People interested in machine learning can choose between self-guided learning, online certification programs such as MOOCs, accredited university degrees, and doctoral research, with industry acceptance and personal goals influencing which path is most appropriate. Industry employers currently prioritize a strong project portfolio over non-accredited certificates, and while master's degrees carry more weight for job applications, PhD programs are primarily suited for research interests rather than industry roles.

Links

• Notes and resources at ocdevel.com/mlg/6
• Try a walking desk - stay healthy & sharp while you learn & code

Learner Types and Self-Guided Education

• Individuals interested in machine learning may be hobbyists, aspiring professionals, or scientists wishing to contribute to research in artificial intelligence.
• Hobbyists can rely on structured resources, including curated syllabi and recommended online materials, to guide their self-motivated studies.
• The "Andrew Ng Coursera" course is frequently recommended as an initial step for self-learners, and advanced resources such as "Artificial Intelligence: A Modern Approach" and "Deep Learning" textbooks are valuable later.

MOOCs and Online Certificates

• MOOCs (Massive Open Online Courses) are widely available from platforms such as Coursera, Udacity, edX, and Khan Academy, but only Coursera and Udacity are commonly recognized for machine learning and data science content.
• Coursera is typically recommended for individual courses; its specializations are less prominent in professional discussions.
• Udacity offers both free courses and paid "nano degrees" which include structured mentoring, peer interaction, and project-based learning.
• Although Udacity certificates demonstrate completion and the development of practical projects, they lack widespread recognition or acceptance from employers.
• Hiring managers and recruiters consistently emphasize the value of a substantial project portfolio over non-accredited certificates for job-seekers.

University Degrees and Industry Recognition

• Master's degrees in machine learning or computer science remain the most respected credentials for job applications in the industry, with requirements often officially listed in job postings.
• The Georgia Tech OMSCS program provides an accredited, fully online Master's degree in Computer Science at a much lower cost than traditional programs, reportedly leveraging Udacity's course infrastructure.
• In some cases, a strong portfolio can substitute for formal educational requirements, particularly if the applicant demonstrates practical and scalable machine learning project experience.
• Portfolio strength is considered analogous to web development hiring, where demonstrated skills and personal projects can compensate for missing degree credentials.

PhD Pathways and Research Careers

• A PhD is generally unnecessary for industry positions in machine learning; a master's degree or an exceptional portfolio is usually adequate.
• Doctoral degrees are most useful for those seeking research roles or wishing to investigate complex theoretical questions in artificial intelligence, rather than working in standard industry applications.
• PhD programs pay a stipend to students, though the compensation is much less than typical industry salaries, which should factor into an individual's decision-making process.

Considerations and Resources

• Choosing an educational path depends on individual goals, available resources, and desired career trajectory; a portfolio of significant machine learning projects is universally beneficial regardless of the chosen route.
• Community discussions and recruiter perspectives suggest that practical skills, proven through real-world projects, are highly valued in addition to or in place of formal degrees.
• Interested individuals can review ongoing discussions and perspectives:
  • Self-Guided Data Science Guide (canyon289)
  • Hacker News – Are credentials required?
  • Cole MacLean's Self-Taught AI Blog
  • Hacker News: Self-Study Paths