**Designing a Machine Learning Model**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 3h 24m | 366 MB

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.

**Course Overview**

1 Course Overview

**Exploring Approaches to Machine Learning**

2 Module Overview

3 Prerequisites and Course Outline

4 A Case Study – Sentiment Analysis

5 Sentiment Analysis as a Binary Classification Problem

6 Rule Based vs. ML Based Analysis

7 Traditional Machine Learning Systems

8 Representation Machine Learning Systems

9 Deep Learning and Neural Networks

10 Traditional ML vs. Deep Learning

11 Traditional ML Algorithms and Neural Network Design

12 Module Summary

**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

**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

**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

**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

**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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