**Machine Learning Fundamentals**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 3h 18m | 525 MB

With the flexibility and features of scikit-learn and Python, build machine learning algorithms that optimize the programming process and take application performance to a whole new level.

You’ll begin by learning how to use the syntax of scikit-learn. You’ll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You’ll apply unsupervised clustering algorithm over 1990 US Census dataset, to discover patterns and profiles, and explore the process to solve a supervised machine learning problem. Then, the focus of the course shifts to supervised learning algorithms. You’ll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You’ll also learn how to perform coherent result analysis to improve performance of the algorithm by tuning hyperparameters. When it finishes, this course would have given you the skills and confidence to start programming machine learning algorithms.

What You Will Learn

- Understand the importance of data representation
- Gain insight into the difference between supervised and unsupervised models
- Explore the data using the Matplotlib library
- Study popular algorithms, such as K-means, Gaussian Mixture, and Birch
- Implement a confusion matrix using scikit-learn
- Study popular algorithms, such as Naïve-Bayes, Decision Tree, and SVM
- Visualize errors in various models using matplotlib

01 Course Overview

02 Installation and Setup

03 Lesson Overview

04 Scikit-Learn

05 Data Representation

06 Data Preprocessing

07 Scikit-Learn API

08 Supervised and Unsupervised Learning

09 Lesson Summary

10 Lesson Overview

11 Clustering

12 Exploring a Dataset – Wholesale Customers Dataset

13 Data Visualization

14 k-means Algorithm

15 Mean-Shift Algorithm

16 DBSCAN Algorithm

17 Evaluating the Performance of Clusters

18 Lesson Summary

19 Lesson Overview

20 Model Validation and Testing

21 Evaluation Metrics

22 Error Analysis

23 Lesson Summary

24 Lesson Overview

25 Exploring the Dataset

26 Naïve Bayes Algorithm

27 Decision Tree Algorithm

28 Support Vector Machine Algorithm

29 Error Analysis

30 Lesson Summary

31 Lesson Overview

32 Artificial Neural Networks

33 Applying an Artificial Neural Network

34 Performance Analysis

35 Lesson Summary

36 Lesson Overview

37 Program Definition

38 Saving and Loading a Trained Model

39 Interacting with a Trained Model

40 Lesson Summary

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