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

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  1. Kanka

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    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.

    ✓ 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

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

    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