Python for Data Science Essential Training Part 1

In the second half of this two-part course, explore the essentials of using Python for data science and machine learning.

Introduction

• Data science life hacks
• What you should know
• How to use Codespaces in this course

1. Introduction to Machine Learning

• Defining data science
• Seeing where machine learning fits in
• Machine learning AI foundations
• Grouping machine learning algorithms
• High-level machine learning roadmap

2. Regression Models

• Linear regression
• Multiple linear regression
• Logistic regression: Concepts
• Logistic regression: Data preparation
• Logistic regression: Treat missing values
• Logistic regression: Re-encode variable
• Logistic regression: Validating dataset
• Logistic regression: Model deployment
• Logistic regression: Model evaluation
• Logistic regression: Test prediction

3. Clustering Models

• Cluster analysis with the K-means method
• Hierarchical cluster analysis
• DBSCAN for outlier detection

4. Dimension Reduction Methods

• Explanatory factor analysis
• Principal component analysis (PCA)

5. Other Popular Machine Learning Methods

• Association rules models with the Apriori algorithm
• Instance-based learning with KNN
• Decision trees with CART
• Bayesian statistics with Naïve Bayes
• Ensemble learning with random forest
• Neural networks with perceptrons
• Building a neural network

6. Getting Started with Natural Language Processing

• Introduction to natural language processing (NLP)
• Cleaning and stemming textual data
• Lemmatizing and analyzing textual data

7. Getting Started with Generative AI Models

• Introduction to generative AI
• Deep dive into generative AI models
• Keeping up with AI developments
• Coding demo: Implementing a generative AI model

Conclusion

• Next steps and additional resources