Data Science and Machine Learning
Data Analysis with Python and Pandas
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Data Science and Machine Learning
Machine Learning A-Z
Frame, build, evaluate, tune and deploy machine learning models for regression, classification and clustering with Python and scikit-learn.
About this course
Build intelligent systems. Master regression, classification, clustering, and model evaluation with hands-on machine learning projects.
What you will learn
- Decide whether a business problem suits machine learning and frame it with a target, metric and baseline
- Train and interpret linear, logistic, tree, random forest and gradient boosting models in scikit-learn
- Evaluate models honestly with train-test splits, cross-validation and the right classification or regression metrics
- Segment data with k-means clustering and simplify features with PCA
- Engineer features and build leak-free pipelines with ColumnTransformer, then tune them with randomised search
- Save a trained pipeline and serve predictions through a simple, validated API with a monitoring plan
Curriculum
Module 1ML Foundations4 lessons
- 1What Machine Learning Is and Is Not Free preview
- 2Supervised vs Unsupervised Learning
- 3The Train-Test Mindset
- 4Setting Up Your ML Environment
- QModule quiz (4 questions)
Module 2Supervised Learning4 lessons
- 5Linear and Logistic Regression
- 6Decision Trees and Random Forests
- 7Model Evaluation Metrics
- 8Overfitting and Regularisation
- QModule quiz (4 questions)
Module 3Unsupervised and Beyond4 lessons
- 9Clustering with K-Means
- 10Dimensionality Reduction (PCA)
- 11Feature Engineering That Wins
- 12Ensembles and Boosting
- QModule quiz (4 questions)
Module 4ML in Practice4 lessons
- 13The End-to-End ML Pipeline
- 14Hyperparameter Tuning
- 15Deploying a Simple Model
- 16Course Project: Predictive Model Build
- QModule quiz (4 questions)
PFinal project: End-to-End Predictive Model for a Business Decision
Who this course is for
- Beginners with basic Python who want a complete, practical grounding in machine learning
- Data analysts ready to move from reporting to prediction
- Developers and business professionals who want to build or evaluate ML projects
Requirements
- A computer that can run Python 3 with scikit-learn, or a Google account to use Colab
- Basic Python and pandas: loading data, selecting columns and simple functions
- School-level maths; all statistics and model ideas are explained in the lessons
What is included
Written lessons
16 lessons, each with a practical exercise and key points to remember.
Module quizzes
A short quiz at the end of each module. Pass mark 70%, and you can retake it.
Final project
End-to-End Predictive Model for a Business Decision. A practical brief with steps and a way to check your own work.
Certificate
A certificate of completion from EMFAD Courses with a unique code anyone can verify.
How the certificate works
Complete every lesson and pass every module quiz with 70% or more. Your certificate then appears in your learner area, ready to print or save as PDF. It shows your name, the course title, the completion date and a unique code. It is a certificate of completion from EMFAD Courses, not an accredited qualification.
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