Build a real, practical foundation in AI & ML – from zero to your first deployed models.
Why This Bootcamp Matters Now
AI is no longer “nice to have” – it’s now baked into products, services, and internal tools across almost every industry. Global AI spending is expected to exceed USD 550 billion, yet organizations face an estimated 50% AI talent gap, meaning they simply can’t find enough people with real AI/ML skills to execute on their ambitions.
Across 15 countries, job ads that demand specialist AI skills (like machine learning and neural networks) have grown much faster than other roles, and AI-exposed jobs tend to offer higher wages and productivity. At the same time, surveys show most professionals think they can “use AI”, but only a small fraction actually have the technical skills to build or properly evaluate AI models.
Online learning platforms are seeing over 10× growth in GenAI course enrollments, proving that people are trying to upskill fast – but most “AI for everyone” courses stay at the surface and don’t build solid fundamentals in Python, data, and core ML algorithms.
This bootcamp exists to close that gap:
Not just “how to prompt ChatGPT”, but how AI & ML actually work under the hood, and how to build, evaluate, and improve real models using modern tools – the skills that make you genuinely employable and useful on real-world projects.
Overview of This Bootcamp
Purpose
Give participants a practical, hands-on foundation in AI and machine learning – enough to understand core concepts, build basic models, and collaborate confidently on real AI projects.
Format
- 3-day intensive, instructor-led bootcamp
- Mix of short theory blocks, live coding, guided labs, and mini-project work
- In-person delivery (format adaptable for organizations)
Core Focus Areas
- Python essentials for ML (NumPy, pandas, Matplotlib)
- Data cleaning, feature engineering, and exploratory data analysis (EDA)
- Supervised learning: regression & classification with scikit-learn
- Model evaluation (train/test splits, metrics, cross-validation)
- Overfitting, underfitting, and how to actually improve model performance
- Intro to unsupervised learning (clustering, dimensionality reduction)
- Gentle intro to deep learning (what neural networks are and when to use them)
- End-to-end mini project: from raw data to a simple, evaluated model
Prerequisites
- Comfortable with basic computer use
- Some exposure to programming helps, but no prior AI/ML experience is required
- We start from fundamentals and build up
Tools Used
- Python + Jupyter Notebooks
- NumPy, pandas, Matplotlib/Seaborn
- scikit-learn (main ML library)
Who Is It For?
This bootcamp is ideal for:
- Aspiring AI/ML engineers and data scientists starting their journey
- Software developers who want to add ML skills to their toolkit
- Data analysts / BI professionals who want to go beyond dashboards into predictive models
- Tech-curious professionals (product managers, consultants, startup founders) who want to understand how ML works at a technical enough level to talk to engineers
Students & fresh graduates in CS / AI / engineering who want a structured, practical intro to ML
How Participants Will Benefit
By the end of the bootcamp, participants will:
- Understand the core ideas of AI & ML: what makes something “machine learning,” what kinds of problems it can and can’t solve.
- Write and run ML code in Python, using industry-standard tools like pandas and scikit-learn.
- Build their first ML models from scratch: regression and classification on realistic datasets.
- Evaluate models properly, using the right metrics for the right problems (accuracy, precision/recall, F1, confusion matrices, RMSE, etc.).
- Recognize overfitting and underfitting and apply common fixes: more data, regularization, better features, cross-validation.
- Get a first mental model of deep learning, understanding what neural networks are and when it makes sense to use them.
Leave with reusable notebooks and templates they can adapt for future projects or interviews.
Bootcamp Structure & Detailed Agenda
Day 1 – AI & ML Foundations + Python for Data
Session 1: What Is AI, Really? (and What It Isn’t)
Description
- Big picture of AI vs. machine learning vs. deep learning
- Types of ML: supervised, unsupervised, (briefly) reinforcement learning
- The ML workflow: from problem → data → model → evaluation → iteration
Exercises
- Group activity: classifying examples into “good ML problem” vs “bad ML problem”
- Identify supervised vs unsupervised tasks from real-world scenarios
Outcomes
- Understand where ML fits in the broader AI landscape
- Recognize when ML is appropriate (and when it’s not)
Session 2: Python Essentials for Machine Learning
Description
Fast, practical Python refresher tailored to ML tasks.
Topics
- Python basics (variables, lists, loops, functions) – very compact
- Working in Jupyter notebooks
- NumPy basics (arrays, vectorized operations)
Exercises
- Write simple functions and manipulate arrays
- Convert a plain-Python loop to a vectorized NumPy operation
Outcomes
- Confidence using Python interactively in notebooks
- Ability to manipulate numeric data efficiently
Session 3: Data Handling with pandas
Description
Turn messy tables into structured, usable data.
Topics
- Loading CSV/Excel files into pandas
- Inspecting datasets (head, info, describe)
- Handling missing values and simple data cleaning
- Feature engineering basics (creating new columns, encoding categories)
Exercises
- Load a real sample dataset (e.g. housing prices or customer churn)
- Fix missing data, remove obvious errors, create a few new features
Outcomes
- Comfort with basic data cleaning & preparation
Understanding that data quality > model complexity
Session 4: Exploratory Data Analysis & Visualization
Description
See the story in your data before you even train a model.
Topics
- Summary statistics: mean, median, distributions
- Visualizing relationships: histograms, boxplots, scatterplots
- Identifying outliers and simple correlations
Exercises
- Build an EDA notebook: distributions, relationships to target variable
- Short reflection: “3 things I learned about this dataset”
Outcomes
- Ability to explore any tabular dataset and spot basic patterns
- Habit of doing EDA before modeling
Day 2 – Core Machine Learning: Regression & Classification
Session 1: Supervised Learning & scikit-learn Workflow
-
Description
Core pattern: split → fit → predict → evaluate.Topics
- Train/test split and why it’s critical
- Anatomy of a scikit-learn model (fit, predict, score)
- Baseline models and why they matter
Exercises
- Implement a basic regression model (e.g. predicting house price)
- Compare simple baseline vs trained model
Outcomes
- Ability to train a basic model end-to-end using scikit-learn
- Understanding of the ML experiment loop
- Train/test split and why it’s critical
Session 2: Regression in Practice
Description
Predict continuous values and understand what the model is doing.
Topics
- Linear regression intuition
- Loss functions (MSE / RMSE)
- Interpreting coefficients (at an intuitive level)
Exercises
- Train a linear regression model on a real dataset
- Compute RMSE, visualize predictions vs actuals
Outcomes
- Ability to set up and evaluate a regression task
- Understanding when linear models are (and aren’t) appropriate
Session 3: Classification in Practice
Description
Predict categories (e.g. churn / no churn, spam / not spam).
Topics
- Logistic regression and basic decision trees
- Classification metrics: accuracy, precision, recall, F1, confusion matrix
- Dealing with class imbalance (basic intuition only)
Exercises
- Train a classifier (e.g. churn prediction, loan approval)
- Plot confusion matrix and interpret errors
Outcomes
- Ability to frame and solve simple classification problems
Understanding that accuracy is not always the right metric
Session 4: Overfitting, Underfitting, and Model Improvement
Description
Why “more complex” is not always “better.”
Topics
- Bias–variance tradeoff
- Overfitting vs underfitting, learning curves (conceptual)
- Cross-validation, regularization (L2) at a high level
- Simple hyperparameter tuning with grid search
Exercises
- Deliberately overfit a small dataset with a deep tree, then fix it
- Use cross-validation scores to pick a better model
Outcomes
- Ability to recognize overfitting in practice
- First experience with systematically improving a model
Day 3 – Unsupervised Learning, Intro to Deep Learning & Mini-Project
Session 1: Unsupervised Learning & Clustering
Description
Find structure when you don’t have labels.
Topics
- Clustering intuition (k-means)
- Dimensionality reduction (PCA) – concept only
- Practical uses: customer segmentation, anomaly detection
Exercises
- Apply k-means to a small dataset
- Visualize clusters and discuss possible interpretations
Outcomes
- Understanding what unsupervised learning is good for
- Ability to run a basic clustering analysis
Session 2: Gentle Introduction to Neural Networks & Deep Learning
Description
What deep learning is, why it works, and when you might need it.
Topics
- From linear models to multi-layer networks
- Neurons, layers, activation functions – intuitively
- High-level view of training (forward pass, loss, backprop)
- Real-world applications: vision, NLP, recommendation systems
Exercises
- Walk through a simple feed-forward network example (e.g. using Keras)
- Compare a simple NN’s performance to a linear model on the same task
Outcomes
- Mental model of neural networks and deep learning
- Understanding when to reach for deep learning vs classical ML
Session 3: End-to-End Mini Project
Description
Apply everything learned to build a small but complete ML solution.
Flow
- Choose a problem (e.g. predict housing price, classify churn, etc.)
- Define objective, metrics, and success criteria
- Data cleaning & EDA
- Train baseline + improved models
- Evaluate, iterate, and document findings
Exercises
- Teams of 2–4 work on their own notebook
- Short “demo” at the end: 3–5 minutes per team presenting their approach and results
Outcomes
- Confidence in doing the full ML cycle on a new dataset
- A portfolio-ready notebook/project participants can show later
Session 4: Next Steps, Learning Path & Q&A
Description
Connect fundamentals to future growth.
Topics
- How to continue learning: recommended paths into
- Deep learning
- NLP
- Computer vision
- MLOps / deployment
- Deep learning
- How to build a simple portfolio of ML projects
- Q&A, common career questions
Outcomes
- Clear map of “what to do next” after the bootcamp
- Guidance on how to keep practicing and stay employable as AI evolves
Sample Hands-On Labs & Exercises
Participants will work on exercises such as:
- Cleaning and analyzing a real-world dataset (e.g. housing, customer churn, HR attrition)
- Building a regression model to predict a numeric outcome
- Building a classification model and interpreting the confusion matrix
- Running a k-means clustering analysis to segment data into groups
Completing a mini capstone project that ties everything together
Outcomes & Takeaways
Each participant will leave with:
- Multiple completed Jupyter notebooks with working code and comments
- A mini project suitable to include in a portfolio or GitHub
- A solid mental model of how AI/ML systems are built and evaluated
- Confidence to move into more advanced topics (deep learning, NLP, GenAI) without getting lost
You can optionally provide:
- A certificate of completion or digital badge
Suggested post-bootcamp challenge set (datasets + prompts) to keep them practicing
FAQs
1. Do I need prior coding experience?
Answer: Some basic familiarity with programming helps, but the bootcamp includes a focused Python primer. Motivated beginners can keep up, especially with hands-on guidance.
2. Is this a math-heavy course?
Answer: We focus on intuition first and only use the math needed to understand what’s going on (basic algebra, high-school level). No heavy proofs or advanced calculus.
3. Will I build real machine learning models?
Answer: Yes. Every day includes labs where you train and evaluate models on real datasets – not toy, contrived data.
4. Do we cover deep learning and neural networks?
Answer: Yes, at an introductory level. You’ll understand what they are, why they work, and run through at least one simple neural network example.
5. Is this bootcamp suitable for non-technical professionals?
Answer: Yes, if you’re comfortable learning some coding and are willing to get hands-on. Product managers, analysts, and startup founders often benefit a lot from seeing how ML actually works.
6. How is this different from free online courses?
Answer: Many online courses are either too theoretical or too rushed. This bootcamp is live, guided, and project-based – with an instructor who can answer questions, debug with you, and connect concepts to real work.
7. Will I be ready for a full ML engineer role after this?
Answer: This is a foundational starting point. You’ll understand the basics well enough to continue into more advanced tracks (deep learning, NLP, MLOps) and to contribute to beginner-level projects and interviews.
Ready to Take the Next Step?
- Schedule a call with our trainer
Haris Aamir
Trainer
Lincoln School