Building Machine Learning Models in Python with scikit-learn

Building Machine Learning Models in Python with scikit-learn

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 3h 13m | 402 MB

This course course will help engineers and data scientists learn how to build machine learning models using scikit-learn, one of the most popular ML libraries in Python. No prior experience with ML needed, only basic Python programming knowledge.

The Python scikit-learn library is extremely popular for building traditional ML models i.e. those models that do not rely on neural networks. In this course, Building Machine Learning Models in Python with scikit-learn, you will see how to work with scikit-learn, and how it can be used to build a variety of machine learning models. First, you will learn how to use libraries for working with continuous, categorical, text as well as image data. Next, you will get to go beyond ordinary regression models, seeing how to implement specialized regression models such as Lasso and Ridge regression using the scikit-learn libraries. Finally, in addition to supervised learning techniques, you will also understand and implement unsupervised models such as clustering using the mean-shift algorithm and dimensionality reduction using principal components analysis. At the end of this course, you will have a good understanding of the pros and cons of the various regression, classification, and unsupervised learning models covered and you will be extremely comfortable using the Python scikit-learn library to build and train your models. Software required: scikit-learn, Python 3.x.

Table of Contents

Course Overview
1 Course Overview

Processing Data with scikit-learn
2 Module Overview
3 Prerequisites and Course Overview
4 Machine Learning Use Cases and scikit-learn
5 Supervised and Unsupervised Learning Techniques
6 Demo – Useful Python Packages
7 Mean and Variance
8 Demo – Scaling Numeric Data
9 Categorical Data and One-hot Encoding
10 Demo – Representing Categorical Data in Numeric Form
11 Representing Text in Numeric Form
12 Frequency Based Encoding – Count Vectors
13 Frequency Based Encoding – TF IDF
14 Demo – CountVectorizers TfidfVectorizer HashingVectorizer
15 Representing Images in Numeric Form
16 Demo – Extracting Features from Images

Building Specialized Regression Models in scikit-learn
17 Module Overview
18 Ordinary Least Square Regression
19 Measuring Fit Using R-squared
20 Demo – Data Preparation
21 Demo – Linear Regression Using Estimators
22 L1 and L2 Norm
23 Overfitting and The Bias-variance Trade-off
24 Multicollinearity in Regression
25 Lasso and Ridge Regression
26 Demo – Lasso Regression
27 Demo – Ridge Regression
28 Support Vector Regression
29 Demo – Support Vector Regression
30 Demo – SVR Reduced Penalty

Building SVM and Gradient Boosting Models in scikit-learn
31 Module Overview
32 Support Vector Machines for Classification
33 Setting up the SVM Classification Problem
34 Demo – SVM Text Classification
35 Demo – SVM Image Classification with Grid Search
36 Decision Trees
37 Random Forests
38 Gradient Boosting Regression
39 Gradient Boosting Regression and Shrinkage Factor
40 Demo – Gradient Boosting Regression with Grid Search

Implementing Clustering and Dimensionality Reduction in scikit-learn
41 Module Overview
42 Clustering
43 K-means Clustering
44 Mean Shift Clustering
45 K-means vs. Mean Shift Clustering
46 Demo – Mean Shift Clustering
47 Demo – Examine Mean Shift Clusters
48 Principal Components Analysis – Intuition
49 Demo – Principal Components Analysis
50 Summary and Further Study