The most rapidly expanding occupations globally currently involve AI and machine learning. This trend is unsurprising given the continuous deployment of new technologies that are transforming global operations through automation and machine intelligence.
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In addition to possessing fundamental abilities in mathematics, computer science, and soft skills like problem-solving and communication, proficiency in programming is essential in the AI and machine learning domain—particularly [ppp1].
Python is a crucial component of the daily tasks of machine learning specialists, as highlighted by Ratinder Paul Singh Ahuja, CTO and VP at [ppp2]. He specifically emphasizes its diverse range of libraries and their respective functions:
- Data processing: Pandas and NumPy
- Model building and training: TensorFlow and PyTorch
- Data and model outcome visualization: Matplotlib and Seaborn
- Deployment: Flask and Django
The best practices in the dynamic field of AI may vary depending on the day, the task at hand, and the organization. Therefore, establishing foundational skills and the ability to distinguish oneself are crucial in this sector.
For individuals seeking to delve into machine learning and Python, there are numerous online resources available, many of which are free. Platforms like [ppp3], [ppp4], and [ppp5] offer valuable learning opportunities. Even watching YouTube tutorials and exploring GitHub repositories can be beneficial.
Rakesh Anigundi, Ryzen AI product lead at [ppp6], believes that focusing on core technical skills and the ability to differentiate oneself can open up ample opportunities for AI enthusiasts in the market. Given the complexity of the field and the job market, companies are also navigating the landscape to determine the most valuable skills for product development and problem-solving. Staying ahead of the curve can significantly boost one’s career prospects.
For those interested in a deeper exploration of machine learning with Python, Fortune has compiled a list of available options, which predominantly offer self-paced learning experiences with varying costs and durations.
5 free and paid resources to advance in machine learning with Python
freeCodeCamp:
Participants can access hours of free machine learning videos and engage in interactive projects such as building a book recommendation engine, a neural network SMS text classifier, and a cat and dog image classifier.
Cost: Free
Length: Self-paced; 36 lessons + 5 projects
Course examples: Tensorflow; Deep Learning Demystified
HarvardX:
This introductory course on machine learning and AI, hosted on edX, allows learners to explore algorithms, neural networks, and natural language processing. The course, part of Harvard’s computer science for artificial intelligence professional certificate program, offers video transcripts in multiple languages.
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:
IBM’s data scientists guide students through machine learning algorithms, Python classification techniques, and data regressions. Prior knowledge of Python, data analysis, data visualization, and high school-level mathematics is recommended.
Cost: $49/month
Length: Approximately 12 hours
Module examples: Regression; Classification; Clustering
Stanford/DeepLearning.ai:
Instructors from Stanford University and DeepLearning.ai, including AI expert Andrew Ng, provide nearly 100 hours of content on machine learning fundamentals and real-world AI applications, with a focus on Silicon Valley. Basic coding experience and knowledge of high school-level mathematics are recommended.
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:
A professor from the University of Michigan’s school of information and college of engineering offers insights into machine learning, including regressions, classifications, neural networks, and more. This course is designed for individuals with existing knowledge in data and AI and is part of a specialization in data science methods and techniques.
Cost: $49/month
Length: Approximately 31 hours
Course examples: Fundamentals of Machine Learning; Supervised Machine Learning; Evaluation
For additional details on these resources and to explore further options, visit Fortune.