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Step-by-Step Machine Learning with Python

Video Description

Put your Python skills to the test and enter the big world of data science to learn the most effective machine learning tools and techniques with this interesting guide

About This Video

  • Learn the fundamentals of machine learning and build your own intelligent applications
  • Master the art of building your own machine learning systems with this example-based practical guide
  • Work with important classification and regression algorithms and other machine learning techniques

In Detail

Data science and machine learning are some of the top buzzwords in the technical world today. The resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. This video is your entry point to machine learning. It starts with an introduction to machine learning and the Python language and shows you how to complete the necessary setup. Moving ahead, you will learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. With the help of the various projects included, you will acquire the mechanics of several important machine learning algorithms, which will no longer seem obscure. Also, you will be guided step-by-step to build your own models from scratch. Toward the end, you will gather a broad picture of the machine learning ecosystem and master best practices for applying machine learning techniques. Throughout this course, you will learn to tackle data-driven problems and implement your solutions with the powerful yet simple Python language. Interesting and easy-to-follow examples—including news topic classification, spam email detection, online ad click-through prediction, and stock prices forecasts—will keep you glued to the screen till you reach your goal.

Table of Contents

  1. Chapter 1 : Getting Started with Python and Machine Learning
    1. The Course Overview 00:05:03
    2. Introduction to Machine Learning 00:06:43
    3. Installing Software and Setting Up 00:06:05
  2. Chapter 2 : Exploring the 20 Newsgroups Dataset with Text Analysis Algorithms
    1. Understanding NLP 00:04:10
    2. Touring Powerful NLP Libraries in Python 00:10:37
    3. Getting the Newsgroups Data 00:03:18
    4. Thinking about Features 00:05:32
    5. Visualization 00:03:15
    6. Data Preprocessing 00:02:29
    7. Clustering 00:04:04
    8. Topic Modeling 00:03:40
  3. Chapter 3 : Spam Email Detection with Naïve Bayes
    1. Getting Started with Classification 00:05:03
    2. Exploring Naïve Bayes 00:02:49
    3. The Mechanics of Naïve Bayes 00:04:50
    4. The Naïve Bayes Implementation 00:16:31
    5. Classifier Performance Evaluation 00:10:23
    6. Model Tuning and cross-validation 00:05:11
  4. Chapter 4 : News Topic Classification with Support Vector Machine
    1. Recap and Inverse Document Frequency 00:04:26
    2. The Mechanics of SVM 00:05:51
    3. The Implementations of SVM 00:05:44
    4. The Kernels of SVM 00:03:13
    5. Choosing Between the Linear and the RBF Kernel 00:03:51
    6. News topic Classification with Support Vector Machine 00:10:27
    7. Fetal State Classification with SVM 00:05:59
  5. Chapter 5 : Click-Through Prediction with Tree-Based Algorithms
    1. Brief Overview of Advertising Click-Through Prediction 00:04:46
    2. Decision Tree Classifier 00:13:44
    3. The Implementations of Decision Tree 00:06:18
    4. Click-Through Prediction with Decision Tree 00:06:53
    5. Random Forest - Feature Bagging of Decision Tree 00:05:14
  6. Chapter 6 : Click-Through Prediction with Logistic Regression
    1. One-Hot Encoding - Converting Categorical Features to Numerical 00:06:00
    2. Logistic Regression Classifier 00:12:09
    3. Click-Through Prediction with Logistic Regression by Gradient Descent 00:21:30
    4. Feature Selection via Random Forest 00:04:30
  7. Chapter 7 : Stock Price Prediction with Regression Algorithms
    1. Brief Overview of the Stock Market And Stock Price 00:03:49
    2. Predicting Stock Price with Regression Algorithms 00:06:41
    3. Data Acquisition and Feature Generation 00:03:30
    4. Linear Regression 00:08:28
    5. Decision Tree Regression 00:07:36
    6. Support Vector Regression 00:02:57
    7. Regression Performance Evaluation 00:03:28
    8. Stock Price Prediction with Regression Algorithms 00:09:49
  8. Chapter 8 : Best Practices
    1. Best Practices in Data Preparation Stage 00:11:18
    2. Best Practices in the Training Sets Generation Stage 00:08:46
    3. Best Practices in the Model Training, Evaluation, and Selection Stage 00:03:41
    4. Best Practices in the Deployment and Monitoring Stage 00:05:40