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Ace Skills Consulting
Intermediate

Machine Learning with Python

Build, evaluate and explain machine learning models in Python that solve real business problems.

  • 40 hours of live, trainer-led sessions
  • scikit-learn pipelines from day one
  • Proper validation, tuning and model evaluation
  • Explainability and responsible AI practices
  • Capstone: an end-to-end model served as an API
Duration
5 Days | 40 Hrs
Next batch
Batches on request
Delivery
Live Online · Onsite · Corporate
Level
Intermediate
Language
English

Why learn this course with Ace Skills

  • Practical machine learning

    Focus on the workflow and judgement that make models useful, not just the algorithms.

  • Honest evaluation

    Learn to validate properly, avoid leakage and pick metrics that match the business goal.

  • Responsible by design

    Explain predictions and check for bias before a model reaches users.

  • End-to-end capstone

    Take a model from raw data to a working API you can demonstrate.

Course overview

Machine learning is most useful when it is built carefully: the right problem framing, honest evaluation and models that people can understand and trust. This five-day course teaches the practical workflow that data scientists use, using Python and scikit-learn.

Day one covers the machine learning workflow, problem framing and preparing data — feature engineering, encoding, scaling and building reproducible pipelines. Day two focuses on supervised learning for regression and classification, from linear and logistic regression to decision trees, and on avoiding data leakage.

Day three introduces ensemble methods such as random forests and gradient boosting, cross-validation, hyperparameter tuning and the right metrics for imbalanced problems. Day four covers unsupervised learning — clustering, dimensionality reduction and anomaly detection — plus model explainability with feature importance and SHAP, and the fairness and privacy questions every model raises.

Day five is a capstone project: you frame a business problem, build and compare models, explain the result and package the final model behind a simple API, with an introduction to experiment tracking and monitoring.

This is an original Ace Skills course with no vendor exam. On completion you receive an Ace Skills Consulting course completion certificate. It builds on our Python for Data Science course and leads naturally into Generative AI and agentic AI training.

Key features

  • Complete ML workflow from framing to deployment
  • Reusable scikit-learn pipelines
  • Cross-validation and hyperparameter tuning
  • Gradient boosting with XGBoost or LightGBM
  • Explainability with SHAP
  • Responsible AI checklist
  • Capstone project served as an API
  • Course completion certificate from Ace Skills Consulting

Who should attend

  • Data analysts moving into data science
  • Software engineers adding ML to their skills
  • Python for Data Science graduates
  • Business intelligence professionals
  • Technical managers leading data teams

Prerequisites

  • Working knowledge of Python and pandas
  • Basic statistics: mean, variance, correlation
  • Our Python for Data Science course or equivalent

Curriculum

5 Days · 40 Hours · 14 Modules · 11 Labs

  1. Foundations

    • What machine learning can and cannot do
    • Supervised vs unsupervised learning
    • Framing a business problem
    • Train, validation and test splits

    Exploratory analysis

    • Target and feature exploration
    • Data quality checks
    • Spotting leakage early
    • Lab: Explore a customer churn dataset · 40 min

    Feature engineering

    • Encoding categorical data
    • Scaling and transformation
    • Handling missing values
    • Date and text features
    • scikit-learn pipelines and ColumnTransformer
    • Lab: Build a reusable preprocessing pipeline · 50 min

  2. Regression

    • Linear regression
    • Regularisation: ridge and lasso
    • Regression metrics
    • Lab: Predict house prices with regularised regression · 45 min

    Classification

    • Logistic regression
    • k-nearest neighbours
    • Decision trees
    • Probability thresholds
    • Lab: Predict churn with three classifiers · 45 min

    Evaluating classifiers

    • Confusion matrix
    • Precision, recall and F1
    • ROC and PR curves
    • Choosing a metric for the business

  3. Ensemble methods

    • Bagging and random forests
    • Gradient boosting
    • XGBoost and LightGBM
    • Lab: Compare random forest and gradient boosting · 45 min

    Validation and tuning

    • k-fold and stratified cross-validation
    • Grid and randomised search
    • Learning curves
    • Overfitting and underfitting
    • Lab: Tune a model with cross-validated search · 45 min

    Imbalanced data

    • Class weights
    • Resampling techniques
    • Cost-sensitive evaluation
    • Lab: Build a fraud detector on imbalanced data · 45 min

  4. Clustering

    • k-means
    • Hierarchical clustering
    • DBSCAN
    • Evaluating clusters
    • Lab: Segment customers with clustering · 45 min

    Dimensionality reduction and anomalies

    • PCA
    • Visualising high-dimensional data
    • Isolation forest for anomaly detection

    Explainability and responsible AI

    • Global and local explanations
    • Permutation importance and SHAP
    • Fairness checks
    • Privacy and documentation with model cards
    • Lab: Explain and audit the churn model · 45 min

  5. From notebook to service

    • Saving and loading models
    • Serving predictions with FastAPI
    • Experiment tracking with MLflow
    • Monitoring for drift
    • Lab: Serve a model behind a REST API · 50 min

    Capstone project

    • Problem framing
    • Model building and comparison
    • Explaining results to stakeholders
    • Lab: End-to-end capstone with presentation · 150 min
Download full syllabus (PDF)

Skills you will gain

  • Machine learning
  • scikit-learn
  • Feature engineering
  • Regression
  • Classification
  • Ensemble methods
  • Cross-validation
  • Hyperparameter tuning
  • Clustering
  • PCA
  • Model explainability
  • Responsible AI
  • Model deployment

Tools & technologies

  • Python
  • Jupyter Notebook
  • pandas
  • scikit-learn
  • XGBoost
  • LightGBM
  • SHAP
  • MLflow
  • FastAPI

Schedule & Pricing

Live Online

Individual

Price on request

  • 40 hours of live, trainer-led sessions
  • 11 hands-on labs
  • Ace Skills course completion certificate

Onsite

At your office or our centre

Pricing depends on location, group size and dates.

  • Delivered at your premises or an Ace centre
  • Same curriculum and labs as live online
  • Dates planned around your team

Corporate / Team

Private batch for your organisation

A private batch with content adapted to your environment and goals.

  • Customised curriculum
  • Private batch, online or onsite
  • Progress and assessment reports
  • GST invoice and PO-based billing

No public batches scheduled — Talk to an Advisor for a private batch.

Projects & labs you will build

  • Day 1 · 50 min

    Build a reusable preprocessing pipeline

  • Day 3 · 45 min

    Build a fraud detector on imbalanced data

  • Day 5 · 150 min

    End-to-end capstone with presentation

Your certificate

Certificate of Completion — Ace Skills Consulting

Awarded on completing the course. It records the course title and training hours, and you can share it on your LinkedIn profile.

Frequently asked questions

Is there an exam?

No. This is an original Ace Skills course with no vendor exam; you receive an Ace Skills course completion certificate.

How much maths do I need?

School-level algebra and basic statistics are enough. Intuition is emphasised over formulas.

Does this cover deep learning?

It introduces where deep learning fits but focuses on classical machine learning, which solves most tabular business problems.

Will I deploy a model?

Yes. The final day packages your capstone model behind a simple API and introduces tracking and monitoring.

Is this useful for cloud ML certifications?

Yes. The concepts underpin cloud ML exams such as the AWS Machine Learning Engineer – Associate, which we also teach.

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