PLU | Designing A Machine Learning Model (2019 EN)

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  1. Kanka

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    Company: Pluralsight
    Author: Janani Ravi
    Full Title: Designing A Machine Learning Model
    Year: 2019
    Language: English
    Genre: Educational: Machine Learning
    Skill Level: Intermediate
    Price: -
    -
    Files: MP4 (+ Exercise Files, Slides .PDF)
    Time: 03:24:20
    Video: AVC, 1280 x 720 (1.778) at 29.000 fps, 200 kbps
    Audio: AAC at 96 Kbps, 2 channels, 48.0 KHz



    This course covers the important differences between various canonical problems in machine learning, as well as the considerations in choosing the right solution techniques, based on the specifics of the problem you are trying to solve and the data that you have available.

    As Machine Learning explodes in popularity, it is becoming ever more important to know precisely how to frame a machine learning model in a manner appropriate to the problem we are trying to solve, and the data that we have available. In this course, Designing a Machine Learning Model you will gain the ability to appropriately frame your use-case and then choose the right solution technique to model it. First, you will learn how rule-based systems and ML systems differ and how traditional and deep learning models work. Next, you will discover how supervised, unsupervised, and reinforcement learning techniques differ from each other. You will learn how classic supervised learning techniques such as regression and classification complement classic unsupervised techniques such as clustering and dimensionality reduction. You will then understand the assumptions and outcomes of these four classes of techniques and how solutions can be evaluated. Finally, you will round out your knowledge by designing end-to-end ML workflows for canonical ML problems, ensemble learning, and neural networks. When you’re finished with this course, you will have the skills and knowledge to identify the correct machine learning problem setup, and the appropriate solution technique for your use-case.


    Lessons:
    1. Course Overview
    01. Course Overview
    2. Exploring Approaches to Machine Learning
    02. Module Overview
    03. Prerequisites and Course Outline
    04. A Case Study: Sentiment Analysis
    05. Sentiment Analysis as a Binary Classification Problem
    06. Rule Based vs. ML Based Analysis
    07. Traditional Machine Learning Systems
    08. Representation Machine Learning Systems
    09. Deep Learning and Neural Networks
    10. Traditional ML vs. Deep Learning
    11. Traditional ML Algorithms and Neural Network Design
    12. Module Summary
    3. Choosing the Right Machine Learning Problem
    13. Module Overview
    14. Choosing the Right Machine Learning Problem
    15. Supervised and Unsupervised Learning
    16. Reinforcement Learning
    17. Recommendation Systems
    18. Module Summary
    4. Choosing the Right Machine Learning Solution
    19. Module Overview
    20. Regression Models
    21. Choosing Regression Algorithms
    22. Evaluating Regression Models
    23. Types of Classification
    24. Choosing Classification Algorithms
    25. Evaluating Classifiers
    26. Clustering Models
    27. The Curse of Dimensionality
    28. Dimensionality Reduction Techniques
    29. Module Summary
    5. Building Simple Machine Learning Solutions
    30. Module Overview
    31. Install and Set Up
    32. Exploring the Regression Dataset
    33. Simple Regression Using Analytical and Machine Learning Techniques
    34. Multiple Regression Using Analytical and Machine Learning Techniques
    35. Exploring the Classification Dataset
    36. Classification Using Logistic Regression
    37. Classification Using Decision Trees
    38. Clustering Using K-means
    39. Dimensionality Reduction Using Principal Component Analysis
    40. Dimensionality Reduction Using Manifold Learning
    41. Module Summary
    6. Designing Machine Learning Workflows
    42. Module Overview
    43. The Machine Learning Workflow
    44. Case Study: PyTorch on the Cloud
    45. Ensemble Learning
    46. Averaging and Boosting, Voting and Stacking
    47. Custom Neural Networks: Their Characteristics and Applications
    48. Module Summary
    7. Building Ensemble Solutions and Neural Network Solutions
    49. Module Overview
    50. Classification Using Hard Voting and Soft Voting
    51. Exploring and Preprocessing the Regression Dataset
    52. Regression Using Bagging and Pasting
    53. Regression Using Gradient Boosting
    54. Regression Using Neural Networks
    55. Summary and Further Study


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