About this episode
Feb 2017
MLG 007 Logistic Regression
<div> <p>The logistic regression algorithm is used for classification tasks in supervised machine learning, distinguishing items by class (such as "expensive" or "not expensive") rather than predicting continuous numerical values. Logistic regression applies a sigmoid or logistic ... Show More
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Feb 2017
MLG 008 Math for Machine Learning
<div> <p>Mathematics essential for machine learning includes linear algebra, statistics, and calculus, each serving distinct purposes: linear algebra handles data representation and computation, statistics underpins the algorithms and evaluation, and calculus enables the optimiza ... Show More
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MLG 009 Deep Learning
<p><a href= "https://ocdevel.com/walk?utm_source=podcast&utm_medium=mlg&utm_campaign=mlg9"> Try a walking desk</a> to stay healthy while you study or work!</p> <p>Full notes at <a href= "htts://ocdevel.com/mlg/9?utm_source=podcast&utm_medium=mlg&utm_campaign=mlg9"> ocdevel.com/ml ... Show More
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Jul 2022
Santiago Valderrama on Getting Smarter on Machine Learning, One Problem at a Time - Ep. 173
Want to learn about AI and machine learning? There are plenty of resources out there to help — blogs, podcasts, YouTube tutorials — perhaps too many. Machine learning engineer Santiago Valdarrama has taken a far more focused approach to helping us all get smarter about the field. ... Show More
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Dec 2019
Serverless NLP Model Training
<p class="p1"><span class="s1">Alex Reeves joins us to discuss some of the challenges around building a serverless, scalable, generic machine learning pipeline.<span class= "Apple-converted-space"> </span> The is a technical deep dive on architecting solutions and a discussion of ... Show More
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May 2021
474: The Machine Learning House
In this episode, I discuss the architecture of a “machine learning house”, representing the skills and learnings you can use as foundations to build your data science career.
Additional materials: www.superdatascience.com/474
5m 44s
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Creating instruction tuned models (Practical AI #223)
At the recent ODSC East conference, Daniel got a chance to sit down with Erin Mikail Staples to discuss the process of gathering human feedback and creating an instruction tuned Large Language Models (LLM). They also chatted about the importance of open data and practical tooling ... Show More
26m 33s
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
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:
