Bang Ti Lenydoor

Machine Learning Fundamentals Professional Certificate

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About Course

Machine Learning Fundamentals teaches you how computers learn patterns from data and how to build, evaluate and explain machine learning models responsibly. You will work in Python with pandas and scikit-learn in Google Colab, a free tool that runs in your browser, so you do not need a powerful computer or any software installation.

You will learn the full workflow used by professional data scientists: framing a problem, preparing data, training regression and classification models, measuring their performance honestly, avoiding overfitting and data leakage, tuning models with cross-validation, and using ensembles and clustering. Every module uses realistic examples from agriculture, health, education, mobile money and microfinance in Africa.

This course is for learners who have basic Python skills (variables, lists, loops and functions) and some familiarity with statistics. If you are new to statistics, we recommend completing our Statistics for Data Analysis course first; if you are new to Python, start with Introduction to Python Programming.

How the course works: six modules, 23 lessons with more than 40 video explanations from respected educators, hands-on Colab exercises in every lesson, a quiz after every module, a capstone project in which you build and evaluate a loan-default prediction model for a microfinance institution, and a final certification examination. Most learners finish in 8 to 10 weeks at 4 to 5 hours per week.

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What Will You Learn?

  • Explain what machine learning is, where it works well and where it does not
  • Distinguish supervised, unsupervised and reinforcement learning
  • Prepare real data: handle missing values, encode categories and scale features
  • Split data correctly and avoid data leakage
  • Train and interpret linear regression, logistic regression, k-nearest neighbours and decision tree models
  • Evaluate models with MAE, RMSE, R-squared, confusion matrices, precision, recall, F1 and ROC-AUC
  • Diagnose overfitting and underfitting and use regularisation to control them
  • Tune models with cross-validation and grid search
  • Build random forest and gradient boosting ensembles
  • Find structure in unlabelled data with k-means clustering and PCA

Course Content

Module 1: What Machine Learning Is

  • What Is Machine Learning?
  • Types of Machine Learning
  • The Machine Learning Workflow
  • Setting Up: Python, Google Colab and scikit-learn
  • Module 1 Quiz: What Machine Learning Is

Module 2: Preparing Data for Machine Learning

Module 3: Regression Models

Module 4: Classification

Module 5: Better Models and Unsupervised Learning

Module 6: Capstone Project and Certification

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