intro_data_science

Introduction to Data Science

Table of contents

• Lesson 1
  • Course introduction
  • NumPy
• Lesson 2:
  • Pandas
  • Exploration of the Titanic dataset
• Lesson 3:
  • Loss function
  • Gradient descent
  • Linear regression
  • scikit-learn model interface
• Lesson 4:
  • Categorical features encoding
  • Exploratory data analysis
  • Regression on real data
  • K-folds validation
• Lesson 5
  • Logistic Regression algorithm
  • Titanic survivors classification
  • Advanced visualization with seaborn
  • Feature engineering
• Lesson 6
  • Text count vectorization
  • Text TFIDF vectorization
  • Text manipulation using pandas
  • Sentiment analysis on movie reviews
  • Trained model analysis
• Lesson 7
  • KMeans clustering
  • Principal component analysis
  • t-distributed stochastic neighbor embedding
  • Clutering and dimensionality reduction on text
• Lesson 8
  • Decision Trees
  • Random Forests
  • Model Ensembles
  • Nearest Neighbors algorithms
  • Locally sensitive hashing
  • Support Vector Machines
  • Neural Networks

References

• Machine learning crash course by Google
• Python Data Science Handbook by Jake VanderPlas