PAC | Machine Learning 101 With Scikit-Learn And StatsModels (2019 EN)

Company: Packt Publishing
Author: 365 Careers
Full Title: Machine Learning 101 With Scikit-Learn And StatsModels
Year: 2019
Language: English
Genre: Educational: Machine Learning
Skill Level: -
Price: €183.99
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Files: MP4 (+ Exercise Files)
Time: 05:12:43
Video: AVC, 1280 x 720 (1.778) at 30.000 fps, 250 kbps
Audio: AAC at 318 Kbps, 2 channels, 44.1 KHz



New to machine learning? This is the place to start: Linear regression, Logistic regression, and Cluster Analysis

This course will provide you with solid Machine Learning knowledge to help you reach your dream job destination.

Machine Learning is one of the fundamental skills you need to become a data scientist. It’s the steppingstone that will help you understand deep learning and modern data analysis techniques.

In this course, we’ll explore the three most fundamental machine learning topics such as Linear regression, Logistic regression and Cluster analysis. Even neural networks geeks (like us) can’t help but admit that it’s these three simple methods that data science revolves around. So, in this course, we make otherwise complex subject matter easy to understand and apply in practice.

Of course, there’s only one way to teach these skills in the context of data science—to accompany statistics theory with a practical application of these quantitative methods in Python. And that’s precisely what we are after. Theory and practice go hand in hand here.

We’ve developed this course with not one but two machine learning libraries: StatsModels and sklearn. This is a course you’ll be eager to complete.


Learn:
✓ You will gain confidence when working with two of the leading ML packages: statsmodels and sklearn
✓ You will learn how to perform a linear regression
✓ You will become familiar with the ins and outs of logistic regression
✓ You will excel at carrying out cluster analysis (both flat and hierarchical)
✓ You will learn how to apply your skills to real-life business cases
✓ You will be able to comprehend the underlying ideas behind ML models

Features:
✓ Learn Machine Learning with StatsModels and Sklearn
✓ Apply skills to real-life business cases
✓ Learn Linear Regression, Logistic Regression, and Cluster Analysis


Lessons:
1. Introduction
01. What Does the Course Cover
2. Setting Up the Working Environment
02. Setting Up the Environment - An Introduction (Do Not Skip, Please)!
03. Why Python and Why Jupyter?
04. Installing Anaconda
05. The Jupyter Dashboard - Part 1
06. The Jupyter Dashboard - Part 2
07. Installing sklearn
3. Linear Regression with StatsModels
08. Introduction to Regression Analysis
09. The Linear Regression Model
10. Correlation vs Regression
11. Geometrical Representation
12. Python Packages Installation
13. Simple Linear Regression in Python
14. What is Seaborn?
15. What Does the StatsModels Summary Regression Table Tell us?
16. SST, SSR, and SSE
17. The Ordinary Least Squares (OLS)
18. Goodness of Fit: The R-Squared
19. The Multiple Linear Regression Model
20. Adjusted R-Squared
21. F-Statistic and F-Test for a Linear Regression
22. Assumptions of the OLS Framework
23. A1: Linearity
24. A2: No Endogeneity
25. A3: Normality and Homoscedasticity
26. A4: No Autocorrelation
27. A5: No Multicollinearity
28. Dealing with Categorical Data
29. Making Predictions
4. Linear Regression with Sklearn
30. What is sklearn
31. Game Plan for sklearn
32. Simple Linear Regression with sklearn
33. Simple Linear Regression with sklearn - Summary Table
34. Multiple Linear Regression with sklearn
35. Adjusted R-Squared
36. Feature Selection through p-values (F-regression)
37. Creating a Summary Table with the p-values
38. Feature Scaling
39. Feature Selection through Standardization
40. Making Predictions with Standardized Coefficients
41. Underfitting and Overfitting
42. Training and Testing
5. Linear Regression - Practical Example
43. Practical Example (Part 1)
44. Practical Example (Part 2)
45. Practical Example (Part 3)
46. Practical Example (Part 4)
47. Practical Example (Part 5)
6. Logistic Regression
48. Introduction to Logistic Regression
49. A Simple Example of a Logistic Regression in Python
50. What is the Difference Between a Logistic and a Logit Function?
51. Your First Logistic Regression
52. A Coding Tip (optional)
53. Going through the Regression Summary Table
54. Interpreting the Odds Ratio
55. Dummies in a Logistic Regression
56. Assessing the Accuracy of a Classification Model
57. Underfitting and Overfitting
58. Testing our Model and Bulding a Confusion Matrix
7. Cluster Analysis
59. Introduction to Cluster Analysis.mp4
60. Examples of Clustering
61. Classification vs Clustering
62. Math Concepts Needed to Proceed
63. K-Means Clustering
64. A Hands-on Example of K-Means
65. Categorical Data in Cluster Analysis
66. The Elbow Method or How to Choose the Number of Clusters
67. Pros and Cons of K-Means
68. Standardization of Features when Clustering
69. Cluster Analysis and Regression Analysis
70. Practical Example: Market Segmentation (Part 1)
71. Practical Example: Market Segmentation (Part 2)
72. What Can be Done with Cluster Analysis?
8. Cluster Analysis: Additional Topics
73. Other Types of Clustering
74. The Dendrogram
75. Heatmaps

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