Machine Learning A-Z [WIP]

Machine Learning A-Z™: Hands-On Python & R In Data Science
by Kirill Eremenko and Hadelin de Ponteves on Udemy

Actually a couple of new jobs and task at work and obviously major curiosity have led me to have a dive into Machine Learning. Guess what, I absolutely love it. It’s fantastic to get your head around the theory, but also try out some simple examples of applied ML – even if it is just to understand when people talk about it. Actually applying it to own real world problems is of course a different story, but this is a first step for me. It all boils down to ask the right question and to understand what the data might be able to tell you, rather then using Machine Learning for the sake of it.
I highly recommend the course, join me if you’d like!

This is the table of content as described on Udemy:

Part 1 – Data Preprocessing
Part 2 – Regression:

  • Simple Linear Regression
  • Multiple Linear Regression
  • Polynomial RegressionSVR
  • Decision Tree Regression
  • Random Forest Regression

Part 3 – Classification:

  • Logistic Regression
  • K-NN
  • SVM
  • Kernel SVM
  • Naive Bayes
  • Decision Tree Clssification
  • 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

Part 9 – Dimensionality Reduction:

  • PCA
  • LDA
  • Kernel PCA

Part 10 – Model Selection & Boosting:

  • k-fold Cross Validation
  • Parameter Tuning
  • Grid Search
  • XGBoost