Machine Learning Course — Python Machine Learning Bootcamp
Become proficient in a world centered around Machine Learning
What will you learn?
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How to take machine learning ideas and translate them into fully functional projects
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Various types of machine learning approaches and models in each section
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Theoretical and intuitive understanding of how each model works
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Practical applications and implementation of each model we discuss
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How to optimize models
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Common pitfalls and how to overcome them
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Technical skills to use machine learning in the workplace or for your own projects
Requirements
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Basic Python knowledge
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Some prior experience with pandas and matplotlib libraries is very helpful
Description
Machine learning continues to grow in popularity, and for good reason. Companies that can effectively leverage machine learning can solve complex problems that have proven extremely difficult with standard software development.
However, building good machine learning models is not always easy, and it is crucial to have a strong foundation so that if/when you encounter issues with models at work, you understand what steps to take to fix them.
That is why this course focuses on always introducing each model we discuss first with theoretical background on how the model works, so you can build proper intuition around its behavior. Then we will have a practical component, where we will implement the machine learning model and use it on real data. This way you gain both hands-on experience, as well as a strong theoretical foundation, about how various machine learning models work, and you will be able to use this knowledge to better choose and improve models, depending on the situation.
In this course we will cover various aspects of machine learning.
We will start by going through a sample machine learning project from idea to developing the final working model. We will learn many important techniques around data preparation, cleaning, feature engineering, optimization and learning techniques, and much more.
After we complete the entire machine learning project, we will then dive deeper into several different areas of machine learning, to better understand each task, and how each model we can use to complete these tasks works, and then also use each model and understand how we can adjust all the parameters we learned in the theory component.
The different areas we will dive deeper into are:
– Classification
