Program in Machine Learning & Applied Data Science

Categories: AI & Automation
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About Course

Turn business data into models that support better, measurable decisions.

This program teaches the full applied data science workflow, from framing a business question to monitoring a model in production. You will analyze data with Python and pandas, explore and visualize it, apply the statistics that machine learning depends on, and build supervised and unsupervised models with scikit-learn. You will also learn to evaluate, tune and explain models honestly, then package and monitor them. All courses are self-paced, use free tools such as Google Colab and JupyterLite, and end with a predictive model built for a real business decision.

60 hours · 9 courses, from Explore to Advance, ending with a final project · Certificate of completion · 12-month access

Who it is for

Analysts, small-business owners, operations and marketing professionals, and developers who want to use data and machine learning to support decisions in their organizations.

Prerequisites

Basic spreadsheet skills and high-school math are enough to start; MLD102 covers the Python you need.

What you will be able to do

  • Frame a business problem as a data science task with clear success metrics
  • Clean, explore and visualize data with Python, pandas and matplotlib
  • Build, evaluate, tune and explain regression, classification and clustering models
  • Deploy a simple model and monitor its data and performance over time

Courses in this program

CourseLevelHoursPrice
MLD101 · The Data Science Workflow and Problem FramingExplore4 h$28.00
MLD102 · Python and pandas for Data AnalysisExplore4 h$28.00
MLD103 · Exploratory Data Analysis and VisualizationBuild6 h$42.00
MLD104 · Statistics for Machine LearningBuild6 h$42.00
MLD105 · Supervised Learning: Regression and ClassificationAdvance8 h$56.00
MLD106 · Unsupervised Learning: Clustering and SegmentationAdvance8 h$56.00
MLD107 · Model Evaluation, Tuning and ExplainabilityAdvance8 h$56.00
MLD108 · Putting Models to Work: Deployment and MonitoringAdvance8 h$56.00
MLD109 · Final Project: A Predictive Model for a Business DecisionAdvance8 h$56.00

Buy the complete program for 10% less than the courses separately, or start with one course and continue at your own pace.

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What Will You Learn?

  • Frame a business problem as a data science task with clear success metrics
  • Clean, explore and visualize data with Python, pandas and matplotlib
  • Build, evaluate, tune and explain regression, classification and clustering models
  • Deploy a simple model and monitor its data and performance over time

Course Content

MLD101 · The Data Science Workflow and Problem Framing (4 h)
Learn how data science projects really run, from a vague business question to a framed problem with a clear target, decision, metric and baseline.

  • Lesson 1 · The data science workflow
  • Lesson 2 · From business question to data science problem
  • Lesson 3 · Metrics, baselines and data readiness
  • Lesson 4 · Lab and assignment
  • MLD101 Assessment · The Data Science Workflow and Problem Framing

MLD102 · Python and pandas for Data Analysis (4 h)
Use Python notebooks and pandas to load, clean, combine and summarize real business data faster and more reliably than with spreadsheets.

MLD103 · Exploratory Data Analysis and Visualization (6 h)
Explore data systematically to find patterns, problems and opportunities, then turn findings into clear charts that support business decisions.

MLD104 · Statistics for Machine Learning (6 h)
The statistics that machine learning depends on: distributions, sampling, uncertainty, hypothesis tests, A/B testing and the bias-variance trade-off, taught with code and business examples.

MLD105 · Supervised Learning: Regression and Classification (8 h)
Build regression and classification models with scikit-learn, from linear and logistic regression to random forests and gradient boosting, using clean, leak-free pipelines.

MLD106 · Unsupervised Learning: Clustering and Segmentation (8 h)
Find natural groups in customers, products and transactions with clustering and dimensionality reduction, and turn them into segments a business can act on.

MLD107 · Model Evaluation, Tuning and Explainability (8 h)
Evaluate models with metrics that match business costs, validate and tune them without fooling yourself, and explain their behavior to the people who rely on them.

MLD108 · Putting Models to Work: Deployment and Monitoring (8 h)
Turn a trained model into a dependable service or batch job, track experiments and versions, and monitor data drift and performance so the model keeps earning its place.

MLD109 · Final Project: A Predictive Model for a Business Decision (8 h)
Frame, build, evaluate, explain and package a predictive model that supports one real business decision, bringing together the full data science workflow.

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