DevFest-19-GDG-Settat

Slides and jupyter notebook tutorial on Machine Learning and Data Science introduction.

Presenetation: Applicative Data Science & TensorFlow 2.0

Notebook Contents:

1. Objectives
2. Machine Learning
3. Types of Learning
   1. Supervised Learnojg
   2. Unsupervised Learning
   3. Reinforcment Learning
   4. Regression and Classification
4. EDA: Exploratory Data Analysis
   1. Load Data
   2. Collect general information
   3. Data Visualisation
   4. Correlation Matirx
   5. More on EDA
5. On to Modelling
   1. Split Data
   2. K-NN Algorithm
   3. Evaluating Model Performences
   4. Define Euclidian Distance
   5. 1NN with Euclidian Distance
   6. 3NN with Euclidian Distance
   7. General KNN with Different Distances
6. Comparing with Sklearn
7. Summary
8. Where to Go Next?

Ressources:

• Dive into Deep Learning
• SuperDataScience Podcast with Dr Alex Antic
• Is moedl.fit() enough? a presentation by "Aakash Nain"
• Google's Machine learning Crash Course
• Bloomberg Foundations of Machine Learning

BY: Elamraoui Sohayb,