Program comparison
Machine Learning Fundamentals in Python vs. Deep Learning in Python: which fits your learning goal?
For a Python learner with traditional machine-learning familiarity choosing broad model training or PyTorch deep-learning depth
Official sites. No commission.
For Python learners with traditional machine-learning familiarity deciding between broad model training and specialized PyTorch depth, DataCamp offers two beginner-level self-paced tracks. Machine Learning Fundamentals in Python surveys multiple approaches, while Deep Learning in Python concentrates entirely on neural networks. Both are accessed through the same platform, so curriculum scope and prior experience are useful starting points for the choice.
Facts side by side
Swipe or scroll the table to compare both programs.
| Fact | Machine Learning Fundamentals in Python | Deep Learning in Python |
|---|---|---|
| Price | DataCamp Premium · USD 35/month for course content, billed monthly at the regular price[1] | DataCamp Premium · USD 35/month for course content, billed monthly at the regular price[1] |
| Duration | 16 hours[2] | 18 hours[3] |
| Level | Beginner[2] | Beginner[3] |
| Credential | Statement of Accomplishment[2] | Statement of Accomplishment[3] |
| Format | Online self-paced learning track[2] | Online self-paced learning track[3] |
| Prerequisites | There are no prerequisites for this track[2] | There are no prerequisites for this track[3] |
Official sites. No commission.
Provider-listed modules and exam domains
Machine Learning Fundamentals in Python
- Supervised Learning with scikit-learn · Course; provider-listed track item. · [2]
- Predictive Modeling for Agriculture · Bonus project · [2]
- Unsupervised Learning in Python · Course; provider-listed track item. · [2]
- Clustering Antarctic Penguin Species · Bonus project · [2]
- Introduction to Deep Learning with PyTorch · Course; provider-listed track item. · [2]
- Reinforcement Learning with Gymnasium in Python · Course; provider-listed track item. · [2]
- Taxi Route Optimization with Reinforcement Learning · Bonus project · [2]
- Machine Learning Fundamentals in Python · Bonus skill assessment · [2]
Deep Learning in Python
- Introduction to Deep Learning with PyTorch · Course; provider-listed track item. · [3]
- Intermediate Deep Learning with PyTorch · Course; provider-listed track item. · [3]
- Building an E-Commerce Clothing Classifier Model · Bonus project · [3]
- Deep Learning for Images with PyTorch · Course; provider-listed track item. · [3]
- Deep Learning for Text with PyTorch · Course; provider-listed track item. · [3]
- Transformer Models with PyTorch · Course; provider-listed track item. · [3]
What the credentials are
Machine Learning Fundamentals in Python
- Credential type
- Statement of Accomplishment · [2]
- Credential
- Statement of Accomplishment · [2]
- Issuer
- DataCamp · [2]
- Sharing
- Add this credential to your LinkedIn profile, resume, or CV; share it on social media and in your performance review. · [2]
- Exam required
- provider does not state · [2]
- Credential expires
- provider does not state · [2]
- Renewal
- provider does not state · [2]
Deep Learning in Python
- Credential type
- Statement of Accomplishment · [3]
- Credential
- Statement of Accomplishment · [3]
- Issuer
- DataCamp · [3]
- Sharing
- Add this credential to your LinkedIn profile, resume, or CV; share it on social media and in your performance review. · [3]
- Exam required
- provider does not state · [3]
- Credential expires
- provider does not state · [3]
- Renewal
- provider does not state · [3]
Who should skip these options
Machine Learning Fundamentals in Python
Deep Learning in Python
- Learners starting from no machine-learning background should consider the narrative's recommendation for familiarity with traditional ML, even though the formal prerequisite section lists none. · [3]
- Learners choosing primarily for a one-time purchase: the sourced access route is a Premium subscription, with billing periods to consider. · [1]
Curriculum Scope and Practical Projects
Machine Learning Fundamentals in Python spans four courses covering supervised learning using scikit-learn, unsupervised learning, an introduction to deep learning with PyTorch, and reinforcement learning with Gymnasium. The curriculum supplements this broad instruction with three bonus projects predicting agricultural outcomes, clustering penguin species, and optimizing taxi routes, alongside one bonus skill assessment. Deep Learning in Python skips the broader landscape to focus exclusively on PyTorch across five courses. It begins with the same introductory course but quickly advances to intermediate deep learning, deep learning for images, deep learning for text, and transformer models. This deeper specialization includes one bonus project focused on building an e-commerce clothing classifier model. (Figures as checked on 8 September 2026.)
Time Commitment and Enrollment Access
Both programs are online, self-paced learning tracks that list no formal prerequisites. However, the target audiences differ based on prior knowledge. DataCamp notes the Deep Learning track is an ideal starting point specifically for those already familiar with traditional machine learning. The Machine Learning Fundamentals track is positioned broadly for anyone aspiring to become a machine learning engineer, data scientist, or AI researcher. The time basis for both programs is official track hours, which represent provider estimates rather than guaranteed completion times. Machine Learning Fundamentals lists 16 hours of listed track content. The Deep Learning track lists a slightly larger content workload of 18 hours. (Figures as checked on 8 September 2026.)
Subscription Cost and Credential Limitations
Both tracks have the same sourced Premium monthly access price. Enrollment requires a Premium subscription, which regularly costs USD 35. This monthly cost buys one month of access to the platform; it is not a guaranteed total cost to finish either track, and the amount paid depends on the billing route and subscription periods selected. The track completion artifact should be distinguished from a separately assessed certification. Students receive a Statement of Accomplishment upon finishing the courses, but the provider does not state any formal exam requirements, expiration dates, or renewal conditions for these completion records. (Figures as checked on 8 September 2026.)
Which to choose if…
Machine Learning Fundamentals in Python
Choose Machine Learning Fundamentals in Python if your goal is to survey supervised learning, unsupervised learning, and reinforcement learning with additional bonus projects across different application areas.
Deep Learning in Python
Choose Deep Learning in Python if you already understand traditional machine learning and want to focus your time entirely on PyTorch, image processing, text processing, and transformer models.
Alternatives in the registry
Related by topic or level.
- AI FundamentalsPrice: DataCamp Premium · USD 35/month for course content, billed monthly at the regular price[1]Duration: Approximately 9 hours[4]
- Keras FundamentalsPrice: DataCamp Premium · USD 35/month for course content, billed monthly at the regular price[1]Duration: 16 hours (Provider-listed track duration; not guaranteed elapsed completion time.)[5]
- Supervised Machine Learning in PythonPrice: DataCamp Premium · USD 35/month for course content, billed monthly at the regular price[1]Duration: 25 hours (Provider-listed track duration; not guaranteed elapsed completion time.)[6]
Sources
Editorial checked . No commercial relationship is documented for this comparison.
- www.datacamp.com/pricing?period=monthly · checked
- www.datacamp.com/tracks/machine-learning-fundamentals-with-python · checked
- www.datacamp.com/tracks/deep-learning-in-python · checked
- www.datacamp.com/tracks/ai-fundamentals · checked
- www.datacamp.com/tracks/keras-fundamentals · checked
- www.datacamp.com/tracks/supervised-machine-learning-in-python · checked
- www.datacamp.com/tracks/machine-learning-fundamentals-with-python
- www.datacamp.com/tracks/deep-learning-in-python