Python for Data Science Essential Training Part 2

Course details

Python for Data Science Essential Training is one of the most popular data science courses at LinkedIn Learning. It has now been updated and expanded to two parts—for even more hands-on experience with Python. In this course, instructor Lillian Pierson takes you step by step through a practical data science project: building machine learning models that can generate predictions and recommendations and automate routine tasks. Along the way, she shows how to perform linear and logistic regression, use K-means and hierarchal clustering, identify relationships between variables, and use other machine learning tools such as neural networks and Bayesian models. You should walk away from this training with hands-on coding experience that you can quickly apply to your own data science projects.

Learning objectives
Why use Python for data science
Machine learning 101
Linear regression
Logistic regression
Clustering models: K-means and hierarchal models
Dimension reduction methods
Association rules
Ensembles methods
Introduction to neural networks
Decision tree models

Concepts

0. Introduction

01 - Machine learning rocks
02 - What you should know

1. Introduction to Data Science

03 - Defining data science
04 - Why use Python for data science
05 - Where does AI fit in

2. Introduction to Machine Learning

06 - Machine learning 101
07 - Grouping machine learning algorithms

3. Regression Models

08 - Linear regression
09 - Multiple linear regression
10 - Logistic regression - Concepts
11 - Logistic regression - Data preparation
12 - Logistic regression - Treat missing values
13 - Logistic regression - Re-encode variables
14 - Logistic regression - Validating data set
15 - Logistic regression - Model deployment
16 - Logistic regression - Model evaluation
17 - Logistic regression - Test prediction

4. Clustering Models

18 - K-means method
19 - Hierarchical methods
20 - DBSCAN for outlier detection

5. Dimension Reduction Methods

21 - Explanatory factor analysis
22 - Principal component analysis (PCA)

6. Other Popular Machine Learning Methods

23 - Association rules models with Apriori
24 - Neural networks with a perceptron
25 - Instance-based learning with KNN
26 - Decision tree models with CART
27 - Bayesian models with Naive Bayes
28 - Ensemble models with random forests

Conclusion

29 - Next steps