This is a collection of Jupyter Notebookes, for getting started in machne learning, using one of (if not) the most popular machine learning library, Scikit-learn. It would be a good starting point for the beginniers, for trying out various elementary machine learning techniques.

• Tutorial 1: Data Preprocessing
• Tutorial 2: Simple Linear Regression
• Tutorial 3: Data Preprocessing
• Tutorial 4: Polynomial Linear Regression
• Tutorial 5: Support vector regression
• Tutorial 6: Decision Tree regression
• Tutorial 7: Random Forest Regression

• Tutorial 8: Logistic Regression
• Tutorial 9: K-Nearest Neighbors
• Tutorial 10: Support Vector Classifier
• Tutorial 11: Naive Bayes Classifier
• Tutorial 12: Decision Tree Classifier
• Tutorial 13: Random Forest Classifier
• Tutorial 14: K-Means Clustering
• Tutorial 15: Hierarchical Clustering
• Tutorial 16: Apriori(Associate Rule Learning)

• Tutorial 17: Random Selection
• Tutorial 18: Upper Confidence Bound
• Tutorial 19: Thompson Sampling

• Tutorial 20: Bag Of Words model

• Tutorial 21: Artificial Neural Network

• Tutorial 22: Convolution Neural Network

• Tutorial 23: Principle Component Analysis (PCA)
• Tutorial 24: Linear Discriminant Analysis(LDA)
• Tutorial 25: Kernel PCA

• Tutorial 26: K-Fold Cross Validation
• Tutorial 27: Grid Search

• Tutorial 28: XGBoost

• Beginniers can start their open source contribution journey, from this repository.
• Description of each cell block will be added. Might that be theory, or explanation of code.
• More notebooks will be added on each category (specially CNN, NL, NLP, Boosting)
• More categories might also be added.
• Datasets for each notebook will be provided(downloadable from each notebook).
• Colab link of every notebok will be added on Readme

Base code of these notebooks are from this amazing Udemy Machine Learning A-Z course .