Course curriculum
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1
Chapter 1: Introductory Requirements
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Code 4 Tomorrow
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Hello!
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Setting Up Google Colab
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Libraries: Pandas, Numpy, ScikitLearn and Matplotlib
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Python Review: Loops, Sets, Tuples and Dictionaries
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Pandas and Data: Dataframes and Pandas Methods
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Quiz #1: Python Review
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2
Chapter 2: Machine Learning Overview
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The Machine Learning Pipeline
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Kinds of Learning
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What can Machine Learning Do?
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Quiz #2: An Overview of Machine Learning
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3
Chapter 3: Numpy/Scipy/MatPlotLib Functions
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Pandas 1: Importing Data
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Pandas 2: Dataframe Operations
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Pandas Quiz
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MatPlotLib 1: How to Scatter Plot
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MatPlotLib Quiz
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Math Review: Vectors and Matrices
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Quiz: Matrices and Vector Operations
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NumPy 1: 1D and 2D NumPy Arrays
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NumPy 2: Linear Algebra
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NumPy Quiz
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MatPlotLib 2: How to Plot
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4
Chapter 4: Training and Testing Data
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Training and Testing
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Overfitting and Underfitting
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Train/Test Split
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Train/Test Strategies
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5
Chapter 5: Simple Linear Regression
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What is Regression?
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Simple Linear Regression
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Optimization Approaches: Gradient Descent and Ordinary Least Squares
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Simple Linear Regression: Time, Marbles and Motion
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Quiz: Optimization Approaches
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6
Chapter 6: Evaluation Metrics
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Intro to Evaluation Metrics
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Eval. Metric 1: Mean Absolute Error
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Eval. Metric 2: Mean Squared Error
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Eval. Metric 3: Root Mean Squared Error
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Eval. Metric 4: Relative Absolute Error
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Eval. Metric 5: Relative Squared Error
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Eval. Metric 6: R squared Score
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Quiz: Evaluation Metrics
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7
Chapter 7: Multiple Linear Regression
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General Overview
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The Prediction Equation
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Key Tips Before Execution
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Multiple Linear Regression: Housing Prices in King County
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8
Chapter 8: Non-Linear Regression
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Polynomial Regression: The Idea
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Polynomial vs Multiple Regression
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PolynomialFeatures and Fit_transform
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Polynomial Regression
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Some Other Non-Linear Models
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Exponential and Sigmoidal Regression
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