AI and machine learning are in demand and Python is the #1 programming language for data scientists. That means learning Python can set you up for a lucrative career.

In this path, you’ll work your way through the basics of Python, master various components of machine learning, like calculus, linear algebra, linear regression, and much more.

Start developing skills in statistics and probability to form valuable insights all from the comfort of your browser.

  • Learn the fundamentals of Python programming and data science
  • ELearn intermediate-level Python skills like data prepping, cleaning, and error correction
  • Reveal power libraries such as pandas and NumPy
  • Build machine learning models and become familiar with Kaggle

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What You’ll Learn

Machine learning enables systems to learn and improve without direct instructions from users. This machine learning introduction path is ideal for building your Python skill set from the ground up.

It covers essential machine learning techniques, including k-nearest neighbors, k-means clustering, and decision trees. You’ll build knowledge of machine learning, deep learning, linear algebra, and the power of Kaggle. You’ll also discover how to graphically display data with data visualization and explore intermediate-level machine learning concepts like deep learning with neural networks.

  • Basic and intermediate Python programming
  • Data cleaning and analysis
  • Fundamentals of statistics and probability
  • Deep learning
  • Data visualization
  • Object-oriented programming in Python
  • Calculus, linear algebra, and linear regression for machine learning
  • Data visualization
  • Pandas and NumPy packages fundamentals
Data Scientist in Python Salary Increase

Machine learning engineer salaries range between $88K-$200K p/year per Glassdoor.

Data Scientist in Python Job Openings

Machine learning roles are expected to grow 40% in the next 6 years according to Simplilearn.

Data Scientist In Python Job Growth

Machine learning engineer is the best job in the US, according to

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:

  • This skill path consists of the 19 courses listed below, which cover the fundamentals of machine learning and Python.
  • You’ll write real code with dozens of practice problems to validate and apply your skills.
  • At the end of each course, you’ll complete a guided project to reinforce your new knowledge and expand your portfolio.
  • When you complete the course, you’ll receive a certificate that you can share with your professional network.
  •  Once you master the fundamentals of machine learning, you’ll be ready for more advanced courses.
  • Engage with our friendly community of machine learning engineers, get feedback on your projects, and keep building your skills.

Enroll in this skill path to learn Machine Learning with Python today!

Machine Learning Introduction with Python Path Course List

Python for Data Science: Fundamentals Part I
Learn the fundamentals of programming in Python and the fundamentals of data science.

Python for Data Science: Fundamentals Part II
Learn how to use Jupyter Notebook and how to build a portfolio project.

Python for Data Science: Intermediate
Learn how to clean and analyze text data, learn object-oriented programming in Python, and learn how to work with dates and times.

Pandas and NumPy Fundamentals
Learn how to analyze data using the pandas and NumPy libraries.

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.

Statistics Fundamentals
Learn about sampling, variables, and distributions.

Statistics Intermediate
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.

Probability: Fundamentals
Learn the fundamentals of probability theory using Python.

Machine Learning Fundamentals
Learn the basics of machine learning, and explore how to avoid common pitfalls in machine learning.

Calculus for Machine Learning
Explore the key ideas from calculus for understanding how mathematical functions behave, and prepare for intermediate machine learning techniques.

Linear Algebra for Machine Learning
Explore the key ideas from linear algebra for understanding linear systems, 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.

Machine Learning in Python: Intermediate
Learn intermediate linear regression and logistic regression concepts and how to prevent overfitting, a common problem in machine learning.

Decision Trees
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.

Machine Learning Project
Walk through a machine learning project start to finish.

Kaggle Fundamentals
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.

Who Is this Machine Learning Introduction Path For?

  • Anyone who wants an introduction to a versatile and popular computer programming language
  • People who are interested in using Python to analyze their own data
  • People who want to build games or personal robots as a hobby
  • Professionals looking to add another computer programming language to their repertoire
  • Professionals looking to advance their skills with machine learning
  • Students looking to level up their Python and machine learning skills for academic papers or a job search
  • Small business owners searching for a way to analyze their data to make important business decisions
  • Anyone interested in a career in machine learning
  • People who understand that AI is the future, and they want to be a part of it

Qualify for In-demand Jobs in Machine Learning

Machine learning engineers are in high demand across many industries. The ability to program software to learn on its own is powerful, and it has many use cases.

  • Machine Learning Engineer
  • Python Developer
  • Data Analytics Engineer
  • Data Scientist
  • AIOps Engineer
  • Cybersecurity Analyst
  • Cloud Architect for Machine Learning
  • Computational Linguist
  • Robotics Engineer
  • Data Lawyer
  • AI Ethicist