⌨️ () Overfitting and Underfitting（01:56:03 - 02:03:05）
Scikit-Learn Course - Machine Learning in Python Tutorial

Scikit-learn is a free software machine learning library for the Python programming language. Learn about machine learning using scikit-learn in this full course.

💻 Code: https://github.com/DL-Academy/MachineLearningSKLearn
🔗 Scikit-learn website: https://scikit-learn.org
✏️ Course from DL Academy. Check out their YouTube channel: https://www.youtube.com/channel/UCTgBlZ1fmNa87NUY1xvoxpg
🔗 View more courses here: https://thedlacademy.com/

⭐️ Course Contents ⭐️

Chapter 1 - Getting Started with Machine Learning
⌨️ (0:00) Introduction
⌨️ (0:22) Installing SKlearn
⌨️ (3:37) Plot a Graph
⌨️ (7:33) Features and Labels_1
⌨️ (11:45) Save and Open a Model

Chapter 2 - Taking a look at some machine learning algorithms
⌨️ (13:47) Classification
⌨️ (17:28) Train Test Split
⌨️ (25:31) What is KNN
⌨️ (33:48) KNN Example
⌨️ (43:54) SVM Explained
⌨️ (51:11) SVM Example
⌨️ (57:46) Linear regression
⌨️ (1:07:49) Logistic vs linear regression
⌨️ (1:23:12) Kmeans and the math beind it
⌨️ (1:31:08) KMeans Example

Chapter 3 - Artificial Intelligence and the science behind It
⌨️ (1:42:02) Neural Network
⌨️ (1:56:03) Overfitting and Underfitting
⌨️ (2:03:05) Backpropagation
⌨️ (2:18:16) Cost Function and Gradient Descent
⌨️ (2:26:24) CNN
⌨️ (2:31:46) Handwritten Digits Recognizer

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⌨️ () Overfitting and Underfitting（01:56:03 - 02:03:05）Scikit-Learn Course - Machine Learning in Python Tutorial

⌨️ () Introduction

⌨️ () Installing SKlearn

⌨️ () Plot a Graph

x = list(range(10))

⌨️ () Features and Labels_1

You made a mistake at , Labels are also inputs and not outputs

⌨️ () Save and Open a Model

⌨️ () Classification

⌨️ () Train Test Split

⌨️ () What is KNN

Sir, how to describe in scatterplot form if more than 2 features

instead of getting in to the file and adding the names of columns, one can use the following command

⌨️ () KNN Example

Giving Key Error @

Sir, what is difference between label enconder and mapping feature?

@ 'As you can see our model is pretty accurate'Well.... 50+ % of the data is labeled as 0, so simply guessing 0 would get us close to the accuracy of the model. The model is quite bad to be honest :DHowever, thank you for the video!

⌨️ () SVM Explained

SVM

At , it gets more textual with little insights from the speaker and adds no value! Sorry to say, but this video just gives information rather than explaining the science and logic behind it

⌨️ () SVM Example

At i am gettingValueError: bad input shape (30, 4) ....but you didn't got the error ... please help.....

⌨️ () Linear regression

⌨️ () Logistic vs linear regression

⌨️ () Kmeans and the math beind it

⌨️ () KMeans Example

⌨️ () Neural Network

⌨️ () Overfitting and Underfitting

⌨️ () Backpropagation

⌨️ () Cost Function and Gradient Descent

⌨️ () CNN

⌨️ () Handwritten Digits Recognizer

freeCodeCamp.org

Timetable

よく話題になっている単語

⌨️ () Overview

⌨️ () Indexing

Love the teaching style! at you mention that you've walked through the code previously. Is there another video to go with this one or did I miss something?

⌨️ () Retrieval

⌨️ () Generation

⌨️ () Query Translation (Multi-Query)

⌨️ () Query Translation (RAG Fusion)

⌨️ () Query Translation (Decomposition)

⌨️ () Query Translation (Step Back)

⌨️ () Query Translation (HyDE)

⌨️ () Routing

⌨️ () Query Construction

⌨️ () Indexing (Multi Representation)

⌨️ () Indexing (RAPTOR)

⌨️ () Indexing (ColBERT)

⌨️ () CRAG

⌨️ () Adaptive RAG

⌨️ () The future of RAG

⌨️ () Video Introduction

⌨️ () Introduction to Data Science and A/B Testing

⌨️ () Basics of A/B Testing in Data Science

⌨️ () Key Parameters of A/B Testing for Data Scientists

⌨️ () Formulating Hypotheses and Identifying Primary Metrics in Data Science A/B Testing

⌨️ () Designing an A/B Test: Data Science Approach

⌨️ () Resources for A/B Testing in Data Science

⌨️ () Analyzing A/B Test Results in Python: Data Science Techniques

⌨️ () Data Science Portfolio Project: Case Study with AB Testing

⌨️ () Reintroduction to A/B Testing in the Data Science Process

⌨️ () Data Science Techniques: Loading Data with Pandas for A/B Testing

⌨️ () Data Science Visualization: Using Matplotlib and Seaborn for A/B Test Click Data

⌨️ () Data Science Power Analysis: Understanding A/B Test Model Parameters

⌨️ () Data Science Calculations: Pooled Estimates and Variance for A/B Testing

⌨️ () Computing A/B Test P-Values: Data Science Methods for Statistical Significance

⌨️ () Practical Significance in A/B Testing: A Data Science Perspective

⌨️ () Conclusion: Wrapping Up A/B Testing in Data Science

⌨️ () Introduction

⌨️ () Create account

⌨️ () Create server

⌨️ () Install software

⌨️ () Configure Postgresql

⌨️ () Users and environment

⌨️ () Requirements.txt

⌨️ () Separate settings file

⌨️ () Introducing Git

⌨️ () Environmental variables

⌨️ () Overfitting and Underfitting（01:56:03 - 02:03:05）
Scikit-Learn Course - Machine Learning in Python Tutorial