#machineLearning

Day 6 of my CNN journey. After building a CNN from scratch with NumPy and experimenting with TensorFlow/Keras, I moved to PyTorch. This time, I wanted to understand what actually happens inside the training loop. 👇 #MachineLearning #DeepLearning

Since it's summer, and more or less internship and tech interview season, I made all 30 chapters of my Machine Learning Q and AI book freely available for the summer: Hope it’s helpful! Happy reading, and good luck if you are interviewing!

Deep-Path: The entire Deep-ML catalog, organized into one structured learning path featuring nearly 2,000 machine learning coding problems and hands-on projects. Throughout the path, you'll work through the math, implement concepts from scratch using minimal libraries like NumPy or CUDA, and build projects step by step There's also Deep-Path Essentials, a shorter version with 400 core problems and projects organized into 10 sections, covering the fundamentals of machine learning

Awesome updated 309-page PDF eBook >> "Patterns, Predictions, and Actions: Foundations of Machine Learning" at …sharpen your Probability, Calculus, and Linear Algebra knowledge with this! ————— #ML #DataScience #DataScientist

Exploratory Data Analysis (EDA) is a critical step in any data analysis project. We will explore how to perform EDA with R, focusing on data visualization and summary statistics. #DataScience #machinelearning #dataAnalysts #ArtificialInteligence #coding

Deep Learning with PyTorch — Step-by-Step Beginner's Guides (3 volumes) Vol.1 (Fundamentals): Vol.2 (Computer Vision): Vol.3 (Sequences and NLP): ——— #MachineLearning #ML #AI #DataScience #DataScientist

Data Scientists: Stop learning more machine learning. Start learning how to SHIP AI. Knowing 20 ML algorithms won't make you an AI Engineer. Building AI systems that actually WORK will. Here's the 9-step roadmap I'd follow to go from Data Scientist → AI Engineer in 2026: 1️⃣ Software Engineering APIs, Git, testing, SQL, async Python, Docker, debugging. A notebook isn't a product. 2️⃣ LLM Applications Claude/OpenAI APIs, prompting, structured outputs, tool calling, context windows. Build apps, not demos. 3️⃣ RAG Systems Chunking, embeddings, vector DBs, hybrid search, reranking, retrieval evaluation. Connect AI to real business data. 4️⃣ AI Agents Tools, state, memory, retries, permissions, human approval. Start with ONE reliable agent. Not a swarm. 5️⃣ AI Evaluation Test sets, hallucinations, task success, latency, cost, agent traces. Your data science background is a HUGE advantage here. 6️⃣ Production Engineering FastAPI, cloud, CI/CD, monitoring, security, observability. Get your work out of localhost. 7️⃣ Optimization Caching, batching, routing, inference cost and latency. Go deeper only when the problem demands it. 8️⃣ AI-Assisted Coding Use Claude Code and Codex to build, test and debug faster. You don't need to write every line. You need to understand the system. 9️⃣ Real Projects — From Day 1 Build a RAG app. Automate a workflow. Deploy an agent. Monitor it. A portfolio beats a pile of certificates. Here's the point: You ALREADY know data, statistics, Python and ML. Don't start over. Build on it. The next step isn't another algorithm. It's learning to turn AI into working software. Stop collecting certificates. Start shipping. ♻️ Repost for a data scientist making the leap to AI engineering. 🚨 Want to learn how to build + ship AI and Data Science projects (that businesses actually want in 2026)? On October 21st, I am hosting a free workshop to help you get started with AI + DS projects in Python (free). Register here (500 seats):

Generative Quantum Machine Learning Methods for Cancer Detection using Augmented Datasets #machinelearning #compchem

🚀 30 AI Algorithms Every Data Scientist Should Know From Machine Learning to Deep Learning, these algorithms are essential for understanding how AI works. Save this cheat sheet for future reference! #AI #MachineLearning #DataScience

Naive Bayes Classification, explained with Python code: + Learn more in this book: ———— #DataScience #MachineLearning #ML #DataScientist

Machine Learning Methods — Free 874-Page Open Access Textbook! Pre-Order, Now! Hang Li’s Springer 2026 book, a follow up 2nd Edition is an enormous reference spanning the four core branches of Machine Learning: • Supervised Learning • Unsupervised Learning • Deep Learning • Reinforcement Learning • 80+ essential algorithms • GBDT + EM Algorithm • Transformers • Diffusion Models • PPO • Mathematical formulations + intuitive explanation • Practical examples+ Machine Learning real-world relevance At 874 pages with 260 illustrations, it is designed for advanced undergraduate/graduate students of ML, educators and AI professionals. Springer explicitly marks the textbook Open Access, free and unlimited access. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Mathematics #Programming #Books #Coding #100DaysofCode Free Open Access: Amazon:

Ever wondered how machines actually learn from data? This step-by-step visual breaks down the 12-stage journey of Machine Learning, turning complex AI training into a simple, intuitive process anyone can follow. From defining the problem to collecting, cleaning, and labeling data… To training, tuning, and evaluating models… All the way to deploying it in the real world and retraining it over time... Each step plays a critical role in transforming raw data into real-world intelligence. Let’s quickly walk through it: 1. Define the Problem – What do you want the machine to solve? 2. Collect the Data – Pull in data from APIs, sensors, or databases. 3. Explore the Data – Understand structure, trends, and outliers. 4. Clean the Data – Fix missing, noisy, or incorrect entries. 5. Label the Data – Add correct answers if it’s supervised learning. 6. Split the Dataset – Into training and test sets (sometimes validation). 7. Choose the Algorithm – Like decision trees, regression, or neural networks. 8. Train the Model – Feed in the training data, let it learn patterns. 9. Evaluate the Model – Test on unseen data using metrics like accuracy. 10. Tune the Model – Improve it by adjusting parameters or trying variations. 11. Deploy the Model – Push it into production for real-world use. 12. Monitor & Retrain – Keep it updated and accurate over time. ✅ Key Takeaway: Machine learning is not magic — it’s methodical. And understanding this pipeline helps you build better models, make smarter decisions, and debug faster. 📌 Save this for your ML journey, whether you're just starting out or reviewing your fundamentals. P.S. Preparing for a software engineering role? Check out my book, System Design and Behavioral Intelligence. It will help you: •⁠ ⁠Build strong System Design foundations and approach design problems with confidence •⁠ ⁠Develop practical frameworks to navigate complex behavioral and leadership challenges Get Your Copy anywhere internationally: US: UK: India :

Understand the difference between supervised and unsupervised learning in 10 seconds! Essential for AI, ML and Data Science interviews. #MachineLearning #AI #DataScience #InterviewPrep #Shorts

Machine Learning from Scratch 🚀 A 33K-star GitHub repo rebuilds machine learning algorithms from scratch, from linear regression to GANs and deep reinforcement learning. It's called ML-From-Scratch, created by Erik Linder-Noren, and the entire project is designed around understanding the implementations rather than optimizing them for speed. The repository contains readable Python implementations of the algorithms normally hidden behind high-level machine learning libraries. It covers - → Linear and logistic regression → Decision trees, random forests and gradient boosting → XGBoost and support vector machines → K-means, DBSCAN, PCA and Gaussian mixtures → Neural networks, CNNs and RNNs → Generative adversarial networks → Deep Q-learning for CartPole The neural-network code is especially useful. It includes implementations of convolutional layers, pooling, batch normalization, dropout, activation functions, loss functions and optimizers. You can follow how the components fit together to train a convolutional classifier, generate handwritten digits with a GAN, or train a reinforcement-learning agent. Most of the numerical work is built around NumPy, with additional scientific Python dependencies where needed. The implementations prioritize readability over performance. It's an older codebase, so some examples need dependency updates to run with current libraries. For anyone who has studied machine learning theory and wants to examine the actual algorithms, this is an excellent reference to work through. GitHub:

Deep Learning with PyTorch — Step-by-Step Beginner's Guides (3 volumes) Vol.1 (Fundamentals): Vol.2 (Computer Vision): Vol.3 (Sequences and NLP): ——— #MachineLearning #ML #AI #DataScience #DataScientist

Harvard's CS50 AI is free online: 7 weeks of lectures and projects from @cs50. → Search: code a tic-tac-toe AI → Logic & probability: Minesweeper, PageRank → Learning: an AI that teaches itself Nim → Neural networks, then language #AI #MachineLearning

Building an AI Agent with Memory: Microsoft Agent Framework + Neo4j #MachineLearning #ChatGPT

AI and ML Papers Explained (HUGE list): Compiled by @omarsar0 @dair_ai 💯🏆🚀💥 #ML #MachineLearning #DataScience #Algorithms #NeuralNetworks #DeepLearning #Mathematics

Learn AI for FREE: #ArtificialIntelligence #LearnAI #MachineLearning #DeepLearning #Python #DataScience #GenerativeAI #AIAgents #PromptEngineering #HuggingFace #Kaggle #FreeLearning #TechEducation #dev_miracleonyia

This is free f*cking gold for AI engineers.. 10 repos worth keeping, from Python basics all the way to LLMs, agents and production AI. Forget the random tutorials. 1. Python-100-Days -- 100 days through fundamentals, data analysis and web dev. ↳ 2. Generative AI for Beginners -- LLM basics, prompting, RAG, agents and fine-tuning. ↳ 3. LLMs from Scratch -- build one yourself: tokenization, attention, transformers, training. ↳ 4. ML for Beginners -- 26 lessons of classical ML across 12 weeks. ↳ 5. OpenAI Cookbook -- working examples that take you from reading to building. ↳ 6. Stable Diffusion -- the original implementation and the research code behind it. ↳ 7. AI Agents for Beginners -- tool use, RAG, agent frameworks and multi-agent systems. ↳ 8. AI for Beginners -- 24 lessons over 12 weeks: neural nets, vision, NLP. ↳ 9. LLM App -- RAG pipelines, enterprise search, real-time data and vector search. ↳ 10. Segment Anything -- Meta's foundation model for image segmentation. ↳ Start from wherever you are: > Python -> Python-100-Days. > ML -> ML-For-Beginners. > LLMs -> LLMs-from-scratch. > Agents -> AI-Agents-for-Beginners. > Building -> OpenAI Cookbook, LLM App. > Vision -> Segment Anything. Pick one, build something real with it, then move on to the next.