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Supervised and Unsupervised Learning with Python

Video Description

Hop on the wonderful journey of machine learning and data analysis

About This Video

  • Explore real-world scenarios in machine learning
  • Learn the important aspects of supervised and unsupervised learning
  • Get a jumpstart on Artificial Intelligence with this introductory course

In Detail

Build real-world Artificial Intelligence (AI) applications to intelligently interact with the world around you, explore real-world scenarios, and learn about the various algorithms that can be used to build AI applications. Packed with insightful examples and topics such as predictive analytics and deep learning, this course is a must-have for Python developers.

Table of Contents

  1. Chapter 1 : Introduction to Artificial Intelligence 7
    1. The Course Overview 00:03:02
    2. Artificial Intelligence and Its Need 00:03:47
    3. Applications and Branches of AI 00:04:48
    4. Defining Intelligence Using Turing Test 00:01:57
    5. Making Machines Think Like Humans 00:03:56
    6. General Problem Solver 00:02:20
    7. Building an Intelligent Agent 00:02:12
    8. Installing Python 3 and Packages 00:02:13
    9. Loading Data 00:02:11
  2. Chapter 2 : Classification and Regression Using Supervised Learning
    1. Supervised Versus Unsupervised Learning 00:02:57
    2. What is Classification? 00:02:10
    3. Preprocessing Data 00:04:14
    4. Label Encoding 00:01:40
    5. Logistic Regression and Naïve Bayes Classifier 00:07:15
    6. Confusion Matrix 00:02:57
    7. Support Vector Machines 00:01:47
    8. Classifying Income Data 00:03:34
    9. What is Regression? 00:02:10
    10. Building a Single and Multivariable Regressor 00:03:45
    11. Estimating Housing Prices 00:02:44
  3. Chapter 3 : Predictive Analytics with Ensemble Learning
    1. What is Ensemble Learning? 00:03:18
    2. What Are Decision Trees 00:04:24
    3. What are Random and Extremely Random Forests? 00:06:20
    4. Dealing with Class Imbalance 00:03:31
    5. Finding Optimal Training Parameters 00:02:26
    6. Computing Relative Feature Importance 00:02:43
    7. Predicting Traffic 00:03:39
  4. Chapter 4 : Detecting Patterns with Unsupervised Learning
    1. Clustering Data with K-Means Algorithm 00:05:55
    2. Estimating the Number of Clusters 00:03:08
    3. Estimating the Quality of Clustering 00:03:20
    4. Building a Classifier 00:04:58
    5. Segmenting the Market 00:02:18
  5. Chapter 5 : Building Recommender Systems
    1. Creating a Training Pipeline 00:03:51
    2. Extracting the Nearest Neighbors 00:02:17
    3. Building a K-Nearest Neighbors Classifier 00:03:41
    4. Computing similarity scores 00:04:57
    5. Finding Similar Users 00:02:54
    6. Building a Movie Recommendation System 00:03:25