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Working in a hands-on learning environment, led by our Go Machine Learning expert instructor, students will learn about and explore: Explore ML tasks and Go’s machine learning ecosystem.Implement clustering, regression, classification, and neural networks with Go.Get to grips with libraries such as Gorgonia, Gonum, and GoCv for training models in Go.
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Unlimited Duration
Last Updated
July 29, 2021
Students Enrolled
20
Total Reviews
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Certification
Go is the perfect language for machine learning; it helps to clearly describe complex algorithms, and also helps developers to understand how to run efficient optimized code. This course will teach you how to implement machine learning in Go to make programs that are easy to deploy and code that is not only easy to understand and debug, but also to have its performance measured. The course begins by guiding you through setting up your machine learning environment with Go libraries and capabilities. You will then plunge into regression analysis of a real-life house pricing dataset and build a classification model in Go to classify emails as spam or ham. Using Gonum, Gorgonia, and STL, you will explore time series analysis along with decomposition and clean up your personal Twitter timeline by clustering tweets. In addition to this, you will learn how to recognize handwriting using neural networks and convolutional neural networks. Lastly, you'll learn how to choose the most appropriate machine learning algorithms to use for your projects with the help of a facial detection project. By the end of this course, you will have developed a solid machine learning mindset, a strong hold on the powerful Go toolkit, and a sound understanding of the practical implementations of machine learning algorithms in real-world projects.
Course Curriculum
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- How to Solve All Machine Learning Problems 00:00:00
- What is a problem? 00:00:00
- What is an algorithm? 00:00:00
- What is machine learning? 00:00:00
- Do you need machine learning? 00:00:00
- The general problem solving process 00:00:00
- What is a model? 00:00:00
- On writing and lesson organization 00:00:00
- Why Go? 00:00:00
- Quick start 00:00:00
- Functions 00:00:00
- Variables 00:00:00
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- Linear Regression – House Price Prediction 00:00:00
- The project 00:00:00
- Exploratory data analysis 00:00:00
- Linear regression 00:00:00
- Discussion and further work 00:00:00
- Classification – Spam Email Detection 00:00:00
- The project 00:00:00
- Exploratory data analysis 00:00:00
- The classifier 00:00:00
- Naive Bayes 00:00:00
- Implementating the classifier 00:00:00
- Putting it all together 00:00:00
- Clean Up Your Personal Twitter Timeline by Clustering Tweets 00:00:00
- The project 00:00:00
- K-means 00:00:00
- DBSCAN 00:00:00
- Data acquisition 00:00:00
- Exploratory data analysis 00:00:00
- Data massage 00:00:00
- Clustering 00:00:00
- Real data 00:00:00
- The program 00:00:00
- Tweaking the program 00:00:00
- Convolutional Neural Networks – MNIST Handwriting Recognition 00:00:00
- Everything you know about neurons is wrong 00:00:00
- Neural networks – a redux 00:00:00
- The project 00:00:00
- CNNs 00:00:00
- Describing a CNN 00:00:00
- Running the neural network 00:00:00
- Testing 00:00:00
- Hot Dog or Not Hot Dog – Using External Services 00:00:00
- MachineBox 00:00:00
- What is MachineBox? 00:00:00
- The project 00:00:00
- The results 00:00:00
- What does this all mean? 00:00:00
- Why MachineBox? 00:00:00
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