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Machine learning

Machine Learning Foundation

The foundations of machine learning, as a recorded video course.

beginnerEnglishCertificate
Recorded course₹399
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About this course

The foundations of machine learning, as a recorded video course.

Course curriculum

11 sections · 132 lessons · 2h 39m total
Lesson 1 - What is Machine Learning
9 lessons
  • FileWrite the Rule
  • VideoPreview8:50
  • QuizIs it machine learning?
  • FileProgramming or Machine Learning
  • FileBuild the Diagram
  • FileSpam Filter Showdown
  • FileDid It Learn, or Did It Memorise?
  • FileWhat Goes In, What Comes Out
  • QuizQuiz 1: check your knowledge
Lesson 2: Three Ways Machine Learn
8 lessons
  • FileGive Each One the Right Answer
  • VideoML02 — Three Ways Machines Learn7:33
  • QuizML02 — Which kind of learning?
  • FileDoes It Come With the Answers?
  • FileWhich Box Does It Go In?
  • FileFind the Groups Nobody Told You About
  • FileTrain the Dog
  • QuizQuiz 2: Check your knowledge
Lesson 3: Features & labels
7 lessons
  • FileRead The Table
  • VideoML03 — Features and Labels4:55
  • FileWhich Column Is the Answer?
  • FileThe Column That Changes Its Mind
  • FileNumbers, Categories, Pixels
  • FileSet Up the Problem
  • QuizQuiz 3 — Check your knowledge
Lesson 4: Guess-Check-Adjust
8 lessons
  • FileThrow It
  • VideoML04 — Guess, Check, Adjust5:20
  • FileName the Step
  • FileTurn the Knob
  • FileRead the Curve
  • FileWhat “Learned” Really Means
  • QuizML04 — The guess-check-adjust loop
  • ReadingReading the Notation
Lesson 5: Loss
8 lessons
  • FileHow Wrong Is This Guess?
  • VideoML05 — Loss5:46
  • FileWhy Square It?
  • FileWhich Model Is Better?
  • Teach the AIML05 — Teach the confused student
  • FileRead the Loss Curve
  • QuizQuiz 5: Check your knowledge
  • Trace tableML05 — Compute the loss by hand
Lesson 6: Gradient Descent
9 lessons
  • FileFind the Bottom, Blindfolded
  • VideoML06 — Gradient Descent6:02
  • ReadingThe Learning Rate, in a Little More Detail
  • Trace tableML06 — One step of gradient descent
  • FileSet the Learning Rate
  • FileWhich Way Is Downhill?
  • Teach the AIML06 - Explain it to a stuck learner
  • FileWhere Does It Stop?
  • QuizML06 — Gradient descent intuition
Lesson 7: Overfitting
7 lessons
  • FileWhich Student Would You Hire?
  • VideoML07 — Overfitting6:35
  • FileShip It or Send It Back?
  • FileSpot the Turn
  • Teach the AIML07 - Explain it to a proud engineer
  • Predict the outputML07 — Which model generalizes?
  • QuizML07 — Spotting overfitting
Module 2 — Supervised Learning, in depth
29 lessons
  • VideoML08 — Linear Regression3:09
  • FileML08 — Predict with a fitted line
  • Write codeML08 — Fit your first model (linear regression)
  • VideoML09 — Logistic Regression3:30
  • FileML09 — From probability to class
  • VideoML10 — Decision Trees3:38
  • FileML10 — Walk a sample down a decision tree
  • Trace tableML10 — Walk a sample down a decision tree
  • VideoML11 — K-Nearest Neighbours3:26
  • Trace tableML11 — KNN by hand: count the votes
  • VideoML12 — Evaluation4:02
  • QuizML12 — Reading the metrics
  • Trace tableML12 — Metrics from a confusion matrix
  • VideoML13 — Train, Validation, Test3:58
  • Parsons problemML13 — Order the train / validate / test workflow
  • VideoML14 — Bias and Variance2:59
  • QuizML14 — Bias vs variance
  • VideoML15 — The Fit Spectrum2:53
  • QuizML15 — Place it on the fit spectrum
  • VideoML16 — Regularization2:58
  • QuizML16 — Regularization
  • VideoML17 — Feature Scaling3:05
  • Write codeML17 — Scale the features
  • QuizML17 — When does scaling matter?
  • VideoML18 — Feature Engineering3:15
  • Write codeML18 — Engineer a useful feature
  • VideoML19 — Hyperparameter Tuning3:30
  • Parsons problemML19 — Order a grid search
  • QuizML19 — Hyperparameter or learned parameter?
Module 3 - Neural Networks & Deep Learning
18 lessons
  • VideoML20 — The Neuron3:22
  • Trace tableML20 — Compute a neuron's output
  • VideoML21 — Layers and the Forward Pass3:21
  • Parsons problemML21 — Order the forward pass
  • Trace tableML21 — Forward pass through two layers
  • VideoML22 — Activation Functions3:14
  • Predict the outputML22 — Predict the activation output
  • QuizML22 — Why activations?
  • VideoML23 — Backpropagation3:26
  • QuizML23 — Backpropagation
  • VideoML24 — Why Deep Networks3:22
  • QuizML24 — Why go deep?
  • VideoML25 — Convolutional Networks (CNN)3:25
  • Trace tableML25 — Slide a CNN filter over a row
  • VideoML26 — Recurrent Networks (RNN)3:32
  • QuizML26 — RNNs and sequences
  • VideoML27 — Transformers4:00
  • QuizML27 — Transformers & attention
Module 4 - Doing It for Real
28 lessons
  • AssignmentCapstone — build a small end-to-end ML model
  • VideoML28 — The ML Workflow2:57
  • Parsons problemML28 — Order the ML workflow
  • VideoML29 — Data Cleaning3:16
  • Write codeML29 — Clean the data (handle missing values)
  • VideoML30 — Imbalanced Data3:06
  • QuizML30 — Imbalanced data
  • VideoML31 — Data Leakage3:24
  • QuizML31 — Spot the data leakage
  • VideoML32 — Ethics and Bias3:18
  • QuizML32 — Fairness & bias
  • Role playML32 — Explain a model's bias risk to a manager
  • VideoML33 — Deploying a Model3:02
  • Parsons problemML33 — From notebook to production
  • VideoML34 — Model Drift3:22
  • QuizML34 — Model drift
  • VideoML35 — Your First Model (+ CodeLab ML35b)3:16
  • VideoML35b - CodeLab First Model3:45
  • Write codeML35 — Your first end-to-end model
  • VideoML36 — Reading Data3:10
  • Write codeML36 — Read and inspect a dataset
  • VideoML37 — Classification Project3:32
  • Write codeML37 — Classification lab: train and score a classifier
  • VideoML38 — Common Mistakes3:15
  • Write codeML38 — Find the bug: the classic leakage mistake
  • VideoML39 — The ML Toolkit3:18
  • QuizML39 — Match the tool to the job
  • VideoML40 — Career Paths3:39
Final Exam and Certificate
1 lesson
  • QuizFinal Exam for Certificate