Clustering and Classification with Machine Learning in Python [Video]
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-
Free ChapterINTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
-
Read in Data from Different Sources with Pandas
-
Data Cleaning & Munging
-
Unsupervised Learning in Python
- Unsupervised Classification- Some Basic Concepts
- K-Means Clustering: Theory
- Implement K-Means on the Iris Data
- Quantifying K-Means Clustering Performance
- K-Means Clustering with Real Data
- How to Select the Optimal Number of Clusters?
- Gaussian Mixture Modelling (GMM)
- Hierarchical Clustering-theory
- Hierarchical Clustering-practical
-
Dimension Reduction & Feature Selection for Machine Learning
- Principal Component Analysis (PCA)-Theory
- Principal Component Analysis (PCA)-Case Study 1
- Principal Component Analysis (PCA)-Case Study 2
- Linear Discriminant Analysis (LDA) for Dimension Reduction
- t-SNE Dimension Reduction
- Feature Selection to Select the Most Relevant Predictors
- Recursive Feature Elimination (RFE)
-
Supervised Learning: Classification
- Concepts Behind Supervised Learning
- Data Preparation for Supervised Learning
- Pointers on Evaluating the Accuracy of Classification Modelling
- Using Logistic Regression as a Classification Model
- kNN- Classification
- Naive Bayes Classification
- Linear Discriminant Analysis
- SVM- Linear Classification
- Non-Linear SVM Classification
- RF-Classification
- Gradient Boosting Machine (GBM)
- Voting Classifier
-
Neural Networks and Deep Learning Based Classification Techniques
About this
video
In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal. By becoming proficient in unsupervised and supervised learning in Python, you can give your company a competitive edge and level up in your career. This course will give you a robust grounding in clustering and classification, the main aspects of machine learning.
The course consists of 7 sections that will help you master Python machine learning. You’ll begin with an introduction to Python data science and Anaconda, which is a powerful Python-driven framework for data science. Next, you'll delve into Pandas and read data structures, including CSV, Excel, and HTML data. As you advance, you’ll perform data cleaning and munging to remove NAs\no data and discover how to handle conditional data, group by attributes, and do much more. You’ll also grasp basic concepts of unsupervised learning such as K-means clustering and its implementation on the Iris dataset. The course will take you through the theory of dimension reduction and feature selection for machine learning and help you understand Principal Component Analysis (PCA) using two case studies. You’ll get to grips with the linear and non-linear classification of SVM along with Gradient Boosting Machine (GBM) and Naive Bayes Classification. Finally, you’ll explore neural networks and discover the powerful H20 framework and for deep learning classification. Additionally, you’ll learn about perceptrons and Artificial Neural Networks (ANN) for binary classification.
By the end of this course, you'll be able to use packages such as NumPy, Pandas, and Matplotlib to work with real data in Python.
All code and supporting files for this course are available at
https://github.com/sanjanapackt/PacktPublishing-Clustering-and-Classification-with-Machine-Learning-in-Python.
- Publication date:
- December 2019
- Publisher
- Packt
- Duration
- 5 hours 50 minutes
- ISBN
- 9781839213632