1277kii / DI_git

Data Science Material

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)