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Making Predictions with Data and Python

Video Description

Build Awesome Predictive Models with Python

About This Video

  • Understand the core concepts in Predictive Analytics and how to apply them to build predictive models in diverse fields
  • Effectively use Python's main library, scikit-learn, for Predictive Analytics and Machine Learning
  • Learn the foundational models and algorithms that are required for any job in the field of Predictive Analytics

In Detail

Python has become one of any data scientist's favorite tools for doing Predictive Analytics. In this hands-on course, you will learn how to build predictive models with Python.

During the course, we will talk about the most important theoretical concepts that are essential when building predictive models for real-world problems. The main tool used in this course is scikit -learn, which is recognized as a great tool: it has a great variety of models, many useful routines, and a consistent interface that makes it easy to use. All the topics are taught using practical examples and throughout the course, we build many models using real-world datasets.

By the end of this course, you will learn the various techniques in making predictions about bankruptcy and identifying spam text messages and then use our knowledge to create a credit card using a linear model for classification along with logistic regression.

Table of Contents

  1. Chapter 1 : The Tools for Doing Predictive Analytics with Python
    1. The Course Overview 00:04:10
    2. The Anaconda Distribution 00:03:22
    3. The Jupyter Notebook 00:05:13
    4. NumPy - The Foundation for Scientific Computing 00:10:36
    5. Using Pandas for Analyzing Data 00:10:52
  2. Chapter 2 : Visualization Refresher
    1. Plotting with Matplotlib 00:09:54
    2. Visualizing data with Pandas 00:06:54
    3. Statistical Visualization with Seaborn 00:06:52
  3. Chapter 3 : Concepts in Predictive Analytics
    1. What Is Predictive Analytics? 00:06:02
    2. How to Do Predictive Analytics? 00:02:53
    3. Machine Learning - Supervised Versus Unsupervised Learning 00:05:51
    4. Supervised Learning - Regression and Classification 00:04:09
    5. Models and Algorithms 00:07:48
  4. Chapter 4 : Regression: Concepts and Models
    1. scikit-learn 00:07:32
    2. The Multiple Regression Model 00:06:38
    3. K-Nearest Neighbors for Regression 00:04:57
    4. Lasso Regression 00:03:52
    5. Model Evaluation for Regression 00:07:29
  5. Chapter 5 : Regression: Predicting Crime, Stock Prices, and Post Popularity
    1. Predicting Diamond Prices 00:14:15
    2. Predicting Crime in US Communities 00:07:51
    3. Predicting Post Popularity 00:09:58
  6. Chapter 6 : Classification: Concepts and Models
    1. Logistic Regression 00:14:41
    2. Classification Trees 00:11:34
    3. Naive Bayes Classifiers 00:06:12
    4. Model Evaluation for Classification 00:14:02
  7. Chapter 7 : Classification: Predicting Bankruptcy, Credit Default, and Spam Text Messages
    1. Predicting Credit Card Default 00:22:49
    2. Predicting Bankruptcy 00:13:55
    3. Building a Spam Classifier 00:12:59
    4. Further Topics in Predictive Analytics 00:07:33