envrironment google colab / AWS sagemaker is used in the reference. Feel free to use your familier environment

Let me share you the roadmap

Basic AI-ML

  1. Module 1: Introduction to AI & Machine Learning (2-3 weeks)

  • What is AI? History, types of AI (Narrow, General, Superintelligence).
  • What is Machine Learning? Supervised, Unsupervised, Reinforcement Learning.
  • Key AI/ML terminology and applications (e.g., image recognition, natural language processing).
  • Ethical considerations in AI.

  1. Module 2: Mathematics for AI/ML (3-4 weeks)

  • Basic Linear Algebra (vectors, matrices, operations).
  • Probability and Statistics (descriptive statistics, probability distributions, hypothesis testing).
  • Introduction to Calculus (gradients, derivatives – conceptual understanding for optimization).

  1. Module 3: Classic Machine Learning Algorithms (6-8 weeks)

  • Supervised Learning:
    • Regression (Linear Regression, Polynomial Regression).
    • Classification (Logistic Regression, K-Nearest Neighbors, Support Vector Machines, Decision Trees, Random Forests).
  • Unsupervised Learning:
    • Clustering (K-Means, Hierarchical Clustering).
    • Dimensionality Reduction (PCA).
  • Model Evaluation Metrics:
    • Accuracy, precision, recall, F1-score, MSE, R-squared
  • Cross-validation and overfitting/underfitting.
  • Project Work:
    • Hands-on projects applying learned concepts (e.g., building a simple spam classifier, an image recognition model, or a basic chatbot).

Advanced AI ML

  1. Module 4: Introduction to Deep Learning (4-6 weeks)

  • Neural Network fundamentals (perceptrons, activation functions, feedforward networks).
  • Introduction to popular deep learning frameworks (TensorFlow/Keras, PyTorch).
  • Convolutional Neural Networks (CNNs) for image classification.
  • Recurrent Neural Networks (RNNs) basics for sequence data.

  1. Module 5: Fundamentals of Natural Language Processing (NLP) (3-4 weeks)

  • Text preprocessing (tokenization, stemming, lemmatization).
  • Word embeddings (Word2Vec, GloVe).
  • Introduction to large language models (LLMs) concepts (no deep dive into architecture yet).
  • Basic text classification and sentiment analysis.

  1. Module 6: MLOps (3-4 weeks)

  • Introduction to MLOps concepts and practices.
  • Model deployment strategies (REST APIs, batch processing).
  • Monitoring and maintaining ML models in production.

  1. Project Work:

    • Hands-on projects applying learned concepts (e.g., building a simple spam classifier, an image recognition model, or a basic chatbot).