Last updated 12/2018MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 563.29 MB | Duration: 4h 11m

Build Awesome Predictive Models with Python

What you'll learn

Understand the main concepts and principles of Predictive Analytics and how to use them when building real-world predictive models.

Properly use scikit-learn, the main Python library for Predictive Analytics and Machine Learning.

Learn the types of Predictive Analytics problem and how to apply the main models and algorithms to solve real world problems.

Build, evaluate, and interpret classification and regression models on real-world datasets.

Understand Regression and Classification

Refresh your visualization skills

Requirements

Knowledge of the Python programming language is assumed. Basic familiarity with Python's Data Science Stack would be useful, although a brief review is given. Familiarity with basic mathematics and statistical concepts is also advantageous to take full advantage of this course.

Description

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.

About the author

Alvaro Fuentes is a Data Scientist with an M.S. in Quantitative Economics and a M.S. in Applied Mathematics with more than 10 years of experience in analytical roles. He worked in the Central Bank of Guatemala as an Economic Analyst, building models for economic and financial data. He founded Quant Company to provide consulting and training services in Data Science topics and has been a consultant for many projects in fields such as; Business, Education, Psychology and Mass Media. He also has taught many (online and in-site) courses to students from around the world in topics like Data Science, Mathematics, Statistics, R programming and Python.

Alvaro Fuentes is a big Python fan and has been working with Python for about 4 years and uses it routinely for analyzing data and producing predictions. He also has used it in a couple of software projects. He is also a big R fan, and doesn't like the controversy between what is the “best” R or Python, he uses them both. He is also very interested in the Spark approach to Big Data, and likes the way it simplifies complicated

things. He is not a software eeer or a developer but is generally interested in web technologies.

He also has technical skills in R programming, Spark, SQL (PostgreSQL), MS Excel, machine learning, statistical analysis, econometrics, mathematical modeling.

Predictive Analytics is a topic in which he has both professional and teaching experience. Having solved practical problems in his consulting practice using the Python tools for predictive analytics and the topics of predictive analytics are part of a more general course on Data Science with Python that he teaches online.

Overview

Section 1: The Tools for Doing Predictive Analytics with Python

Lecture 1 The Course Overview

Lecture 2 The Anaconda Distribution

Lecture 3 The Jupyter Notebook

Lecture 4 NumPy - The Foundation for Scientific Computing

Lecture 5 Using Pandas for Analyzing Data

Section 2: Visualization Refresher

Lecture 6 Plotting with Matplotlib

Lecture 7 Visualizing data with Pandas

Lecture 8 Statistical Visualization with Seaborn

Section 3: Concepts in Predictive Analytics

Lecture 9 What Is Predictive Analytics?

Lecture 10 How to Do Predictive Analytics?

Lecture 11 Machine Learning - Supervised Versus Unsupervised Learning

Lecture 12 Supervised Learning - Regression and Classification

Lecture 13 Models and Algorithms

Section 4: Regression: Concepts and Models

Lecture 14 scikit-learn

Lecture 15 The Multiple Regression Model

Lecture 16 K-Nearest Neighbors for Regression

Lecture 17 Lasso Regression

Lecture 18 Model Evaluation for Regression

Section 5: Regression: Predicting C, Stock Prices, and Post Popularity

Lecture 19 Predicting Diamond Prices

Lecture 20 Predicting C in US Communities

Lecture 21 Predicting Post Popularity

Section 6: Classification: Concepts and Models

Lecture 22 Logistic Regression

Lecture 23 Classification Trees

Lecture 24 Naive Bayes Classifiers

Lecture 25 Model Evaluation for Classification

Section 7: Classification: Predicting Bankruptcy, Credit Default, and Spam Text Messages

Lecture 26 Predicting Credit Card Default

Lecture 27 Predicting Bankruptcy

Lecture 28 Building a Spam Classifier

Lecture 29 Further Topics in Predictive Analytics

The course is designed for Data analysts or data scientists interested in learning how to use Python to perform Predictive Analytics as well as Business analysts/business Intelligence experts who would like to go from descriptive analysis to predictive analysis. Software eeers and developers interested in producing predictions via Python will also benefit from the course.

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