Unlock deeper insights into Machine Learning with this vital guide to cutting-edge predictive analytics.
- Discover the different types of machine learning and know when to use them
- Explore machine learning algorithms and implement them in Python
- Use powerful open source machine learning libraries to train predictive models
- Use pandas, NumPy, and matplotlib to manipulate data
- Evaluate and fine-tune machine learning models
Prior knowledge of Python is needed.
Who is this course intended for?
Packt has been committed to developer learning since 2004. A lot has changed in software since then – but Packt has remained responsive to these changes, continuing to look forward at the trends and tools defining the way we work and live. And how to put them to work.
With an extensive library of content – more than 4000 books and video courses -Packt’s mission is to help developers stay relevant in a rapidly changing world. From new web frameworks and programming languages to cutting-edge data analytics, and DevOps, Packt takes software professionals in every field to what’s important to them now.
From skills that will help you to develop and future-proof your career to immediate solutions to everyday tech challenges, Packt is a go-to resource to make you a better, smarter developer.
|Giving Computers the Ability to Learn from Data|
|The Course Overview||00:00:00|
|Transforming Data into Knowledge||00:00:00|
|Types of Machine Learning||00:00:00|
|Training Machine Learning Algorithms for Classification|
|Implementing a Perceptron Algorithm in Python||00:00:00|
|The Iris Dataset||00:00:00|
|Training the Perceptron||00:00:00|
|Improving the Visualization||00:00:00|
|Adaline in Python||00:00:00|
|A Tour of Machine Learning Classifiers Using Scikit-Learn|
|Logistic Regression in Scikit-Learn||00:00:00|
|Predicting Class Probabilities||00:00:00|
|Training a Support Vector Machine in Scikit-Learn||00:00:00|
|The Effect of Gamma||00:00:00|
|Building Good Training Sets – Data Preprocessing|
|Mapping Ordinal Features||00:00:00|
|Feature Importance’s with Random Forests||00:00:00|
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