What background is recommended?

Comfort with Python and foundational data analysis is recommended. Familiarity with Data Science Fundamentals will help you get the most from the course.

How long is the course?

The course runs for seven weeks and includes fourteen instructor-led classes.

Which tools will I use?

You will work with Python and the scikit-learn ecosystem for data preparation, model training, validation, and evaluation.

You will develop machine-learning workflows for prediction and classification problems using real-world datasets.

Yes. It is instructor-led and available in person in Boston or live online.

Will I receive a certificate?

Students who meet the attendance and project requirements receive a Codeliq Academy certificate of completion.

Select Apply Now, complete the application form, and the Codeliq Academy team will contact you with next steps.

Machine Learning

Learn to design, train, and evaluate machine learning models for real-world applications.

Duration: 7 weeks (14 classes)
Founding price: $2,000

Machine Learning • scikit-learn • Regression • Classification • Clustering • Model Evaluation •Machine Learning • scikit-learn • Regression • Classification • Clustering • Model Evaluation •Machine Learning • scikit-learn • Regression • Classification • Clustering • Model Evaluation •Machine Learning • scikit-learn • Regression • Classification • Clustering • Model Evaluation •

Course Overview

Learn to turn data into tested, useful machine-learning models.

Across seven weeks, you will build, validate, and interpret models using practical scikit-learn workflows and real-world datasets.

Model: Build supervised and unsupervised learning solutions.
Validate: Use sound data-splitting and evaluation practices.
Interpret: Translate model results into better decisions.

What You’ll Learn

Build practical skills in machine learning with Python, from data preparation and model training to evaluation and interpretation.

ML Foundations

Understand supervised and unsupervised learning for real-world problems.

Model Building

Train regression, classification, clustering, and support-vector models.

Evaluation

Validate model performance and make informed model-selection decisions.

Applied ML

Build reliable workflows for prediction and classification tasks.

Curriculum

7 weeks (14 classes). Live instructor-led classes.

Week 01
Machine Learning Foundations
Frame problems and understand core learning paradigms.
1. Supervised, unsupervised, and reinforcement learning
2. Problem framing, datasets, features, and targets
Week 02
Data Preparation
Prepare trustworthy inputs for model development.
1. Train-test splits, cross-validation, and preprocessing
2. Feature scaling, encoding, and leakage prevention
Week 03
Regression Models
Predict continuous outcomes with interpretable models.
1. Linear regression and regularization
2. Regression metrics and error analysis
Week 04
Classification Models
Solve binary and multiclass prediction problems.
1. Logistic regression, decision trees, and SVMs
2. Confusion matrices, precision, recall, and ROC curves
Week 05
Unsupervised Learning
Discover structure in unlabeled data.
1. K-Means clustering and dimensionality reduction
2. Cluster evaluation and practical interpretation
Week 06
Model Selection
Improve performance while avoiding overfitting.
1. Hyperparameter tuning and pipelines
2. Bias, variance, regularization, and model comparison
Week 07
Applied ML Project
Build and present a complete machine-learning workflow.
1. Train, evaluate, and refine a project model
2. Present findings, limitations, and next steps

7 weeks14 live classes1 capstone project

Frequently Asked Questions

Everything you need to know before joining.

This course is for learners who want to move from data analysis into building predictive and classification models.

Machine Learning Course

Build models that matter

Join the cohort and learn a disciplined, practical approach to machine learning.

Machine Learning

Course Overview

Build practical skills in machine learning with Python, from data preparation and model training to evaluation and interpretation.

ML Foundations

Model Building

Evaluation

Applied ML

7 weeks (14 classes). Live instructor-led classes.

Machine Learning Foundations

Data Preparation

Regression Models

Classification Models

Unsupervised Learning

Model Selection

Applied ML Project

Everything you need to know before joining.

01Who is this Machine Learning course for?

02What background is recommended?

03How long is the course?

04Which tools will I use?

05What will I build?

06Is the course live?

07Will I receive a certificate?

08How do I apply?

Build models that matter