A bootcamp curriculum tells you what’s taught. It doesn’t always tell you what you’ll be able to do afterward, or how to prove it to an employer. This post takes the AI and ML BootCamp’s nine modules and translates them into the machine learning skills you’ll actually build, the work they show up in, and the evidence you can point to in an interview.
Machine learning skills, module by module
| Skill | Where it’s taught | Where it shows up at work |
|---|---|---|
| Framing a business question as an analytics problem | Fundamentals of data analytics | Turning “why are customers leaving?” into something a model can answer |
| Reading data with statistics | Statistics, basic and advanced | Spotting when a difference is real and when it’s noise, using hypothesis tests and distributions |
| Working with data in Python | Python essentials | Loading, reshaping, and plotting data with NumPy, Pandas, and Matplotlib |
| Cleaning and preparing data | Data science with Python | Handling missing values, outliers, imbalanced classes, and feature engineering |
| Building and evaluating predictive models | Supervised learning | Regression and classification, judged with confusion matrices and ROC curves |
| Finding structure without labels | Unsupervised learning | Customer segmentation with clustering, and reducing features with PCA |
| Building neural networks | Deep learning | Image classification with CNNs and OpenCV, sequence models with RNNs and LSTMs |
| Working with text | Natural language processing | Sentiment analysis, topic models, and simple chatbots |
Where the time goes
Number of modules per skill area
Source: AgileFever AI and ML BootCamp curriculum, October 2026. Grouping is ours.
More than half the program is about understanding and handling data before any model gets trained. That’s where most real machine learning work goes, and it’s what interviewers probe when they ask you to walk through a project.
How to show these skills to an employer
A certificate says you finished a course. These say you can do the work:
| Skill | Evidence that works |
|---|---|
| Data cleaning and EDA | A notebook that explains each cleaning decision and the reason for it alongside the code |
| Model evaluation | A short write-up comparing two models, with the metric you chose and why it fits the business problem |
| Deep learning | Your capstone or a CNN project, with notes on what you tried when accuracy stalled |
| Communicating results | A one-page summary a non-technical manager could follow |
| End-to-end ownership | The capstone: face mask detection or customer churn prediction, from raw data to a working result |
The program includes 13+ domain projects in areas such as healthcare, fintech, and retail. You don’t need all of them on your GitHub. Two or three well-explained projects in the domain you’re applying to will do more than thirteen with no write-up.
Skills this program doesn’t focus on
It’s worth knowing the limits, so you can plan what comes next:
- Deploying and monitoring models in production. The MLOps BootCamp covers this
- Building LLM applications and AI agents. The GenAI and Agentic AI BootCamp covers this
- Large-scale data pipelines. The Data Engineer BootCamp covers this
Cost and dates
The AI and ML BootCamp is 80 hours over 10 weeks, currently $1,600 in the US (regular $2,000) or ₹85,000 in India (regular ₹1,00,000), with EMI from $66 a month. Upcoming cohorts start on October 26 and November 28, 2026.
Additional discounts are available. Contact us to know more.
What to read next
For the full program, read AI and ML BootCamp: The Complete Guide. If you’re new to coding, see Is the AI and ML BootCamp right for career switchers?
