Course Description
Applied Supervised Learning with Python provides you a rich understanding of machine learning, one of the most pursued topics in information science, and Python, one of the most popular scripting languages. Through this course, you’ll learn Jupyter Notebooks, the technology used in academic and commercial circles with in-line code running support.
Overview
Applied Supervised Learning refers to the process of using data to train machine learning models in order to make predictions or decisions. In this context, “supervised” means that the training data has labeled examples, which means that the model can learn from these examples and make accurate predictions on new, unseen data.
Python is a popular programming language for machine learning and data analysis due to its simplicity, flexibility, and large number of libraries and frameworks available. In this course or resource, users can learn about various techniques and best practices related to Applied Supervised Learning in Python, including topics such as data preparation, model selection, and evaluation.
Machine learning—the ability of a machine to give correct answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques to your data science projects using Python. You’ll explore Jupyter notebooks, a technology widely used in academic and commercial circles with support for running inline code.
With the help of fun examples, you’ll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you’ll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You’ll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn.
By the end of this course, you’ll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!
What you will Learn
- Understand the concept of supervised learning and its applications
- Implement common supervised learning algorithms using machine learning Python libraries
- Validate models using the k-fold technique
- Build your models with decision trees to get results effortlessly
- Use ensemble modeling techniques to improve the performance of your model
- Apply a variety of metrics to compare machine learning models
Who should attend
Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It’ll help if you have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.
Course-specific Technical Requirements- Software
- Any of the following operating systems:
- Windows 7 SP1 32/64-bit, Windows 8.1 32/64-bit, or Windows 10 32/64-bit
- Ubuntu 14.04 or later
- macOS Sierra or later
- Browser: Google Chrome or Mozilla Firefox
- Anaconda