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Machine Learning Course

Section 4 — Introduction to Optimisation and the Gradient Descent Algorithm

  • Cost Functions
  • Creating Charts with Subplots
  • Gradient Descent Algorithm
  • Optimisation
  • Understanding the Learning Rate
  • Creating 3-Dimensional Charts
  • Understanding Partial Derivatives and How to use SymPy
  • Batch Gradient Descent with SymPy
  • Concatenating Numpy Arrays
  • The Mean Squared Error (MSE)
  • A MSE Cost Function
  • Gradient Descent with a MSE Cost Function

Section 5 — Predict House Prices with Multivariable Linear Regression

  • Index Data, Pandas Series, and Dummy Variables
  • Descriptive Statistics: the Mean vs the Median
  • Correlation: Understanding Strength & Direction
  • Calculating Correlations and the Problem posed by Multicollinearity
  • Visualizing Correlations with a Heatmap
  • Seaborn Pairplots & Jupyter Microbenchmarking Techniques
  • Multivariable Regression
  • How to Shuffle and Split Training & Testing Data
  • How to Calculate the Model Fit with R-Squared
  • Model Evaluation
  • How to Interpret Coefficients using p-Values and Statistical Significance
  • VIF & Testing for Multicollinearity
  • Model Simplification & Bayesian Information Criterion
  • How to Analyze and Plot Regression Residuals
  • Build a Valuation Tool: Working with Pandas Series & Numpy ndarrays

Sections 6 — Pre-Process Text Data for a Naive Bayes Classifier to Filter Spam Emails: Part 1

  • The Naive Bayes Algorithm and the Decision Boundary for a Classifier
  • Basic Probability
  • Joint & Conditional Probability
  • Bayes Theorem
  • Introduction to Natural Language Processing (NLP)
  • Tokenizing, Removing Stop Words and the Python Set Data Structure
  • Word Stemming & Removing Punctuation
  • Removing HTML tags with BeautifulSoup
  • Advanced Subsetting on DataFrames: the apply() Function
  • Logical Operators to Create Subsets and Indices
  • Word Cloud with Python
  • Sparse Matrix (Part 1): Split the Training and Testing Data
  • Sparse Matrix (Part 2): Data Munging with Nested Loops
  • Sparse Matrix (Part 3): Using groupby() and Saving .txt Files

Sections 7 — Train a Naive Bayes Classifier to Create a Spam Filter: Part 2

  • Create a Full Matrix
  • Count the Tokens to Train the Naive Bayes Model

Sections 8 — Test and Evaluate a Naive Bayes Classifier: Part 3

  • Joint Conditional Probability (Part 1): Dot Product
  • Joint Conditional Probability (Part 2): Priors
  • Making Predictions: Comparing Joint Probabilities
  • The Accuracy Metric
  • False Positive vs False Negatives
  • The Recall Metric, Precision Metric, F-score or F1 Metric
  • A Naive Bayes Implementation using SciKit Learn