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di_git's Introduction

Introduction to ML

  • Lets keep the class interactive
  • Ask questions anytime

3 Days plan

  1. Overview / Python / Pandas
  2. Pandas/ Data visualization / ML Algorithms
  3. More ML Algorithms / Deep Learning

OverView

  • AI vs. RPA
  • Machine learning
  • Machine Reasoning
  • AWS Stack
    • S3
    • SageMaker
  • Applications
  • Databases
    • Relational
    • Graph
      • Neo4j

Python Basics

  • Variables
  • Flow Control
  • Functions

Data Cleaning and Manipulation

Pandas

  • Reading excel files
  • Merging / Grouping
  • Apply

Visualization

  • MatplotLib
  • Seaborn

ML Concepts

  • Variance / Bias
  • Overfitting / Underfitting
  • Regularization
  • Performance Metrics
  • Test Train Split
  • Hyper Parameter Tuning

ML Algorithms

  • Linear Regression
  • Logistic Regression
  • KNN
  • Naive Bayes
  • Decision Trees
  • Random Forest
  • SVM
  • PCA
  • Deep Learning
  • NLP
  • Time Series

Hands_On Excercises / Challenges

For each section there will be individual/group excercises

  • Spam Detection Challenge

Setting up

You'll be provided a username and password to login into AWS, we will use SageMaker as main development environemnt. (No need for any installations on your local machine)

di_git's People

Contributors

mjghorbany avatar

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