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Machine Learning Fundamentals

Video Description

Use Python and scikit-learn to get up and running with the hottest developments in AI

About This Video

  • Explore scikit-learn uniform API and its application into any type of model
  • Understand the difference between supervised and unsupervised models
  • Learn the usage of machine learning through real-world examples

In Detail

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.

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Table of Contents

  1. Chapter 1 : Introduction to Scikit-Learn
    1. Course Overview 00:02:18
    2. Installation and Setup 00:04:01
    3. Lesson Overview 00:00:37
    4. Scikit-Learn 00:05:26
    5. Data Representation 00:04:24
    6. Data Preprocessing 00:16:45
    7. Scikit-Learn API 00:05:27
    8. Supervised and Unsupervised Learning 00:05:08
    9. Lesson Summary 00:00:28
  2. Chapter 2 : Unsupervised Learning: Real-Life Applications
    1. Lesson Overview 00:01:09
    2. Clustering 00:05:32
    3. Exploring a Dataset: Wholesale Customers Dataset 00:03:00
    4. Data Visualization 00:04:40
    5. k-means Algorithm 00:08:29
    6. Mean-Shift Algorithm 00:04:16
    7. DBSCAN Algorithm 00:03:26
    8. Evaluating the Performance of Clusters 00:04:33
    9. Lesson Summary 00:00:36
  3. Chapter 3 : Supervised Learning: Key Steps
    1. Lesson Overview 00:00:54
    2. Model Validation and Testing 00:13:35
    3. Evaluation Metrics 00:13:42
    4. Error Analysis 00:11:22
    5. Lesson Summary 00:00:34
  4. Chapter 4 : Supervised Learning Algorithms: Predict Annual Income
    1. Lesson Overview 00:01:12
    2. Exploring the Dataset 00:05:35
    3. Naïve Bayes Algorithm 00:05:18
    4. Decision Tree Algorithm 00:02:44
    5. Support Vector Machine Algorithm 00:04:17
    6. Error Analysis 00:06:48
    7. Lesson Summary 00:00:53
  5. Chapter 5 : Artificial Neural Networks: Predict Annual Income
    1. Lesson Overview 00:01:13
    2. Artificial Neural Networks 00:15:34
    3. Applying an Artificial Neural Network 00:03:27
    4. Performance Analysis 00:10:45
    5. Lesson Summary 00:00:29
  6. Chapter 6 : Building Your Own Program
    1. Lesson Overview 00:00:56
    2. Program Definition 00:09:29
    3. Saving and Loading a Trained Model 00:05:06
    4. Interacting with a Trained Model 00:03:03
    5. Lesson Summary 00:01:34