airblockdata / machine-learning

About

This repository is a related to all about Machine Learning - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python), Feature Selection technique in python etc. Follow Coursesteach for more content 😊

                          https://coursesteach.com/

Course 01 - 📚🧑‍🎓📺 Machine Learning

📚Chapter: 1 - Introduction

• Why we used AI(ML Part1)
• What is machine learning?(ML Part2)
• Types of Machine Learning?(ML Part3)
• Steps involved in Building a Machine Learning Model
• Best Free Resources to Learn Machine Learning

📚Chapter: 2 - Linear Regression with one Variable

• Simple Linear Regression using sklearn(Lab1))
• Simple Linear Regression with python-Andrew(Lab2)
• Understanding the Linear Regression Cost Function(Tutorial)
• What the cost function is doing?(Tutorial)
• Understanding Gradient Descent: A Powerful Optimization Algorithm
• Gradient Descent For Linear Regression

📚Chapter: 3 - Linear Algebra

• Understanding Matrices and Vectors in Linear Algebra
• Understanding Addition and Scalar Multiplication of Matrices

Course 02 -📚🧑‍🎓Unsupervised Learning with scikit_learn

• Anomaly_Detection
• BIRCH Clustering in Machine Learning
• Anomaly_Detection_with_Isolation_Forest_algorithm
• Kmean
• Unsupervised_learning
• DBSCAN Clustering in Machine Learning
• Clus-K-Means-Customer-Seg-py-v1.ipynb
• Clus-Hierarchical-Cars-py-v1.ipynb
• Clus-DBSCN-weather-py-v1.ipynb
• Hierarchical Clustering-Agglomerative method

Module 01 - ** Please visit my Medium articles **

• Introduction to Artificial Intelligence (AI)
• Understanding the Basics of Machine Learning

Course 03 - 📚🧑‍🎓📺Supervised Learning with scikit_learn

📚Chapter:1-Classification|Code|

• Classification (Supervised Learning)-Tutorial
• Classification using Scikit-Learn-Tutorial

📚Chapter:2-Regression|Code|

• A Comprehensive Guide to Regression in scikit-learn
• Bagging_&_Random_Forests
• Reg-Mulitple-Linear-Regression-Co2-py-v1.ipynb
• KNN with Python
• Build Machine Learning Pipelines
• Simple_Linear_Regression_using_scikit_learn
• Linear_Regression_Andrew
• Supervised_(Classification)_ML_Model_Training_and_Evulation
• Reg-NoneLinearRegression-py-v1.ipynb
• Reg-Polynomial-Regression-Co2-py-v1.ipynb
• Reg-Simple-Linear-Regression-Co2-py-v1.ipynb
• Clas-Decision-Trees-drug-py-v1.ipynb
• Clas-K-Nearest-neighbors-CustCat-py-v1.ipynb
• Voting_Classifiers.ipynb
• Perceptron in Machine Learning
• Decision_Trees
• Linear_Regression
• XGBoost_in_Machine_Learning.ipynb
• Model_Evaluation_&_Scoring_Matrices
• Naive Bayes Algorithm in Machine Learning
• Naive_Bayes
• Nerual Networks
• Supervised_learning_with_Sklearn
• PyCaret in Machine Learning

• Association Mining
  • Apriori_Algorithm

Module 03 - Preprocessing with scikit_learn

• Data_Processing_in_Python_.ipynb
• Upload_Dataset_from_github_to_Colab.ipynb
• Feature_Selection
• Create_new_Features_(Faker)
• Give_Columns_name_to_dataset_(resize)_using_Python
• StandardScaler in Machine Learning
• Creating_artificial_datasets.ipynb
• Data_representation_in_scikit_learn.ipynb

Module 04 - Anomaly Detection

• Anomaly_Detection.ipynb
• Anomaly_Detection_using_Using_Python library.ipynb

Module -Recommendation System

• Collaborative-Filtering
• Content-Based

Module 04 - Model Evaluation with scikit_learn

• Bias and Variance using Python
• hyperparameter_tuning.ipynb
• What_is_Cross_Validation_in_Machine_Learning_.ipynb
• Scikit_Plot_Visualizing_Machine_Learning_Algorithm_Results_&_Performance (1).ipynb

Module 05 - Data Visualization and Exploratoration with scikit_learn

• Automate_Exploratory_Data_Analysis
• Data_Exploratory_and_Ploting

Module 06 - Statistics

• Statistics_for_Machine_Learning

Module 07 - Machine Learning with Pycaret

• Classification
• Regression
• Clustering
• Anomaly Detection

• Deep learning library
  • FastAI_in_Machine_Learning.ipynb

Module 07 - [Distance Measure ]

• Distance Measure

Module 06 - Model Need to implement

• 50 Machine Learning Algorithms Explained using Python
• Akramz / Hands-on-Machine-Learning-with-Scikit-Learn-Keras-and-TensorFlow Public
• Data Cleaning with Python
• 70+ Machine Learning Algorithms & Models Explained with Python
• Interpreting Tree-Based Model's Prediction of Individual Sample
• Predicting presence of Heart Diseases using Machine Learning
• How to Master Scikit-learn for Data Science
• All Machine Learning Algorithms & Models Explained
• Python AI: How to Build a Neural Network & Make Predictions
• 60 Machine Learning Algorithms & Models Explained with Python
• ageron/handson-ml2
• All Machine Learning Algorithms & Models with Python
• How to Master Scikit-learn for Data Science
• rushter/MLAlgorithms
• 80+ Machine Learning Algorithms & Models Explained with Python
• 5x12themlsbook
• edyoda data-science-complete-tutorial
• ageron handson-ml Public

Contributors

We would love your help in making this repository even better! If you know of an amazing AI course that isn't listed here, or if you have any suggestions for improvement in any course content, feel free to open an issue or submit a course contribution request.

                   Together, let's make this the best AI learning hub website! 🚀

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