Machine Learning Crash Course using Python

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Tatiana Petrache
Machine Learning Crash Course using Python

Acest curs este predat în limba română, iar materialele sunt în limba engleză şi/sau în limba română, după caz.

La cerere, cursul poate fi personalizat.

Why Python? Because simplicity is the best, we want to focus on understanding the building blocks of Machine Learning with the shortest learning curve. Python comes equipped with a collection of pre-built packages and is the to-go language for fast prototyping.

All the concepts below will be combined with programming exercises based on real datasets and use cases which will enable you to have a clear and gradual understanding of Machine Learning. We will provide you with enough information and examples so that after finising this course you can confidently write your own Machine Learning applications

Caracteristici curs

  • Capitole 15
  • Durata 2 zile
  • Nivel de cunostinte Orice nivel
  • Limba Romana
  • Studenti 12
  • Day 1: Intro to Machine Learning

    • Capitol 1.1 Basic terminology and concepts Locked 0m
    • Capitol 1.2 Linear Regression: A Visual Understanding Locked 0m
    • Capitol 1.3 Minimizing Loss with Gradient Descent: A simple, visual approach Locked 0m
    • Capitol 1.4 First Steps with Gradient Descents: A coding approach Locked 0m
    • Capitol 1.5 Train, Test & Validation: How to split your data and why Locked 0m
    • Capitol 1.6 Feature Engineering: Trying to make your model smarter Locked 0m
    • Capitol 1.7 Underfitting versus Overfitting: Simple model versus complex model Locked 0m
    • Capitol 1.8 Key take-aways for the day Locked 0m
  • Day 2: Logistic Regression

    • Capitol 2.1 A Visual Understanding Locked 0m
    • Capitol 2.2 Binary Classification: Tuning the “decision threshold” Locked 0m
    • Capitol 2.3 Evaluation metrics: Precison & Recall Locked 0m
    • Capitol 2.4 Understand Prediction Bias: Possible Root Causes Locked 0m
    • Capitol 2.5 Optimizing Predictions: Regularization in high dimensional space Locked 0m
    • Capitol 2.6 From Logistic Regression to Neural Nets: Build your intuition Locked 0m
    • Capitol 2.7 Key take-aways for the day Locked 0m
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Tatiana Petrache