English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 4h 10m | 771 MB
Sciki-Learn and beyond. Build Awesome Predictive Models with Python
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.
What You Will 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
Table of Contents
01 The Course Overview
02 The Anaconda Distribution
03 The Jupyter Notebook
04 NumPy – The Foundation for Scientific Computing
05 Using Pandas for Analyzing Data
06 Plotting with Matplotlib
07 Visualizing data with Pandas
08 Statistical Visualization with Seaborn
09 What Is Predictive Analytics
10 How to Do Predictive Analytics
11 Machine Learning – Supervised Versus Unsupervised Learning
12 Supervised Learning – Regression and Classification
13 Models and Algorithms
14 scikit-learn
15 The Multiple Regression Model
16 K-Nearest Neighbors for Regression
17 Lasso Regression
18 Model Evaluation for Regression
19 Predicting Diamond Prices
20 Predicting Crime in US Communities
21 Predicting Post Popularity
22 Logistic Regression
23 Classification Trees
24 Naive Bayes Classifiers
25 Model Evaluation for Classification
26 Predicting Credit Card Default
27 Predicting Bankruptcy
28 Building a Spam Classifier
29 Further Topics in Predictive Analytics
Resolve the captcha to access the links!