Skip to content
/ machine-learning Public

🤖 All about Machine Learning and Deep Learning and Agentic AI • Data → Model → Predict

37 stars 9 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

Rustam-Z/machine-learning

BranchesTags

Repository files navigation

Machine Learning & Deep Learning & Agentic AI: Roadmap

Content

Math (Calculus, Linear Algebra, Propability & Statistics)

Python for Data Science

Books:

  • "Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython"
  • "Python Data Science Handbook"

Machine Learning

My notes from Stanford course: https://github.com/Rustam-Z/machine-learning-stanford-notes

What is Machine Learning?

  • Machine learning (ML) is field of study that gives computers the ability to learn without being explicitly programmed. Machine Learning is making computers do things that we’ve never made computers do before.
  • A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.

Why use ML?

  • Problems for which existing solutions require a lot of hand-tuning or long lists of rules: one Machine Learning algorithm can often simplify code and perform better (eg. spam classifier)
  • Complex problems for which there is no good solution at all using a traditional approach: the best Machine Learning techniques can find a solution (eg. speech recognition)
  • Fluctuating environments: a Machine Learning system can adapt to new data.
  • Getting insights about complex problems and large amounts of data (eg. data mining)

Types of ML Systems:

  • Whether or not they are trained with human supervision supervised, unsupervised, semisupervised, and Reinforcement Learning
    • Supervised learning - training data with labels (expected outputs).
      • Tasks: classification, regression (univariate / multivariate).
      • Class / sample / label / feature (predictors: age, brand, ...) / attribute
      • Algorithms: k-Nearest Neighbors, Linear Regression, Logistic Regression, Support Vector Machines (SVMs), Decision Trees and Random Forests, Neural networks
    • Unsupervised learning - training data is unlabeled.
      • Tasks: clustering, anomaly detection, visualization & dimensionality reduction.
      • Clustering (find similar visitors): K-Means, DBSCAN, Hierarchical Cluster Analysis (HCA)
      • Anomaly detection & novelty detection (detect unusual things): One-class SVM, Isolation Forest
      • TIP! Use dimensionality reduction algo before feeding to supervised learning algorithm.
      • TIP! Automatically removing outliers from a dataset before feeding it to another learning algorithm.
    • Semisupervised learning - a lot of unlabeled data and a little bit of labeled data.
      • Example: like in Google photos, it recongnizes same person in many pictures. We need supervised part because we need to seperate similar clusters. (like similar people)
    • Reinforcement Learning - agent can observe environment, and perform some actions, and get rewards and penalties. Then it must teach itself the best strategy (policy) to get max reward. A policy defines what action the agent should choose when it is in a given situation.

Courses:

Books:

  • *"Machine Learning For Absolute Beginners" Oliver Theobald
  • *"Machine Learning for Humans" - all in one, very short explanation of ML
  • *"The Hundred-Page Machine Learning Book" Andriy Burkov
  • *“Deep Learning with Python” - first chapters
  • ***"Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow" - first chapters
  • "Machine Learning Engineering" Andriy Burkov
  • "The Elements Of Statistical Learning: Data Mining, Inference and Prediction"
  • "AI and Machine Learning for Coders" - Laurence Moroney, deeplearning.ai TensorFlow Developer specialization instructor
  • "Python Machine Learning"
  • *"Machine Learning Yearning" Andrew Ng - After finishing this book, you will have a deep understanding of how to set technical direction for a machine learning project.

Practice:

Neural Networks and Deep Learning

My notes from Deep Learning course by Andrew Ng: https://github.com/Rustam-Z/deep-learning-notes

Courses:

Books:

Extra:

Agentic AI

At the heart of agentic AI are autonomous agents that can:

  • Perceive: Interpret input from the environment.
  • Plan: Generate strategies to achieve goals.
  • Act: Execute actions and interact with the world.
  • Learn: Improve decisions based on feedback.

Start with Intro to LLM by Andrej Karpathy course.

To read:

To watch:

General notes

Research

About

🤖 All about Machine Learning and Deep Learning and Agentic AI • Data → Model → Predict

Topics

data-science machine-learning deep-learning tensorflow scikit-learn keras

Resources

Readme
Activity

Stars

37 stars

Watchers

1 watching

Forks

9 forks
Report repository

Contributors 2

  •  
  •  

Languages