Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics
About This Python Machine Learning Video course
- Leverage Python’s most powerful open source libraries for deep learning, data wrangling, and data visualization
- Get to know effective strategies and best practices to improve and optimize machine learning systems and algorithms
- Ask—and answer— tough questions of your data with robust statistical models, built for a range of datasets
Python Machine Learning in detail
Machine learning and predictive analytics are transforming the way that businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, and is becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data. Its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.
This video course gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science courseis invaluable. It coversa wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and features guidance and tips on everything from sentiment analysis to neural networks.
With this video,you’ll soon be able to answer some of the most important questions facing you and your organization.
Python, Data Science training table of contents (duration : 3h22m)
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Giving Computers the Ability to Learn from Data
- The Course Overview free 00:01:25
- Transforming Data into Knowledge free 00:04:44
- Types of Machine Learning free 00:05:01
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Training Machine Learning Algorithms for Classification
- Implementing a Perceptron Algorithm in Python 00:11:44
- The Iris Dataset 00:11:07
- Training the Perceptron 00:03:44
- Improving the Visualization 00:08:03
- Adaline in Python 00:15:17
- Feature Standardization 00:09:26
- Implementing Adaline 00:14:37
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A Tour of Machine Learning Classifiers Using Scikit-Learn
- Scikit-Learn Perceptron 00:15:45
- Logistic Regression in Scikit-Learn 00:07:36
- Predicting Class Probabilities 00:08:55
- Training a Support Vector Machine in Scikit-Learn 00:10:36
- The Effect of Gamma 00:06:33
- Decision Trees 00:21:04
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Building Good Training Sets – Data Preprocessing
- Handling Data 00:08:24
- Mapping Ordinal Features 00:13:18
- Feature Scaling 00:15:49
- Feature Importance's with Random Forests 00:09:43
- Certificate
Instructor : Packt
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With which software version is this tutorial compatible with?Python
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What is the required level to follow this tutorial ?beginner
