How Our Machine Learning Introduction Path Works
Our hands-on machine learning with Python courses will show you how to effectively cluster and classify data, identify useful patterns, implement machine learning algorithms to streamline your process, and much more. We know videos may work for some learners, but we believe that the key to retaining more practical knowledge is applying your skills through practice.
This path will take you from absolute Python beginner to job-ready. If you need help or have questions along the way, we’re here for you. Our built-in support tools answer technical questions, and you’ll have the support of our thriving community of programmers.
Here’s a quick glance at this skill path:
Enroll in this skill path to learn Machine Learning with Python today!
Machine Learning Introduction with Python Path Course List
Data Visualization Fundamentals
Learn the fundamentals of data visualization in Python by striking a good balance between graph interpretation (statistics) and tooling (Matplotlib and Seaborn).
Data Cleaning and Analysis
Learn data cleaning and analysis with pandas, how to combine datasets,how to clean string data, and how to resolve missing data.
Averages and Variability: Learn how to summarize distributions using the mean, the median, and the mode. Learn to measure variability using variance or standard deviation, and learn how to locate and compare values using z-scores.
Calculus for Machine Learning
Explore the key ideas from calculus for understanding how mathematical functions behave, and prepare for intermediate machine learning techniques.
Linear Regression for Machine Learning
Learn how to make predictions using the linear regression machine learning model; two different ways of fitting a linear regression model; and how to select, clean, and transform features.
Understand the types of relationships decision trees can represent, build a decision tree implementation, and learn how to use the random forest machine learning model.
Deep Learning Fundamentals
Learn how neural networks are represented, how neural networks capture nonlinearity in the data, and how adding hidden layers can provide improved model performance.
Build a simple machine learning model, and make your first Kaggle sublesson. Create new features and select the best-performing features to improve your score.