Learn Machine Learning & AI vs Neural Network Academy Lite

Learn Machine Learning & AI takes you from "what is machine learning" to understanding the algorithms behind modern artificial intelligence — 105 subjects and 2,650 lessons, in pieces short enough to read on a phone. No prior AI experience required. If you can follow basic algebra and a little Python, you can follow this course. WHAT YOU WILL LEARN Machine Learning Foundations — how machine learning differs from traditional programming, the training workflow, test data, overfitting, underfitting, bias and variance, and how models are evaluated. Supervised Learning — linear and logistic regression, decision trees, random forests, support vector machines, k nearest neighbours and naive Bayes, each with the intuition behind it. Unsupervised Learning — k means and hierarchical clustering, dimensionality reduction, principal component analysis and anomaly detection. Deep Learning and Neural Networks — perceptrons, activation functions, backpropagation, convolutional networks for images, recurrent networks and LSTMs for sequences, and an introduction to transformers. Reinforcement Learning — agents, environments, rewards, Q learning and policy based methods, without heavy mathematics. Natural Language Processing — tokenization, embeddings, sentiment analysis, and how large language models are built on these ideas. Tools and Libraries — where Python, TensorFlow, Keras, scikit learn, NumPy, Pandas, H2O, OpenCV and Spark MLlib fit into a real workflow. Applied Machine Learning — healthcare, finance, recommendation systems, fraud detection, computer vision and forecasting. TEST WHAT YOU HAVE LEARNED Over 8,000 quiz questions across all 105 subjects. Ten questions per session, four options each, tied to the chapter you have been reading. Three timed difficulty levels: beginner 30 seconds per question, intermediate 20, expert 10. A session runs 5:00, 3:20 or 1:40. Pass at 70%. Filter by level, by attempted or untried, and by best score, so practice targets the algorithms you have not yet understood. Every attempt is stored. ML INTERVIEW PREPARATION 25 machine learning roles with 100 questions each — 2,500 in total — from junior ML engineer through senior, staff and principal, plus data scientist, applied scientist, MLOps, NLP and computer vision. Timed like a real screening round, scored, and saved so you can retake and compare. STUDY OFFLINE Download any subject for offline reading. Lesson text is stored on the device and stays available with no connection; images still need network. Anything you open is cached automatically. The Downloads screen shows what is saved and lets you cancel, resume or remove a subject. PROGRESS, STREAKS AND CERTIFICATES Mark lessons complete, watch the progress ring fill on every subject, and use the Continue card to return to exactly where you stopped. A daily streak counts every day you study, with an optional reminder that is off by default. Complete a subject and pass its quiz at 70% or above to unlock a certificate you can save and share. BUILT FOR LEARNING ON A PHONE Plain language first, mathematics second — every algorithm is explained in words before any notation appears. Short sections you can finish in a few minutes. Syntax highlighted code with one tap copy. Search across the whole course. Bookmarks and history, light, dark and system themes, adjustable font size, and tablet layouts. WHO IT IS FOR Students who want a second explanation when the lecture did not land. Developers moving into machine learning engineering or data science. Candidates preparing for ML and data science interviews, where the questions are still mostly about fundamentals. Professionals who need to understand what an AI system in their industry is actually doing. No video to buffer. No account required. Open your first lesson today. Questions, corrections or a topic request? Email mail@ehsanur.com — we update the app based on what learners ask for.

Neural Network Academy Lite makes artificial intelligence hands-on, approachable, and genuinely fun. Whether you're a student, a curious adult, or a developer new to machine learning, this app guides you from "what even is a neuron?" to training real AI on your own photos -- no experience needed. LESSONS Five bite-sized lessons cover the ideas behind every modern AI system: - What is a Neuron? -- drag sliders and watch a single neuron compute in real time - Weights & Bias -- see exactly how small number changes shift a network's output - Activation Functions -- explore sigmoid and ReLU on live interactive graphs - How Networks Learn -- follow a forward pass step by step with animated signal flow - Backpropagation -- understand the algorithm behind every AI you've ever used Each lesson ends with a quiz that locks in what you've learned and earns XP to unlock the next tool. PLAYGROUND Train a real neural network on classic logic problems: XOR, AND, and OR. Watch the live loss curve fall as your network finds the pattern. Adjust layer count, neuron count, and learning rate -- then hit Train and see the math work. NETWORK BUILDER Design your own architecture from scratch. Add layers, set neuron counts, and watch the signal flow forward through your custom network before training begins. HOT DOG LAB Draw examples directly on a pixel canvas, label them, and train a classifier on your own drawings. Works with any two categories you choose -- a playful introduction to image classification. PHOTO CLASSIFIER The most powerful tool in the app. Pick two categories, add photos from your library, tap Train, and watch a real model learn to tell them apart. Test it instantly with any new photo. BUILT FOR LEARNING - XP and level system keeps you motivated and on track - Lessons unlock progressively -- concepts build on each other naturally - Every idea has an interactive demo, not just a wall of text - Live training animations show loss dropping in real time - Clean native iOS design -- no ads, no subscriptions, no account needed Neural Network Academy Lite is for anyone who has ever wondered how AI actually works. Not just how to use it -- how it thinks.