9.2 Scikit learn Introduction смотреть онлайн

This is the Section 9 which is going to describe Scikit - learn and Data Visualization in details. Currently you are in Section 9: and Briefly describe sections are:-

Data : https://github.com/gitShubhamSingh/data

Section 1: Introduction
Section 2: Machine Learning 101 (End to End)
Section 3: Machine Learning and Data Science Framework
Section 4: The 2 Paths
Section 5: Data Science Environment Setup
Section 6: Pandas Data Analysis
Section 7: Numpy
Section 8: Matplotlib Plotting and Data Visualization
Section 9: Scikit-learn Creating Machine Learning Model
Section 10: Supervised Learning Classification + Regression
Section 11: Project 1 Supervised Learning ( Classification )
Section 12: Project 2 Supervised Learning ( Time Series Data - Regression)
Section 13: Data Engineering
Section 14: Neural Network, Deep Learning, Transfer Learning, TensorFlow
Section 15: Story Telling + Communication How to present your work
Section 16: Career Advice + Extra Bits
Section 17: Learn Python
Section 18: Learn Python Part 2
Section 19: Bonus Learn Advance Statistics and Mathematics
Section 20: Where to Go from Here
Section 21: Bonus Section

Get Excited, Let's Start...


github : https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/section-2-data-science-and-ml-tools/introduction-to-matplotlib-video.ipynb

scikit-learn machine learning map : https://scikit-learn.org/stable/tutorial/machine_learning_map/index.html

scikit-learn github : https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/section-2-data-science-and-ml-tools/introduction-to-scikit-learn-video.ipynb

scikit-learn-documentation: https://scikit-learn.org/stable/user_guide.html

introduction-to-scikit-learn-github: https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/section-2-data-science-and-ml-tools/introduction-to-scikit-learn.ipynb

scikit-learn-workflow : https://github.com/mrdbourke/zero-to-mastery-ml/blob/master/section-2-data-science-and-ml-tools/scikit-learn-workflow-example.ipynb

sklearn-min-max scalar : https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html

sklearn-standered scalar : https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html

Feature Scaling for machine learning : https://www.analyticsvidhya.com/blog/2020/04/feature-scaling-machine-learning-normalization-standardization/

Feature Scaling with scikit-learn : https://benalexkeen.com/feature-scaling-with-scikit-learn/

Feature Scaling why it is required : https://rahul-saini.medium.com/feature-scaling-why-it-is-required-8a93df1af310

Filling Missing Data with scikit-learn colab: https://colab.research.google.com/drive/162DyoCBFeufMjJI7n4XCiYpdqIdktZw_

ROC and AUC Curve : https://www.youtube.com/watch?v=4jRBRDbJemM

Recieve Operating Characterstics (ROC) : https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html

Google Machine Learning (RAC and AUC) : https://developers.google.com/machine-learning/crash-course/classification/roc-and-auc