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Course Description
With machine learning, developers can train machines to learn from their own experiences without explicitly programming them to do so. With machine learning libraries—a compilation of functions and routines readily available for use—it’s easier to develop software that implements machine learning. These libraries allow developers to research and write complex programs without writing a lot of code. Machine learning libraries cover many different functions including text processing, graphics, data manipulation, and scientific computation. As machine learning continues to offer new possibilities, hundreds of machine learning libraries are being developed.
In this KnowledgeCity course on Python for Data Science Advanced: More Machine Learning Libraries, we’ll explore a variety of machine learning algorithms. We’ll look at Keras, a high-level neural network API written in Python. You’ll also learn about CatBoost, which provides a gradient boosting framework. Then we’ll delve into convolutional neural networks, an open-source numerical computation library designed for deep learning and other machine learning tasks.
What You'll Learn
- Identify how machine learning libraries help developers build complex programs
- Understand the function of Keras, a high-level neural network API written in Python
- Explain the function of convolutional neural networks
- Explore the CatBoost gradient boosting framework
- Examine additional machine learning libraries including Theano, LightGBM, and XGBoost
Key Takeaways
- Machine learning lets developers train machines to learn from their own experiences without explicitly programming them to do so.
- Machine learning libraries are compilations of ready-to-use functions and routines that make it easier to develop software implementing machine learning.
- These libraries let developers research and write complex programs without writing a lot of code, covering functions such as text processing, graphics, data manipulation, and scientific computation.
- Keras is a high-level neural network API written in Python, and CatBoost provides a gradient boosting framework.
- As machine learning continues to offer new possibilities, hundreds of machine learning libraries are being developed.
Frequently Asked Questions
What machine learning libraries does this course cover?
The course explores a variety of machine learning libraries through lessons on Keras, Theano, LightGBM, XGBoost, and CatBoost.
What will I learn about Keras in this course?
You will learn about Keras, a high-level neural network API written in Python, and understand its function.
What skills does this course help build?
The course focuses on skills in Data Libraries, Deep Learning Methods, Gradient Boosting, and Machine Learning Algorithms.
Does this course cover gradient boosting?
Yes. It covers CatBoost, which provides a gradient boosting framework, along with other libraries such as LightGBM and XGBoost.
What does the course teach about convolutional neural networks?
The course delves into convolutional neural networks and aims to help you explain their function.
