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