Become a Machine Learning Master.
Starting with the basics, ending at mastery. We've got you covered every step of the way.
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And finally, our Course Material.

Chapter 1: Introduction to Machine Learning
Get introduced to Machine Learning by learning about the fundamentals of ML, Linear Regression, and more in ScikitLearn.
💻1: Introduction to Machine Learning ... We're so proud of this course.
💻2: Data Visualization ... Graph it up!
🧠3: Recall 1
💻4: Correlation Coefficient ... One of many useful statistical concepts.
🧠5: Recall 2
💻6: Inputs and Outputs ... Most models have inputs and predict outputs.
🧠7: Recall 3
💻8: Simple Linear Regression ... The simplest model.
🧠9: Recall 4
💻10: Visualizing SLR ... So dang useful.
🧠11: Recall 5
💻12: Model Parameters ... Very important concept.
🧠13: Recall 6
💻14: NumPy Vectorization ... Just a warmup. Feel free to skip!
💻15: Residuals ... This is how we define error in a regression model.
🧠16: Recall 7
💻17: Mean Squared Error ... The most common error function.
🧠18: Recall 8
✏️19: Final Assessment

Chapter 2: Machine Learning Fundamentals
Take your ML to the next level by incorporating feature engineering techniques such as Ordinal and OneHot encoding, and preprocessing methods such as Standardization.
💻1: The Design Matrix ... Our input array used to train the model.
🧠2: Recall 1
💻3: Multiple Linear Regression ... The most classic ML algorithm.
🧠4: Recall 2
💻5: Regression Metrics ... There's a bunch of them. MSE is the most important one.
🧠6: Recall 3
💻7: Categorical Variables ... We'll teach this properly unlike many resources.
🧠8: Recall 4
💻9: Ordinal Categorical Variables ... Variables with an order to them.
💻10: Nominal Categorical Variables ... Variables without an order to them.
🧠11: Recall 5
💻12: Preprocessing Methods ... Functions that scale (transform) numerical columns.
🧠13: Recall 6
💻14: Bringing it all Together ... Let's make one heck of a model!
✏️15: Final Assessment

Chapter 3: NonLinear Machine Learning
Unleash your inner ML God by mastering advanced Statistical Learning concepts such as Underfitting vs Overfitting, and the infamous BiasVariance Tradeoff.
💻1: NonLinear Models ... Straight is boring.
💻2: Linear vs NonLinear Models ... Which is better? It depends.
🧠3: Recall 1
💻4: Training Set vs Test Set  Part 1 ... Make sure you understand this!
💻5: Training Set vs Test Set  Part 2 ... Make sure you understand this!
🧠6: Recall 2
💻7: Overfitting ... You've made a model that's TOO good.
💻8: Underfitting ... You've made a model that's not good enough.
🧠9: Recall 3
💻10: Picking the Better Model ... We care about the Test Set!
💻11: Finding the Best Model ... Hyperparameter Tuning.
🧠12: Recall 4
💻13: The BiasVariance Tradeoff ... The Holy Grail of Machine Learning.
🧠14: Recall 5
✏️15: Final Assessment

Chapter 4: Classification Models
Unlock ultimate ML power by transferring all your knowledge over to the other side of machine learning: Classification.
💻1: Regression vs Classification ... The difference lies in the output variable.
🧠2: Recall 1
💻3: Logistic Regression ... The most classic classification algorithm.
💻4: Visualizing Logistic Regression ... We'll actually graph the probabilities.
🧠5: Recall 2
💻6: Decision Boundaries ... They're so cool.
🧠7: Recall 3
💻8: Confusion Matrix ... Don't let it confuse you too!
🧠9: Recall 4
💻10: Precision & Recall ... 2 Common Binary Classification Metrics.
💻11: F1Score ... An allencompassing metric.
🧠12: Recall 5
💻13: Binary Crossentropy Function ... Classification algorithms optimize this.
💻14: Picking the Best Model ... Similar to in regression, just different metrics.
✏️15: Final Assessment

Chapter 5: Bonus!
Become a Machine Learning Master by learning MultiClass Classification and Unsupervised methods such as Clustering and Dimensionality Reduction.
💻1: Introduction to MultiClass Classification ... The binary stuff extends to > 2.
💻2: MultiClass Classification Metrics ... Generalized metrics.
🧠3: Recall 1
💻4: HoldOut Set ... We actually need a third dataset split.
💻5: Train Set / Validation Set / Holdout Set ... We'll use that third dataset split.
🧠6: Recall 2
💻7: Cross Validation ... The ultimate ML training. But it has a tradeoff...
🧠8: Recall 3
💻9: Learning the Models ... A quick summary of popular ones.
💻10: Regularization ... A technique we use to help prevent overfitting.
🧠11: Recall 4
💻12: A Taste of Forecasting / Time Series ... Sorry we can't do any more.
🧠13: Recall 5
💻14: Unsupervised Learning: Clustering ... A taste of unsupervised learning.
🧠15: Recall 6
💻16: Dimensionality Reduction (PCA) ... More unsupervised learning.
🧠17: Recall 7
✏️18: Final Assessment