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This repository is a related to all about C++ - an A-Z guide to the world of C++. This supplement contains the implementation of C++ topic (in Colab). Follow Coursesteach for more content
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| Tutorial | Video | Video |
|---|---|---|
| 1- Why we used AI | Content 2 | Content 3 |
| 2- What is machine learning?) | Video1-Video2 | Content 6 |
| 3-Types of Machine Learning?(ML Part3) | Video1-Video2 | --- |
| 4-Steps involved in Building a Machine Learning Model | --- | --- |
| 5-Best Free Resources to Learn Machine Learning | --- | --- |
| Tutorial | Video | Code |
|---|---|---|
| Model Representation | Video1 | --- |
| 1-Simple Linear Regression using sklearn(Lab1) |  |
|
| 2-Simple Linear Regression with python-Andrew | --- | |
| Understanding the Linear Regression Cost Function(Tutorial) | Video | --- |
| What the cost function is doing?(Tutorial) | Video | --- |
| Understanding Gradient Descent: A Powerful Optimization Algorithm | Video-Video1 | --- |
| Gradient Descent For Linear Regression | Video | --- |
- 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
| Tutorial | Video | Code |
|---|---|---|
| Classification (Supervised Learning)-Tutorial | Video1-Video2 | |
| Classification using Scikit-Learn-Tutorial | Video | |
| Tutorial | Video | Code |
|---|---|---|
| Regression in scikit-learn | Video1Video2 | |
| Tutorial | Video | Code |
|---|---|---|
| 1-Preprocessing in Machine Learning | Video1 |
- 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
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
Module -Recommendation System
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 06 - Statistics
Module 07 - Machine Learning with Pycaret
- 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
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