Loading publication...

Home Books My Library About the Author About AXLY

Log In Register

Loading publication...

A

AXLY

An independent digital reading platform dedicated to sharing thoughtful technical books and accessible learning—one page at a time.

Written & published by Slayer (Medicharla Ravi Kiran).

PlatformBook Library My Library & Progress About AXLY Author Profile (Slayer)

InformationPrivacy Notice Terms of Use Reader Sign In Create Account

Connect with Authorravikiran@axly.in GitHub Profile LinkedIn Profile

© 2026 AXLY. All published works authored by Medicharla Ravi Kiran (Slayer). All rights reserved.

books.axly.in · Independent Author Platform

ML + Deep Learning Fundamentals — AXLY Books

Home BooksML + Deep Learning Fundamentals

Cover of ML + Deep Learning Fundamentals

Available FreeTechnologyFirst Edition (2026)

ML + Deep Learning Fundamentals

A 15-Day Course

Total Chapters33 Chapters

Reading Time~9 Hours

EditionFirst Edition (2026)

Access100% Free

About this Publication

ML + Deep Learning Fundamentals is a structured self-study course designed to help learners build a foundation in machine learning, deep learning, and the concepts behind modern Generative AI. Across 15 learning days, explore Python and mathematical essentials, NumPy, Pandas, regression, model evaluation, decision trees, clustering, embeddings, neural networks, backpropagation, PyTorch, CNNs, attention, and Transformers. The book also includes practical projects, assignments, worked examples, solutions, revision resources, and introductory learning paths covering LLMs, Retrieval-Augmented Generation (RAG), AI agents, tool calling, and MCP. Designed for self-study, this book helps learners progress from foundational concepts toward practical ML and GenAI projects at their own pace.

© 2026 SlayerAll Rights Reserved

Licensed under All Rights Reserved. Free access for individual educational study and technical advancement.

#Machine Learning#Deep Learning#Python#NumPy#Pandas#PyTorch#Neural Networks

Table of Contents

Browse the 33 published chapters and choose one to start reading.

Preface & About the AuthorWhy this book exists, and who wrote it Preface Machine learning looks huge from the outside. This book breaks the beginner path into fifteen focused days, from NumPy and Pandas, through regression, neural networks and Py

Start HereStart here Is This Book Beginner-Friendly? A Difficulty Map Short answer: yes for Days 2–8, and yes with patience for Days 9–15. Every day opens with a plain-words Big Picture, uses analogies, and works small numbers by

The 15-Day Course PART I Detailed beginner notes for every day, with worked examples, code, a quick recap and a checkpoint. Day 1 is the prerequisite day: do it even if you think you know the basics, then move on. | Day | Topic | | --- |

Day 1 · Prerequisites: Python & Math Essentials — Part 1: Python EssentialsSuggested pace: 1–1.5 hrs read · 2–3 hrs type and solve · rest to review your mistakes Goal of today Build the minimum Python and math you need for the rest of the course. You do not need advanced calculus. Learn the min

Home BooksML + Deep Learning Fundamentals

Cover of ML + Deep Learning Fundamentals

Available FreeTechnologyFirst Edition (2026)

ML + Deep Learning Fundamentals

A 15-Day Course

Total Chapters33 Chapters

Reading Time~9 Hours

EditionFirst Edition (2026)

Access100% Free

About this Publication

ML + Deep Learning Fundamentals is a structured self-study course designed to help learners build a foundation in machine learning, deep learning, and the concepts behind modern Generative AI. Across 15 learning days, explore Python and mathematical essentials, NumPy, Pandas, regression, model evaluation, decision trees, clustering, embeddings, neural networks, backpropagation, PyTorch, CNNs, attention, and Transformers. The book also includes practical projects, assignments, worked examples, solutions, revision resources, and introductory learning paths covering LLMs, Retrieval-Augmented Generation (RAG), AI agents, tool calling, and MCP. Designed for self-study, this book helps learners progress from foundational concepts toward practical ML and GenAI projects at their own pace.

© 2026 SlayerAll Rights Reserved

Licensed under All Rights Reserved. Free access for individual educational study and technical advancement.

#Machine Learning#Deep Learning#Python#NumPy#Pandas#PyTorch#Neural Networks

Table of Contents

Browse the 33 published chapters and choose one to start reading.

Preface & About the AuthorWhy this book exists, and who wrote it Preface Machine learning looks huge from the outside. This book breaks the beginner path into fifteen focused days, from NumPy and Pandas, through regression, neural networks and Py

Start HereStart here Is This Book Beginner-Friendly? A Difficulty Map Short answer: yes for Days 2–8, and yes with patience for Days 9–15. Every day opens with a plain-words Big Picture, uses analogies, and works small numbers by

The 15-Day Course PART I Detailed beginner notes for every day, with worked examples, code, a quick recap and a checkpoint. Day 1 is the prerequisite day: do it even if you think you know the basics, then move on. | Day | Topic | | --- |

Day 1 · Prerequisites: Python & Math Essentials — Part 1: Python EssentialsSuggested pace: 1–1.5 hrs read · 2–3 hrs type and solve · rest to review your mistakes Goal of today Build the minimum Python and math you need for the rest of the course. You do not need advanced calculus. Learn the min

#Transformers

#Generative AI

Authored by Slayer (Medicharla Ravi Kiran)

22 min read

Day 1 · Part 2: Math Essentials Part 2 — Math Essentials Good news: you need intuition and a few formulas , not proofs. Every idea below is followed by a tiny number example you can check by hand. 1. Arithmetic, percentages, averages Definition A perc

Day 1 · Key Terms and Part 3: Check Yourself Key Terms of Day 1 Come back to this table whenever a word feels unfamiliar. | Term | Plain-English meaning | | --- | --- | | Variable | A name that points to a stored value | | Type | The kind of value: int, float, str

Day 2 · NumPy: Arrays, Shapes & Matrix MathSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 1: lists, loops, functions and import ☐ What a matrix shape such as (2, 3) means (rows, columns) ☐ Dot product

Day 3 · Pandas: Load, Clean & Prepare DataSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 2: arrays and shapes ☐ Day 1: dictionaries and lists (a DataFrame is built from them) ☐ Averages (mean, median)

Day 4 · Linear Regression + Gradient DescentSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ The straight line y = w·x + b (Day 1, Math 2) ☐ Averages and squared gaps (Day 1, Math 1 and 5) ☐ The slope idea fr

Day 5 · Overfitting, Regularization & Cross-ValidationSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 4: loss and fitting a line ☐ Why we split data into training and test sets (Day 4) ☐ Day 1: mean and standard d

Day 6 · Metrics, Decision Trees, Random Forest, BoostingSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 5: train / validation / test split and cross-validation ☐ Day 1: probability as a number from 0 to 1 ☐ Day 3: P

Day 7 · k-NN, k-Means, PCA, EmbeddingsSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 1 and Day 2: vectors, dot product and shapes ☐ Day 5: why features should be on a similar scale ☐ Day 1: mean (

Day 8 · Mini Project 1: Telco Customer ChurnSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 3: Pandas cleaning and encoding ☐ Days 5–6: cross-validation and metrics ☐ Day 7: why scaling matters If one of

Day 9 · Neural Networks From ScratchSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 2 and Day 1: matrix multiplication and shapes ☐ Day 4: loss and gradient descent ☐ Day 1: probability (softmax)

Day 10 · Backpropagation: How a Network LearnsSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 9: forward pass and loss ☐ Day 4: one gradient-descent update ☐ Day 1, Math 7: the three derivative rules If on

Day 11 · PyTorchSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Days 9–10: forward pass, backward pass, training loop ☐ Day 1: Python classes ( init and methods) ☐ Day 2: array sh

Day 12 · MNIST, Dropout, Early Stopping, CNNSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 11: tensors, nn.Module , training loop ☐ Day 5: overfitting and early stopping ☐ Day 2: reshaping If one of the

Day 13 · Attention & TransformersSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 1 and Day 9: dot product and softmax ☐ Day 2: matrix shapes and @ ☐ Day 7: embeddings (a word as a vector) ☐ Da

Day 14 · Capstone + RecapSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 8: the full project workflow ☐ Day 12: training and evaluating a neural network ☐ Day 6: metrics and baselines

Day 15 · What Comes NextSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 13: attention and the Transformer idea ☐ Day 7: embeddings and cosine similarity ☐ Day 6: precision and recall

Day 15 · GenAI Bridges PART II The missing links between the 15-day fundamentals and real GenAI systems: LLMs, RAG, tools, agents, evaluation, and security. - How Transformers become LLMs - Embeddings → Vector DB → RAG - Tools, Agents, and MC

Day 15 · Study Toolkit PART III Cheat sheets, self-tests, and study routines to keep the knowledge fresh. - Cheat sheet + what comes after - Master beginner checkpoint bank - If you get stuck — revision map - Recommended study pattern - Inter

Supplement: Topics the Book Named but Never TaughtThis supplement fills the gaps found in the main book. Each topic gives a plain explanation, a small example, and a snippet where one fits. Where each topic belongs. Read S1–S4 after Day 12 (CNN), S5 after Day 6, S6 afte

Assignment & Project Guide: Day-by-Day AssignmentsBeginner-friendly tasks, three-tier hints, fill-in READMEs and full solutions for Days 2–15 and the GenAI Project Ladder. How this part works with the book - Page references. Where this part says “Book pages 65, 84” it m

Assignment & Project Guide: GenAI Project LadderThe book does not ask you to build all ten projects at once. Pick a stage only after its prerequisite concepts feel comfortable. The ladder climbs from data cleaning to vector search, then RAG, tools, agents, MCP and a p

Assignment & Project Guide: Submission, Rubric and Checklist One repo, one story. Use exactly one repository so your progression from NumPy to GenAI is visible as one continuous artifact. text ml-dl-fundamentals/ ├── README.md ├── Day-01-NumPy-Pandas/ ├── Day-02-Linear-Regression

Solutions Appendix and References Stop. Have you tried Level 1 and the hints? If yes, go ahead. Read one solution, understand each line, close it, and type your own version from memory. Notes on the solutions - TESTED : run on the exact generated data f

Project Walkthroughs: How to Use This Part and One-Time SetupThe book gives you the ideas, the code and the finish lines. This part slows everything down. Each project is cut into small numbered steps, and each step tells you what to do, why, what you should see, and what to do if

Project 1 — Telco Customer Churn (Day 8) Goal. A phone company wants to know which customers are about to leave (“churn”) so it can offer them a deal first. You will build a model that answers: will this customer churn, yes or no? What you will produce. A note

Project 2 — MNIST: MLP vs CNN (Day 12) Goal. Teach a network to read handwritten digits (0–9), train two designs on identical data, and compare them honestly. On MNIST a CNN usually beats a plain MLP even with a similar number of weights, but you are here to

Project 3 — The Capstone (Day 14) Goal. Prove you understand a model by rebuilding it from a blank file, with no tutorial open. The capstone is not about a high score. It is about being able to explain every line and to show that someone else can run it

Project 4 — The GenAI Project Ladder (Day 15)The ladder has ten stages. Do not build all ten at once. Do one stage, finish it, push it to GitHub, then climb. The ten finish lines and the hints and rubric for each live in Part IV (p. 367) and the bridge chapters (p.

Beginner’s Companion (Plain-English Help) BEGINNER’S COMPANION For anyone who read a page twice and still felt lost. That is normal, and it is not a sign you are bad at this. Machine learning explanations usually start with formulas. This companion starts with

#Transformers

#Generative AI

Authored by Slayer (Medicharla Ravi Kiran)

22 min read

Day 1 · Part 2: Math Essentials Part 2 — Math Essentials Good news: you need intuition and a few formulas , not proofs. Every idea below is followed by a tiny number example you can check by hand. 1. Arithmetic, percentages, averages Definition A perc

Day 1 · Key Terms and Part 3: Check Yourself Key Terms of Day 1 Come back to this table whenever a word feels unfamiliar. | Term | Plain-English meaning | | --- | --- | | Variable | A name that points to a stored value | | Type | The kind of value: int, float, str

Day 2 · NumPy: Arrays, Shapes & Matrix MathSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 1: lists, loops, functions and import ☐ What a matrix shape such as (2, 3) means (rows, columns) ☐ Dot product

Day 3 · Pandas: Load, Clean & Prepare DataSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 2: arrays and shapes ☐ Day 1: dictionaries and lists (a DataFrame is built from them) ☐ Averages (mean, median)

Day 4 · Linear Regression + Gradient DescentSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ The straight line y = w·x + b (Day 1, Math 2) ☐ Averages and squared gaps (Day 1, Math 1 and 5) ☐ The slope idea fr

Day 5 · Overfitting, Regularization & Cross-ValidationSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 4: loss and fitting a line ☐ Why we split data into training and test sets (Day 4) ☐ Day 1: mean and standard d

Day 6 · Metrics, Decision Trees, Random Forest, BoostingSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 5: train / validation / test split and cross-validation ☐ Day 1: probability as a number from 0 to 1 ☐ Day 3: P

Day 7 · k-NN, k-Means, PCA, EmbeddingsSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 1 and Day 2: vectors, dot product and shapes ☐ Day 5: why features should be on a similar scale ☐ Day 1: mean (

Day 8 · Mini Project 1: Telco Customer ChurnSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 3: Pandas cleaning and encoding ☐ Days 5–6: cross-validation and metrics ☐ Day 7: why scaling matters If one of

Day 9 · Neural Networks From ScratchSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 2 and Day 1: matrix multiplication and shapes ☐ Day 4: loss and gradient descent ☐ Day 1: probability (softmax)

Day 10 · Backpropagation: How a Network LearnsSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 9: forward pass and loss ☐ Day 4: one gradient-descent update ☐ Day 1, Math 7: the three derivative rules If on

Day 11 · PyTorchSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Days 9–10: forward pass, backward pass, training loop ☐ Day 1: Python classes ( init and methods) ☐ Day 2: array sh

Day 12 · MNIST, Dropout, Early Stopping, CNNSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 11: tensors, nn.Module , training loop ☐ Day 5: overfitting and early stopping ☐ Day 2: reshaping If one of the

Day 13 · Attention & TransformersSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 1 and Day 9: dot product and softmax ☐ Day 2: matrix shapes and @ ☐ Day 7: embeddings (a word as a vector) ☐ Da

Day 14 · Capstone + RecapSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 8: the full project workflow ☐ Day 12: training and evaluating a neural network ☐ Day 6: metrics and baselines

Day 15 · What Comes NextSuggested pace: 1–1.5 hrs learn · 2–3 hrs code · rest to debug and document What you should know first ☐ Day 13: attention and the Transformer idea ☐ Day 7: embeddings and cosine similarity ☐ Day 6: precision and recall

Day 15 · GenAI Bridges PART II The missing links between the 15-day fundamentals and real GenAI systems: LLMs, RAG, tools, agents, evaluation, and security. - How Transformers become LLMs - Embeddings → Vector DB → RAG - Tools, Agents, and MC

Day 15 · Study Toolkit PART III Cheat sheets, self-tests, and study routines to keep the knowledge fresh. - Cheat sheet + what comes after - Master beginner checkpoint bank - If you get stuck — revision map - Recommended study pattern - Inter

Supplement: Topics the Book Named but Never TaughtThis supplement fills the gaps found in the main book. Each topic gives a plain explanation, a small example, and a snippet where one fits. Where each topic belongs. Read S1–S4 after Day 12 (CNN), S5 after Day 6, S6 afte

Assignment & Project Guide: Day-by-Day AssignmentsBeginner-friendly tasks, three-tier hints, fill-in READMEs and full solutions for Days 2–15 and the GenAI Project Ladder. How this part works with the book - Page references. Where this part says “Book pages 65, 84” it m

Assignment & Project Guide: GenAI Project LadderThe book does not ask you to build all ten projects at once. Pick a stage only after its prerequisite concepts feel comfortable. The ladder climbs from data cleaning to vector search, then RAG, tools, agents, MCP and a p

Assignment & Project Guide: Submission, Rubric and Checklist One repo, one story. Use exactly one repository so your progression from NumPy to GenAI is visible as one continuous artifact. text ml-dl-fundamentals/ ├── README.md ├── Day-01-NumPy-Pandas/ ├── Day-02-Linear-Regression

Solutions Appendix and References Stop. Have you tried Level 1 and the hints? If yes, go ahead. Read one solution, understand each line, close it, and type your own version from memory. Notes on the solutions - TESTED : run on the exact generated data f

Project Walkthroughs: How to Use This Part and One-Time SetupThe book gives you the ideas, the code and the finish lines. This part slows everything down. Each project is cut into small numbered steps, and each step tells you what to do, why, what you should see, and what to do if

Project 1 — Telco Customer Churn (Day 8) Goal. A phone company wants to know which customers are about to leave (“churn”) so it can offer them a deal first. You will build a model that answers: will this customer churn, yes or no? What you will produce. A note

Project 2 — MNIST: MLP vs CNN (Day 12) Goal. Teach a network to read handwritten digits (0–9), train two designs on identical data, and compare them honestly. On MNIST a CNN usually beats a plain MLP even with a similar number of weights, but you are here to

Project 3 — The Capstone (Day 14) Goal. Prove you understand a model by rebuilding it from a blank file, with no tutorial open. The capstone is not about a high score. It is about being able to explain every line and to show that someone else can run it

Project 4 — The GenAI Project Ladder (Day 15)The ladder has ten stages. Do not build all ten at once. Do one stage, finish it, push it to GitHub, then climb. The ten finish lines and the hints and rubric for each live in Part IV (p. 367) and the bridge chapters (p.

Beginner’s Companion (Plain-English Help) BEGINNER’S COMPANION For anyone who read a page twice and still felt lost. That is normal, and it is not a sign you are bad at this. Machine learning explanations usually start with formulas. This companion starts with