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Programming Probabilistically: An introduction to the world of data science

By

Eric Schles

Hello and welcome to my book! You'll find the following sections:

  1. Descriptive Statistics and Hypothesis testing
  2. Linear Models
  3. Entropy and Tree Models
  4. Neural Network Models

Each section covers about 4 to 5 chapters worth of materail broken out into:

  • Basics
  • Mathematical Intuition
  • Implementation
  • Typical API
  • Advanced Use Cases

Sections to come:

  • Reinforcement Learning
  • Engineering for Data Science
  • Text Processing
  • Image Processing
  • Support Vector Machines
  • Genetic Algorithms
  • Recommender Systems
  • A/B testing and other related workflows
  • SQL best practice
  • Timeseries Forecasting and Analysis
  • Geospatial Analysis
  • Geospatial and Timeseries forecasting
  • Video Processing
  • Building Data Dashboards
  • Working With Search
  • Building An OCR System
  • Advanced Python Usage
  • Active Learning

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