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Ml Practice

Syllabus, study tools, lectures, and curriculum map.

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Weekly outline

Syllabus

Week topics from the course map

00W00

Topic

Incomplete

01W01

End-to-end machine learning project on scikit-learn

Incomplete

02W02

Graph Theory(VOL 3)

Incomplete

03W03

Regression on scikit-learn - Linear regression

Incomplete

04W04

Polynomial regression, Regularized models

Incomplete

05W05

Logistic regression

Incomplete

06W06

Classification on scikit-learn - Binary classifier

Incomplete

07W07

Classification on scikit-learn - Multiclass classifier

Incomplete

08W08

Support Vector Machines using scikit-learn

Incomplete

09W09

Decision Trees, Ensemble Learning and Random Forests

Incomplete

010W10

Decision Trees, Ensemble Learning and Random Forests (Continued)

Incomplete

011W11

Neural networks models in scikit-learn

Incomplete

012W12

Unsupervised learning

Incomplete

Study tools

Memorize first

Compact recall strip

Open notes

1 min

ML Practice: End-to-End Project Workflow

Open this first for the core logic and traps.

Dive in

2 min

Cross-Validation: K-Fold, Stratified, Group, and Time Series Splits

Open this first for the core logic and traps.

Dive in

2 min

Data Cleaning & Preprocessing

Open this first for the core logic and traps.

Dive in

Practice Dock

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Use this when the theory is already clear and the goal is fast, clean retrieval under pressure. Mark weak questions, copy a revision pack, and move on.

TheoTeacherTextbookSpeedrun

Ask less. Recall more.

Open Practice Dock

SM-2 Active

Spaced Repetition

Flashcards

Spaced repetition engine for long-term retention of core concepts.

Start session

Not Available

Interactive Simulations

Visual Labs

Exam Mode

Adaptive Practice

Mock Exam

Set paper

Code Protocol

OPPE Simulator

Secure Comm-Link Terminal

Secure Comm-Link // @MachineLearningPractice

Uplink 12ms

Syllabus Synchronization: Active

Open current cue

Syllabus Matrix Registry

Global Course Index

Open Full Frame

Master Registry

v6.4 Directory

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

BSCS2008

Diploma

4 Credits

Machine Learning Practice

This companion course to the ML Theory course introduces the student to scikit-learn, a popular Python machine learning module, to provide hands-on...

Required Dependencies:

Deployment Hub External Docs

Execution Protocol

Module 0

Topic

Module 1

End-to-end machine learning project on scikit-learn

Module 2

Graph Theory(VOL 3)

Module 3

Regression on scikit-learn - Linear regression

Module 4

Polynomial regression, Regularized models

Module 5

Logistic regression

Module 6

Classification on scikit-learn - Binary classifier

Module 7

Classification on scikit-learn - Multiclass classifier

Module 8

Support Vector Machines using scikit-learn

Module 9

Decision Trees, Ensemble Learning and Random Forests

Module 10

Decision Trees, Ensemble Learning and Random Forests (Continued)

Module 11

Neural networks models in scikit-learn

Module 12

Syllabus

Topic

End-to-end machine learning project on scikit-learn

Graph Theory(VOL 3)

Regression on scikit-learn - Linear regression

Polynomial regression, Regularized models

Logistic regression

Classification on scikit-learn - Binary classifier

Classification on scikit-learn - Multiclass classifier

Support Vector Machines using scikit-learn

Decision Trees, Ensemble Learning and Random Forests

Decision Trees, Ensemble Learning and Random Forests (Continued)

Neural networks models in scikit-learn

Unsupervised learning

Compact recall strip

One drill at a time, with Theo hints, teacher lanes, and a replay queue.

Flashcards

Visual Labs

Mock Exam

OPPE Simulator

Secure Comm-Link Terminal

Syllabus Matrix Registry

Master Registry

Foundational

Diploma

BSc Degree

BS Degree

PG / MTech

Machine Learning Practice

Execution Protocol

Topic

End-to-end machine learning project on scikit-learn

Graph Theory(VOL 3)

Regression on scikit-learn - Linear regression

Polynomial regression, Regularized models

Logistic regression

Classification on scikit-learn - Binary classifier

Classification on scikit-learn - Multiclass classifier

Support Vector Machines using scikit-learn

Decision Trees, Ensemble Learning and Random Forests

Decision Trees, Ensemble Learning and Random Forests (Continued)

Neural networks models in scikit-learn

Unsupervised learning

Video Archive

Intelligence Hub