Go from “I know Python and data” to “I can build and evaluate real ML models.”
Why This Workshop Matters
Machine learning has moved from R&D labs into everyday products: recommendation engines, fraud detection, forecasting systems, chatbots, quality control, and more. Roles like Machine Learning Engineer and Data Scientist remain among the most in-demand AI jobs across industries.
Most serious introductory ML curricula from Google, Coursera (Andrew Ng), Kaggle, and others converge on the same foundations:
- Supervised learning (regression & classification)
- Unsupervised learning basics (clustering, dimensionality reduction)
- Model evaluation, overfitting vs underfitting
- Practical workflows with libraries like scikit-learn
However, many learners get stuck in theory or scattered tutorials and never reach the point where they can take a dataset, choose a reasonable model, train it, and know whether it’s any good.
This workshop is designed to close that gap.
Applied Machine Learning 101 focuses on classic, practical ML: you will learn the core concepts, the standard workflow, and enough hands-on practice to start building your own models with confidence.
Call us to learn more about this applied machine learning 101 workshop
Workshop Snapshot
Purpose
Give participants a clear, practical understanding of the machine learning workflow and the ability to build, evaluate, and improve basic ML models using Python and scikit-learn.
Format
- 2-day, in-person, instructor-led workshop
- Mix of short concept sessions, live demos, and guided labs
- Strong emphasis on practice and intuition, not heavy math
Level
- Beginner–friendly for people who already know basic Python and data handling
- Ideal next step after a “Python for Data Science”–type workshop
Core Topics
- Framing problems as ML tasks
- Supervised learning: regression & classification
- The standard ML workflow with scikit-learn
- Model evaluation and metrics
- Overfitting, underfitting, and model improvement
- Intro to unsupervised learning (clustering)
- High-level view of model selection and next steps
Core Tools
- Python + Jupyter Notebooks
- pandas (for data handling)
scikit-learn (for modeling and evaluation)
Who This Workshop Is For
This workshop is ideal for:
- Early-stage AI/ML learners who know some Python and want to build real models
- Data & BI analysts who want to move from descriptive analytics into predictive modeling
- Developers and engineers who want to understand ML workflows and integrate models into products
- Students and fresh graduates in AI, CS, IT, statistics, or related fields
- Tech-curious professionals (e.g. product managers, consultants, founders) who want to understand ML deeply enough to work effectively with ML teams
Recommended prerequisites
- Basic Python (variables, functions, lists/dicts)
- Basic familiarity with pandas and simple data manipulation
If you’ve completed a Python/data workshop or equivalent self-study, you’re ready.
What Participants Will Learn
By the end of Applied Machine Learning 101, participants will be able to:
- Recognize common ML problem types and frame real-world questions as supervised or unsupervised learning tasks
- Use a standard ML workflow: split data, train models, evaluate, iterate
- Build regression models to predict numeric outcomes and interpret basic results
- Build classification models to predict categories and understand confusion matrices and key metrics
- Select and apply appropriate evaluation metrics (accuracy, precision, recall, F1, RMSE, etc.)
- Identify overfitting vs underfitting and use simple strategies to improve generalization
- Get a first, intuitive feel for unsupervised learning (e.g. clustering) and where it fits
- Document and present a small end-to-end ML project in a clear, professional way
They won’t just “know what ML is”—they’ll have actually built and evaluated models themselves.
Workshop Agenda
(Detailed enough for clients to see the value, flexible enough to adapt to different datasets and domains.)
Day 1 – Framing Problems & Supervised Learning
Session 1 – Machine Learning in Practice
- How ML is used in real systems (recommendations, risk scoring, forecasting, etc.)
- Types of ML: supervised, unsupervised, reinforcement (high-level)
- What makes a good ML problem: labels, data, and clear objectives
- Overview of the ML lifecycle: data → features → model → evaluation → deployment → monitoring
Outcome:
Participants can look at a problem and say: “This is a classification/regression problem; here’s roughly how I’d frame it for ML.”
Session 2 – The scikit-learn Workflow
- Datasets: features vs target
- Train/test splits (and why evaluating on the training set is misleading)
- The fit → predict → evaluate pattern in scikit-learn
- Concept of baselines and why they matter
Outcome:
Participants understand the basic “shape” of a modern ML workflow as used across many introductory courses and industry tutorials.
Session 3 – Regression: Predicting Numeric Values
- When to use regression (forecasts, prices, scores, etc.)
- Intuition for linear regression (fitting a line/plane to data)
- Loss and error (MSE/RMSE at a conceptual level)
- Reading basic outputs and sanity-checking predictions
Outcome:
Participants can set up and evaluate a simple regression model and interpret key error metrics in plain language.
Session 4 – Classification: Predicting Categories
- When to use classification (churn, approval, fraud vs not fraud, etc.)
- Logistic regression and tree-based models (high-level intuition)
- Evaluation metrics: accuracy, precision, recall, F1, ROC/AUC (conceptual)
- Why accuracy alone can be misleading (class imbalance)
Outcome:
Participants understand how to measure model quality in classification problems and what trade-offs metrics represent.
Day 2 – Evaluation, Overfitting & Unsupervised Learning
Session 5 – Model Evaluation & Improvement
- Overfitting vs underfitting: how they appear in practice
- The bias–variance idea (intuitive explanation)
- Cross-validation as a more robust evaluation strategy
- Basic hyperparameter tuning (grid search / simple adjustments)
Outcome:
Participants can recognize when a model is “too simple” vs “too complex” and know a few levers they can use to improve performance without memorizing formulas.
Session 6 – Intro to Unsupervised Learning (Clustering)
- What unsupervised learning is (no labels, structure discovery)
- Clustering intuition (e.g. k-means) and when it’s useful (segmentation, grouping patterns)
- Concept of dimensionality reduction (PCA) at a very high level
Outcome:
Participants know where unsupervised learning fits, what kinds of problems it can help with, and how it differs from supervised ML.
Session 7 – Putting It Together: End-to-End ML Walkthrough
- Start with a (generic) tabular dataset
- Choose a prediction task and define success criteria
- Prepare the data (features, target, simple cleaning)
- Train a baseline model, then a slightly improved one
- Evaluate and compare results
- Reflect on trade-offs and next steps
This session is less about following step-by-step instructions and more about seeing the whole flow at once, with participants working along in their own notebooks.
Outcome:
Participants see how all the pieces connect and feel ready to attempt similar projects on other datasets.
Session 8 – Project Framing & Next Steps
- How to frame future ML mini-projects for learning and portfolios
- Examples of beginner-friendly ML projects (without prescribing exact datasets)
- How to document experiments: notebooks, readmes, simple reports
- Suggested learning paths: deeper ML, deep learning, NLP, MLOps, etc., connected to widely-used online resources (Google ML Crash Course, Coursera, fast.ai, Kaggle)
Outcome:
Participants leave with a clear idea of how to keep practicing and how to turn workshop experience into tangible progress toward AI/ML roles.
Call us to learn more about this applied machine learning 101 workshop
What Participants Take Away
After Applied Machine Learning 101, participants will have:
- A clear mental model of how machine learning works end-to-end
- Experience training and evaluating both regression and classification models
- A practical understanding of overfitting, underfitting, and model evaluation
- A first exposure to unsupervised learning and when to consider it
- Well-structured notebooks and templates they can adapt to new datasets
- The confidence to tackle small ML projects independently and to progress into more advanced AI courses
FAQs
1. How is this different from a Python or data analysis workshop?
Answer: A Python/data workshop focuses on working with data: loading, cleaning, and visualizing. Applied Machine Learning 101 assumes you can already do that at a basic level and focuses on choosing, training, and evaluating models.
2. Do I need strong math skills?
Answer: No advanced math is required. We use just enough algebra and basic statistics to explain what models are doing, with an emphasis on intuition and practice.
3. Is this course enough to become a Machine Learning Engineer?
Answer: This is an on-ramp, not the final destination. It gives you solid foundations and practical experience so you can continue into more advanced ML, deep learning, and MLOps training with confidence.
4. Which tools will I be using?
Answer: You’ll work with an industry-standard stack used in many leading courses and real projects: Python, Jupyter Notebooks, pandas, and scikit-learn.
5. Can this workshop be adapted to our industry or data?
Answer: Yes. While the structure stays the same, the examples and datasets can be tailored to domains like finance, retail, manufacturing, education, or public services.
Ready to Take the Next Step?
- Schedule a call with our trainer
Haris Aamir
Trainer
Lincoln School