Machine Learning Fundamentals
Learn the core principles of machine learning algorithms and applications. This course takes you from the foundations of supervised and unsupervised learning through model evaluation and a complete end-to-end project, building real models with Python and scikit-learn.
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This course includes:
- 52 hours on-demand video
- 64 articles and resources
- 40 downloadable datasets and notebooks
- 30 hands-on coding exercises
- Certificate of completion
- Lifetime access
- Access on mobile and TV
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About This Course
Machine Learning Fundamentals is an intermediate course that teaches the core principles behind modern machine learning algorithms and how to apply them to real problems. You'll learn how supervised, unsupervised, and reinforcement learning differ, and work through the complete machine learning workflow: framing a problem, preparing data, training models, and evaluating results.
Across 12 weeks (about 6 hours per week) you'll implement regression, classification, ensemble, and clustering algorithms in Python using scikit-learn, learn to diagnose overfitting and the bias-variance tradeoff, engineer features and build reproducible pipelines, and get a practical introduction to neural networks. The course concludes with an end-to-end capstone where you take a raw dataset all the way to a validated, deployed-ready model.
What You'll Learn
- Distinguish supervised, unsupervised, and reinforcement learning and choose the right approach
- Apply the end-to-end ML workflow from data preparation to model deployment
- Build and tune regression and classification models with scikit-learn
- Use ensemble methods such as random forests and gradient boosting
- Cluster and reduce dimensionality with k-means and PCA
- Evaluate models with cross-validation and diagnose bias, variance, and overfitting
- Engineer features and construct reproducible scikit-learn pipelines
Requirements
- Working knowledge of Python (variables, functions, loops, and libraries)
- Basic statistics: mean, variance, probability, and distributions
- We recommend completing Python for Data Analysis first if you are new to data work
- A computer able to run Python 3 with NumPy, pandas, and scikit-learn (or a free cloud notebook)
- Familiarity with Jupyter notebooks is helpful but not required
Who This Course Is For
- Data analysts who want to move into predictive modeling and machine learning
- Python developers looking to add ML skills to their toolkit
- Students and researchers who need a rigorous, practical ML foundation
- Professionals preparing for a data scientist or ML engineer role
- Anyone with Python and basic stats who wants to understand how ML algorithms actually work
Understand what machine learning is, how it differs from traditional programming, and the three main paradigms that frame the rest of the course.
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1.1 What Is Machine Learning?
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1.2 Supervised Learning Explained
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1.3 Unsupervised Learning Explained
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1.4 Reinforcement Learning Overview
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1.5 Real-World ML Applications and Limits
Learn the full lifecycle of an ML project and how to clean, split, and scale data so models train reliably.
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2.1 Framing a Machine Learning Problem
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2.2 Handling Missing Data and Outliers
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2.3 Encoding Categorical Variables
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2.4 Train/Test Splits and Data Leakage
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2.5 Exercise: Preparing a Real Dataset
Predict continuous values with linear regression and learn how regularization controls model complexity.
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3.1 Simple and Multiple Linear Regression
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3.2 Cost Functions and Gradient Descent
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3.3 Ridge, Lasso and Elastic Net Regularization
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3.4 Polynomial and Nonlinear Regression
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3.5 Exercise: Predicting House Prices
Assign data to discrete categories using the most widely used classification algorithms and understand their tradeoffs.
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4.1 Logistic Regression
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4.2 k-Nearest Neighbors
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4.3 Decision Trees
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4.4 Support Vector Machines
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4.5 Multiclass and Imbalanced Classification
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4.6 Exercise: Building a Spam Classifier
Combine many models to improve accuracy and robustness using bagging and boosting techniques.
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5.1 Bagging and the Bias-Variance Effect
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5.2 Random Forests
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5.3 Gradient Boosting and XGBoost
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5.4 Feature Importance from Ensembles
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5.5 Exercise: Boosting a Tabular Model
Find structure in unlabeled data through clustering and dimensionality reduction.
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6.1 k-Means Clustering
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6.2 Choosing k and Evaluating Clusters
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6.3 Hierarchical and Density-Based Clustering
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6.4 Principal Component Analysis (PCA)
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6.5 Exercise: Customer Segmentation
Measure how well models generalize and learn to control overfitting using cross-validation and the bias-variance tradeoff.
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7.1 Classification Metrics: Precision, Recall, ROC-AUC
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7.2 Regression Metrics and Residual Analysis
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7.3 Cross-Validation Strategies
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7.4 Bias-Variance Tradeoff and Overfitting
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7.5 Hyperparameter Tuning with Grid and Random Search
Transform raw inputs into useful features, build reproducible scikit-learn pipelines, and get a practical introduction to neural networks.
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8.1 Feature Engineering Techniques
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8.2 Feature Scaling and Selection
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8.3 Building scikit-learn Pipelines
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8.4 Introduction to Neural Networks and Perceptrons
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8.5 Backpropagation and Activation Functions
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8.6 Exercise: A First Neural Network Classifier
Bring every skill together to take a raw dataset all the way to a validated, well-documented model in a complete capstone project.
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9.1 Defining the Problem and Success Metrics
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9.2 Exploratory Data Analysis and Cleaning
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9.3 Model Selection and Tuning
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9.4 Evaluating and Interpreting the Final Model
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9.5 Packaging and Deploying a Model
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9.6 Capstone: Submit Your End-to-End Project
Dr. Priya Raman
Machine Learning Engineer & Researcher
About the Instructor
Dr. Priya Raman is a machine learning engineer and researcher with over 9 years of experience building production ML systems. She has worked on recommendation engines, fraud detection, and demand forecasting models serving millions of users, and currently leads an applied ML team at a data analytics company.
She holds a Ph.D. in Computer Science with a focus on statistical machine learning, and has published peer-reviewed work on model evaluation and feature selection. Priya has contributed to open-source ML tooling in the Python ecosystem and is a frequent speaker at data science meetups and conferences.
Her teaching emphasizes intuition before mathematics and hands-on practice with real datasets, so students understand not just how to call an algorithm but when and why to use it. She has taught machine learning to more than 20,000 students worldwide.
Other Courses by Dr. Priya Raman
Daniel Okafor
Exactly the course I needed to bridge the gap between knowing Python and actually building models. Dr. Raman explains the bias-variance tradeoff and cross-validation in a way that finally made them click. The scikit-learn pipeline module alone was worth the price.
Mei Lin
Solid, practical coverage of the core algorithms. The ensemble methods and classification modules were excellent. I knocked off one star because the neural networks section is just an introduction; I'd have loved more depth there. Still, a great foundation that prepared me well for an applied ML course.
Carlos Mendes
The end-to-end capstone is what sets this course apart. Taking a messy raw dataset all the way to a tuned, evaluated model gave me real confidence. The exercises use realistic datasets rather than toy examples, and the explanations of when to use each algorithm are spot on.
You should be comfortable writing basic Python and understand fundamental statistics such as mean, variance, and probability. No prior machine learning experience is required. If you are new to working with data in Python, we recommend taking our Python for Data Analysis course first so you can focus on the ML concepts here.
The course is designed to run over 12 weeks at roughly 6 hours per week, which includes about 52 hours of video plus the coding exercises and capstone project. Because you have lifetime access, you can move faster or slower; many students finish in 10 to 14 weeks depending on how much time they invest in the hands-on work.
All exercises use Python 3 with NumPy, pandas, Matplotlib, and scikit-learn, which are free and open source. The ensemble module also demonstrates gradient boosting with XGBoost. You can run everything locally with a standard Python install or use a free cloud notebook environment; no paid software is required.
This course focuses on the fundamentals of classical machine learning and includes a practical introduction to neural networks so you understand how they work. It is not a deep learning specialization. Once you complete it, you will have the foundation needed to take a dedicated deep learning course with confidence.
Yes. You will receive a certificate of completion once you finish all the lectures, exercises, and the capstone project. You can add this certificate to your LinkedIn profile or resume to demonstrate your machine learning skills to potential employers.
Absolutely. The course includes access to a Q&A discussion board where you can ask questions and get help from the instructor and fellow students. Dr. Raman typically responds within 24 to 48 hours. There is also a community Discord server where students share notebooks, datasets, and feedback.
In the capstone you take a raw, real-world dataset through the complete machine learning workflow:
- Frame the problem and define success metrics
- Clean the data and engineer features
- Train and compare several candidate models
- Tune hyperparameters and validate with cross-validation
- Evaluate, interpret, and package the final model for deployment
The result is a portfolio-ready project that demonstrates you can deliver an end-to-end machine learning solution.
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