/MachineLearning-A2Z

ML Roadmap 🛣️📌⏳

Primary LanguageJupyter Notebook

❄️❄️❄️❄️❄️❄️❄️❄️❄️❄️ 👉 Roadmap for Machine Learning 👈 ❄️❄️❄️❄️❄️❄️❄️❄️❄️❄️

The purpose of this repo is to keep all my learning/practicing material at one place. This helps me for future reference and also provides a ROADMAP for newbies breaking into the field.

ENJOY!

Regression

  • SimpleLinearRegression
  • MultipleLinearRegression [Backward elimination/Recursive feature elimination]
  • PolynomialRegression
  • SVR [non-linear SVR with Radial Basis Function]
  • DecisionTreeRegression
  • RandomForestRegression

Classification

  • LogisticRegression
  • KNN
  • SVM
  • KernelSVM
  • NaiveBayes
  • DecisionTreeClassification
  • RandomForestClassification

Clustering

  • K-Means
  • Hierarchical

Association Rule Learning

  • Apriori
  • Eclat

Reinforcement Learning

  • UCB
  • ThompsonSampling

Natural Language Processing

  • BagOfWords - SentimentAnalysis

DimensionalityReduction

  • PCA
  • LDA
  • KernelPCA

Deep learning - Supervised [Tensorflow2 & keras]

  • Artificial Neural Networks/ANN
  • Convolutional Neural Networks/CNN
  • Recurrent Neural Networks/RNN

Deep learning - Unsupervised

  • Self Organizing Maps/SOM
  • Boltzmann Machines (Recommender system)
  • AutoEncoders (Recommender system)