19 interactive labs from RAIN: write Arduino code for a virtual kit, tune controllers, plan robot paths, and train machine learning models. Once opened, the labs keep working without the internet.
For 12 months of access. Every lab, including new ones as they are added.
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The RIDE Lab is free for RDA trainees and AIML trainees in training. Log in to the portal and open it from your menu.
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Write Arduino code, upload it to a virtual Uno wired to the RAIN kit and test it: LEDs, buttons, sensors, a servo valve on a water tank and a robot car that avoids obstacles and follows a line.
Build circuits on a virtual breadboard and measure them: Ohm's law, voltage dividers for sensors, LEDs and resistors, RC timing, transistors as switches and op-amps.
Block diagrams, transfer functions, open and closed loop, PID, disturbances and noise, with time response, poles and zeros, Bode, root locus and the Routh table.
Tune P, PI and PID controllers and on-off control with hysteresis on motors, heaters, tanks and a drone, with anti-windup, noise, and Ziegler-Nichols tuning.
An industrial plant on a control-room screen: tank level and temperature loops, cascade, feedforward, valves, alarms and an IoT dashboard.
State-space models, controllability, pole placement, LQR, observers and the Kalman filter, ending with balancing an inverted pendulum.
Frames, rotations and transforms in 3D, robot arms with DH parameters, forward and inverse kinematics, the Jacobian, workspaces and mobile robot motion.
Design a robot from real parts against a specification: speed, torque, battery life and cost, compare concepts, then test it in an arena.
Draw a map and watch BFS, Dijkstra, A*, RRT and potential fields find a way, step by step, then drive the path with a robot.
Outdoor robots on real terrain: slopes, mud and sand, routes that save time or energy, replanning as the robot discovers the land, and farm and survey coverage.
Odometry drift, LiDAR, Kalman and particle filters, occupancy grid maps and SLAM with loop closure.
Flocking, consensus, formations, foraging, coverage and task allocation: simple local rules, surprising group behaviour.
Traffic jams from nowhere, junctions and signals, and what an autonomous car sees and decides on a busy Nigerian road.
Clean, explore and chart real-style data with the pandas code shown alongside, then measure uncertainty with samples, intervals and tests.
Symbolic algebra and calculus, linear algebra as transformations, gradient descent, probability, complex numbers, Laplace and Fourier.
Linear and logistic regression trained by gradient descent, honest evaluation, bias and variance, k-nearest neighbours and decision trees.
Random forests and boosting, support vector machines, clustering, PCA, anomaly detection, recommendations and time series.
From one neuron to deep networks: backpropagation step by step, optimisers, regularisation, convolutions and a CNN you train in the browser.
Tokens, embeddings, n-grams, RNNs and LSTMs, and attention and transformers taken apart one step at a time.
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