You can jump even deeper into some complex issues like oversampling, data cleaning but the point is how can you take data and immediately apply it.

This is the basic ML classification, regression, unsupervised learning methods, Reinforcement Learning, NLP and Deep Learning.

Data Scientist and a Machine Learning expert. I share my knowledge by simplifying all my learning, algorithms, and coding libraries.

The codes can be viewed in R and Python.

Part 1 - Data Preprocessing

Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression

Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

Part 4 - Clustering: K-Means, Hierarchical Clustering

Part 5 - Association Rule Learning: Apriori, Eclat

Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling

Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP

Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks.
here is the dataset for Deep Learning -> https://drive.google.com/drive/folders/1y4YQ-rnelgfuEKhXQbaP8UIrmlwI-nXv?usp=drive_link

Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA

Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

There are also notes that are gathered in the link below:

https://www.notion.so/Machine-Learning-By-Kirill-Eremenko-Hadelin-de-Ponteves-Revision-21d967a6fbb7435ebce452bf6cc0a8d0?pvs=4

• Anyone interested in Machine Learning.
• Students who have at least high school knowledge in math and who want to start learning Machine Learning.
• Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
• Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
• Any students in college who want to start a career in Data Science.
• Any data analysts who want to level up in Machine Learning.
• Any people who are not satisfied with their job and who want to become a Data Scientist.
• Any people who want to create added value to their business by using powerful Machine Learning tools.