Learn The Ways Of Machine Learning With Python Through One Of These 5 Courses And Specializations

Explore the realm of machine learning with Python through a curated selection of five courses and specializations. These resources offer comprehensive training, covering foundational principles to advanced applications, enabling learners to acquire essential skills and expertise in utilizing Python for machine learning endeavors.

The fastest growing jobs in the world right now are ones dealing with AI and machine learning. That’s according to the World Economic Forum.

This should come at no surprise as new technology is being deployed practically on the daily that is revolutionizing the ways in which the globe works through automation and machine intelligence.

Beyond having foundational skills in mathematics and computer science and soft skills like problem-solving and communication, core to the AI and machine learning space is programming—specifically Python. The programming language is one of the most in-demand for all tech experts.

Python plays an integral part of machine learning specialists’ everyday tasks, says Ratinder Paul Singh Ahuja, CTO and VP at Pure Storage. He specifically points its diverse set of libraries and their relevant roles:

• Data processing: Pandas and NumPy
• Model building and training: TensorFlow and PyTorch
• Data and model outcome visualization: Matplotlib and Seaborn 
• Deployment: Flask and Django

As you can imagine, the best practices in the everchanging AI may differ depending on the day, task, and company. So, building foundational skills overall—and being able to differentiate yourself—is important in the space.

The good news for those who are looking to learn the ropes in the machine learning and Python space, there are seemingly endless ways to gain knowledge online—and even for free.

For those exploring the subject on your own, resources like W3Schools, Kaggle, and Google’s crash course are good options. Even as simple as watching YouTube videos and checking out GitHub can be useful.

“I think if you focus on core technical skills, and also the ability to differentiate, I think that there’s still plenty of opportunity for AI enthusiasts to get into the market,” says Rakesh Anigundi, Ryzen AI product lead at AMD.

Anigundi adds that because the field and job market is so complicated, even companies themselves are trying to figure out what are the most useful skills to build products and solve problems. So, doing anything you can to stay ahead of the game can be part of what helps propel your career.

For those looking for a little bit of a deeper dive into machine learning with Python, Fortune has listed some of the options on the market; they’re largely self-paced but vary slightly in terms of price and length.

5 free and paid ways to get ahead in machine learning with Python

freeCodeCamp: Machine Learning with Python

Participants can watch hours of free videos about machine learning. At the end, each course has one learning multiple-choice question. Users are provided five different challenges to take on. The interactive projects include the creation of a book recommendation engine, neural network SMS text classifier, and cat and dog image classifier.  

Cost: Free

Length: Self-paced; 36 lessons + 5 projects

Course examples: Tensorflow; Deep Learning Demystified

HarvardX: Machine Learning and AI with Python

Hosted with edX, this introductory course allows students to learn about machine learning and AI straight from two of Harvard’s expert computer science professors. Participants are exposed to topics like algorithms, neutral networks, and natural language processing. Video transcripts are also notably available in nearly a dozen other languages. For those wanting to learn more, the course is part of Harvard’s computer science for artificial intelligence professional certificate program.

Cost: Free (certificate available for $299)

Length: 6 weeks (4–5 hours/week)

Course learning goals: Explore advanced data science; train models; examine result; recognize data bias

IBM: Machine Learning with Python course

Data scientists from IBM guide students through machine learning algorithms, Python classifications techniques, and data regressions. Participants are recommended to have a working knowledge of Python, data analysis, and data visualization as well as high school-level mathematics. 

Cost: $49/month

Length: 12 hours (approximately)

Module examples: Regression; Classification; Clustering

Stanford/DeepLearning.ai: Machine Learning Specialization

With nearly 100 hours of content, instructors from Stanford University and DeepLearning.ai, including renowned AI and edtech leader Andrew Ng, walk students through the foundations of machine learning. It also focuses on the applications of AI into the real world, especially Silicon Valley. Participants are recommended to have some basic coding experience with knowledge of high school-level mathematics.

Cost: $49/month

Length: 2 months (10 hours/week)

Course examples: Supervised Machine Learning: Regression and Classification; Advanced Learning Algorithms; Unsupervised Learning, Recommenders, Reinforcement Learning

University of Michigan: Applied Machine Learning in Python

A professor from the University of Michigan’s school of information and college of engineering teaches students the ins and outs of machine learning, with discussion of regressions, classifications, neural networks, and more. The course is for individuals with already some existing knowledge in the data and AI world. It  is part of a larger specialization focused on data science methods and techniques.

Cost: $49/month

Length: 31 hours (approximately)

Course examples: Fundamentals of Machine Learning; Supervised Machine Learning; Evaluation

Source: Fortune