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 - 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 Our members see more. Join us! ------------- Our members see more. Join us!