Python for Machine Learning - The Essential Starter Kit

Overview

Before you jump into machine learning though, it’s important to know the basics. This course is an essential starting point for machine learning with an approach that is accessible and rooted in practical value. You’ll learn vital pre-machine learning skills, with a focus on data preparation, to avoid common data science risks, like “garbage in, garbage out.”  Perfect for beginners and professionals looking to advance into roles that include machine learning, this course is designed to introduce you to Python, a programming language known for its simplicity and readability. Python has an extensive library that includes packages specifically designed for machine learning, such as NumPy, Pandas, Scikit-learn, SciPy, and Statsmodels. Through a series of modules, you’ll gain hands-on experience with Python's syntax, data structures, and functions relevant to machine learning tasks.  

Upon completion of the Python for Machine Learning - The Essential Starter Kit course from University of Waterloo, you’ll have a solid understanding of proper data preparation and how to get the results you’re seeking.  

What you will learn

• Explain the practical benefits and uses of machine learning: Gain an understanding of the real-world applications of machine learning and its significance in various fields such as business, healthcare, finance, marketing, and more. Recognize the difficulties with operationalizing machine learning models and build a broader understanding of the applicability of machine learning. 
• Create a machine learning portfolio in Jupyter Notebook to show potential employers: Learn how to set up and configure your Python environment using Jupyter Notebook, leveraging the Anaconda distribution platform. Explore the benefits of Jupyter Notebook for interactive coding, data exploration, and documentation, and understand how Anaconda simplifies package management and environment creation. 
• Identify key components of data preparation in machine learning: Understand the crucial role of data preparation in machine learning pipelines. Identify key components such as data cleaning, missing values, feature scaling, categorical variable encoding, and common manipulations. Recognize the significance of data preprocessing for improving model performance and achieving reliable results. 
• Gain hands-on experience with some of Python's packages and libraries: Learning the Python libraries and packages NumPy, Pandas, Scikit-learn, SciPy, and Statsmodels is crucial for a well-rounded proficiency in data science and machine learning. From data handling and preprocessing to modeling and analysis, these libraries and packages are the building blocks of modern data-driven decision-making and advanced research.