English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 3h 44m | 430 MB
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.
Topics include:
- 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
Table of Contents
1 Machine learning rocks
2 What you should know
3 Defining data science
4 Why use Python for data science
5 Where does AI fit in
6 Machine learning 101
7 Grouping machine learning algorithms
8 Linear regression
9 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
18 K-means method
19 Hierarchical methods
20 DBSCAN for outlier detection
21 Explanatory factor analysis
22 Principal component analysis (PCA)
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
29 Next steps
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