Python for Data Science and Machine Learning Essential Training Part 2

5h 16mIntermediate2024-07-30

Authors

Lillian Pierson, P.E.

Engineer, CEO, and Head of Product at Data-Mania

Course details

If you are a working professional who wants to use business data to make improved decisions through predictive analytics, this course can help you. Lillian Pierson—engineer, CEO, and the head of product at Data-Mania—guides you through a robust combination of basic data science coding experience, demonstrations, challenges, solutions, and exercises that you can quickly apply in customized data analyses and analytics projects. Learn best practices for data cleaning, data visualization, data analysis, and Python programming.

By the end of the course, you will be able to use Python to:

Clean, reshape, reformat, and describe data
Generate data visualizations for data presentation and visual exploratory analysis
Identify and remove outliers
Perform simple data analysis
Source, scape, and analyze data from the internet
Generate collaborative analytics assets using Plot.ly

Skills covered

Data Science FoundationsMachine LearningPythonEssential TrainingArtificial Intelligence (AI)Programming LanguagesData ScienceOpen SourceSoftware Development

Concepts

Introduction

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

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

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

Clustering Models

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

Dimension Reduction Methods

Explanatory factor analysis
Principal component analysis (PCA)

Other Popular Machine Learning Methods

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

Getting Started with Natural Language Processing

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

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