Machine Learning with Python

Learn machine learning algorithms
in just 2 months
Study flexibly
for 15 hours per week
Finish with a portfolio
that demonstrates your new skills

Want to know more about the course?

Discover if machine learning is the right fit for you with our course PDF.

Curious about this course?

Contact us to find out if it’s right for you

Career Advisor

“How would you like to get in touch?”

“I’m here to help you become a machine learning expert!”

Alana, Senior Program Advisor

Curious about this course?

Contact us to find out if it’s right for you

Career Advisor

“How would you like to get in touch?”

“I’m here to help you become a machine learning expert!”

Alana, Senior Program Advisor

This February, we’re offering the first 100 students reduced tuition worth up to $1,365 offon all of our career change programs 🥳 Secure your spot today!

Machine Learning with Python Course details

In this course, you will

Learn the fundamental principles of machine learning, and apply them to a real-world project.

Use Python to conduct advanced analyses using machine learning algorithms.

Dive into the real-world applications of machine learning such as unsupervised learning, deep learning, and visual data.

Work 1:1 with an expert mentor, who'll provide you with individualized support, advice, and feedback.

Join an active community of over 5000 graduates and 700 instructors, and get access to exclusive events and webinars.

Fully online

Study for an average of 15–20 hours per week for 2 months

Personalized mentorship

Our course mentors are rated 4.96/5

Outcome-oriented

Finish with a certificate of completion and complete portfolio project

Why learn machine learning?

Machine learning allows businesses to make better predictions

In order to remain competitive, companies face growing demands to predict market trends and customer behavior. By integrating machine learning models into their data analytics, businesses acquire significantly improved capabilities for accurately forecasting.

Boost your career with machine learning skills

Supercharge your resumé—whether you want to work on a side project, build your own business, or simply contribute a broader skillset to your company, learning machine learning is a surefire way to maximize the value you provide.

Machine learning experts are in high demand

Machine learning and AI are new and rapidly evolving fields, and as companies grow and need to improve efficiency, those with machine learning skills are in high demand worldwide.

Why choose a CareerFoundry course?

Work with your very own course mentor

You'll enjoy a truly collaborative online learning experience, with tailored written and video feedback on everything you do from an expert who works in your new field day in, day out.

Get the perfect balance of theory and practice

With a curriculum designed in-house in collaboration with leading machine learning experts, the course will help you get to grips with complex machine learning methods and make long term predictions for your first project.

Finish with a job-ready portfolio

Guided by the expert advice of your mentor, you’ll finish the course with a portfolio, complete with a professional case study that showcases your ability to think like a machine learning expert.

Machine learning is the science of getting computers to learn without being explicitly programmed.

Sebastian Thrun
Co-founder of Udacity

Meet your new team

At CareerFoundry, you’re never alone! From the moment you start the course, you’ll be assigned a personal mentor. This seasoned and influential expert will act as your teacher, coach, and confidant through every step of the course—providing individualized support, advice, and feedback.

Your mentor

is an experienced machine learning expert

Your mentor will provide detailed video reviews of each project you complete during the course.

Our mentors haven’t just made a name for themselves at top companies in the industry—but have helped shape it.

A project-based curriculum that gets you thinking like a machine learning expert.

Learn the skills you need to stand out as a data analyst with machine learning experience.

Created by experienced instructional designers, authored by industry experts, and kept up-to-date by course editors, our curriculum will serve as the foundation of your learning experience.

Achievement 1: Basics of Machine Learning for Analysts

​​​​​​1.1 The History and Tools of Machine Learning

Describe the difference between machine learning and AI, explain where machine learning is used, and identify tools for using machine learning.

1.2 Ethics and Direction of Machine Learning Programs

​​​Define how to constrain, direct, and understand data and examine case studies to summarize how and why ethics is an important topic in machine learning.

​​​​​1.3 Optimization in Relation to Problem-Solving

Identify the benefits of optimization algorithms in machine learning models and demonstrate understanding of the basics of regression such as simple, multiple linear, and nonlinear regression.

1.4 Supervised Learning Algorithms Part 1

Describe the Python libraries used for machine learning and define data sets and how they’re used in machine learning.

1.5 Supervised Learning Algorithms Part 2

Identify what supervised machine learning can and can’t be used for, and explain the uses for regression and classification in supervised learning.

1.6 Presenting Machine Learning Results

Identify the dangers in cognitive bias, recognize how to lead a presentation to ensure valuable information is retained, and finally, you'll create and give a presentation on your assessment of machine learning tools.

Achievement 2: Real-World Applications of Machine Learning

2.1 Unsupervised Learning Algorithms

Differentiate between unsupervised and supervised learning and identify the key differences in their approaches and applications.

Implement unsupervised learning algorithms to identify patterns and structures in data without relying on predefined labels or target variables.

2.2 Complex Machine Learning Models and Keras Part 1

Set up complex machine learning problems using Keras and TensorFlow.

Set up a machine learning model to predict participant behavior.

2.3 Complex Machine Learning Models and Keras Part 2

Apply Random Forests and Support Vector Machines algorithms to real-world machine learning problems.

Use deep learning models with the Keras library to help direct and refine your results.

Use deep learning models using advanced techniques to accurately detect changes in human works.

2.4 Evaluating Hyperparameters

Analyze the impact of hyperparameters and tuning on the performance of deep learning models, compare, and contrast the advantages and disadvantages of various hyperparameter settings.

Use the correct hyperparameters for your specific machine learning problem to iterate on your results.

2.5 Visual Applications of Machine Learning

Define how machine learning and image recognition can work together, create a handwriting discriminator, and frame a problem as a supervised learning problem using Generative Adversarial Networks (GANs).

2.6 Presenting Your Final Results

Create a presentation that compiles all your findings, produce a thought experiment, and practice applying the correct mindset when approaching machine learning problems.

Price

Machine Learning with Python

OR secure your spot and pay today, then per month for months (starting one month after your program start date).
  • Learn through our comprehensive, project-based curriculum
  • Receive regular, personalized feedback from your course mentor
  • Deliver your first project as a machine learning specialist, which will form the basis of your professional portfolio
  • Get an in-depth review of your portfolio project from your mentor on a video call
  • Gain exclusive access to our global community—plus events and webinars

FAQ

1.    Who is the course for?

This course is for those who would like to learn how to use Python to conduct advanced analyses using machine learning algorithms and models. The Machine Learning with Python Course is available as a specialization course for our Data Analytics Program, or it can be taken as a standalone course.

2.    What are the prerequisites and requirements for the course?

To successfully complete the course, you’ll need to have experience with the programming language Python. You’ll also need to have experience with tools and software such as Excel and Jupyter Notebook, and experience working with statistical algorithms. You should already be comfortable using many of the common libraries for working with data, for instance, Pandas, NumPy, and sci-kit-learn.

Additionally, you’ll need:*

  • Interest in machine learning
  • Written and spoken English proficiency at a B2 level or higher
  • A computer (macOS, Windows, or Linux) with a webcam, microphone, and an internet connection.

*Note: You will be required to invest some independent study time (approximately 1-2 hours per week) towards familiarizing yourself with the tools you’ll use throughout the course, and learning how to use them.

3.    Which tools will I use and what are the costs?

You’ll be using the tools; Jupyter Notebook, GitHub, and Python. You'll also use a Word Processing tool and a Presentation tool of your choice.

All the tools and software you’ll need are free to use—with no additional cost to you.

4.    What are the minimum system requirements?

Compatible operating systems: Windows 11, macOS versions 10.13 and later, Ubuntu, Debian, CentOS, or Fedora (Linux).

Questions? Contact us for more information on requirements for your specific operating system.

5.    Is the course 100% online?

Yes, the course is entirely asynchronous and online—so you can study when and wherever you’d like so long as you can get online and complete the course on time.

But this doesn’t mean the learning experience is isolated or lonely! You’ll have your mentor, tutor, and student advisor there to support you—as well as access to our active student community on Slack.

6.    Is the course content practical and hands-on?

We take a rigorously practical approach to learning. You’ll have the opportunity to apply everything you learn in practical ways. Every exercise builds up to a completed portfolio project that your mentor will review and that will show employers the in-demand skills you learn in the course.

7.    How long does it take to complete the course?

If you set aside 15-20 hours per week to study, you’ll complete the course in approximately two months (eight weeks). If you’re able to devote 30-40 hours per week, you can complete the course in about a month (four weeks).

8.    What’s included in the course tuition?

This course offers immersive training in the field of machine learning—including expert-authored curriculum, hands-on projects, and personalized mentorship and support from experts in the field. Everything you need to stand out in the field as the specialist you’ll be.

Find out more here:

  • How it works: From the curriculum to your support team, and beyond—here are the details.
  • Meet our mentors: Get to know who the CareerFoundry mentors are and how the dual-mentorship model works.
  • Graduate outcomes: Here’s some of the work our graduates did in the full program—and where they’re at today.
9.    Are there payment plans available?

Yes, we offer two payment options for your specialization course. First, you can save a little money by paying your full tuition up front. If that’s not feasible, you can pay a set amount up front (varies depending on currency), and then the remainder in three monthly payments.

10.    Is the course accredited and what does ZFU-approved mean?

While the course is not university accredited, it does undergo a rigorous quality assurance and certification process with the ZFU (Staatliche Zentralstelle für Fernunterricht)—the state body for distance learning in Germany.

This process ensures that the course meets a high stand for an excellent and effective learning experience.

On successful completion of this certification process, the course is assigned a unique approval number (7442323) which can be checked against a public register.

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