Q: Will I earn university credit for completing the Specialization?

This Specialization doesn't carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.

Question 1

How long does it take to complete the Specialization?

Accepted Answer

This self-paced Specialization typically takes 9–10 weeks at 3–4 hours per week (≈ 27–40 hours). Weeks 1–3 cover Python setup, NumPy, and Matplotlib/Seaborn with a mini visualization project; Weeks 4–6 focus on supervised/unsupervised ML and MLPs (TensorFlow/Keras/PyTorch) with a model-comparison notebook; Weeks 7–9 apply predictive analytics and NLP (clustering, classification, NLTK) in an end-to-end project. Week 10 is a buffer to refine notebooks and polish portfolio-ready docs. Plan for two 90-minute sessions weekly; fast-track in 7–8 weeks if experienced, or extend to 12 weeks for a lighter pace.

Question 2

What background knowledge is necessary?

Accepted Answer

Basic Python (variables, lists, loops, functions) and comfort with Jupyter/Anaconda are recommended. High-school math (algebra, functions, basic statistics) is sufficient; light familiarity with vectors/matrices and probability helps but isn’t required. No prior AI/ML experience is assumed.

Question 3

Do I need to take the courses in a specific order?

Accepted Answer

Yes—complete them sequentially for the best experience. If you already use NumPy and can create plots in Matplotlib/Seaborn, you may start at Course 2; if you’ve trained and evaluated classifiers before, you can jump to Course 3.

Question 4

What will I be able to do upon completing the Specialization?

Accepted Answer

You’ll set up a Python AI stack and wrangle data with NumPy, then create clear analytics visuals with Matplotlib/Seaborn. You’ll build, tune, and explain supervised and unsupervised ML models (e.g., logistic regression, SVMs, clustering), implement MLPs with TensorFlow/Keras/PyTorch, and evaluate models with metrics like confusion matrices and clustering scores. You’ll also design an NLP pipeline in NLTK (tokenization, NER, parsing) and deliver portfolio-ready, reproducible notebooks that communicate findings to stakeholders.

Question 5

Is this course really 100% online? Do I need to attend any classes in person?

Accepted Answer

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Question 6

What is the refund policy?

Accepted Answer

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

Question 7

Can I just enroll in a single course?

Accepted Answer

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Question 8

Is financial aid available?

Accepted Answer

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Question 9

Can I take the course for free?

Accepted Answer

No, you cannot take this course for free. When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you cannot afford the fee, you can apply for financial aid.

Question 10

Will I earn university credit for completing the Specialization?

Accepted Answer

This Specialization doesn&#x27;t carry university credit, but some universities may choose to accept Specialization Certificates for credit. Check with your institution to learn more.