Introduction to Course
Python Crash Course
➢ OPTIONAL: Python Crash Course
➢ Python Crash Course - Part One
➢ Python Crash Course - Part Two
➢ Python Crash Course - Part Three
➢ Python Crash Course - Exercise Questions
➢ Python Crash Course - Exercise Solutions
➢ Introduction to NumPy
➢ NumPy Arrays
➢ Coding Exercise Check-in: Creating NumPy Arrays
➢ NumPy Indexing and Selection
➢ Coding Exercise Check-in: Selecting Data from Numpy Array
➢ NumPy Operations
➢ Check-In: Operations on NumPy Array
➢ NumPy Exercises
➢ Numpy Exercises – Solutions
➢ Introduction to Pandas
➢ Series - Part One
➢ Check-in: Labeled Index in Pandas Series
➢ Series - Part Two
➢ DataFrames - Part One - Creating a DataFrame
➢ DataFrames - Part Two - Basic Properties
➢ DataFrames - Part Three - Working with Columns
➢ DataFrames - Part Four - Working with Rows
➢ Pandas - Conditional Filtering
➢ Pandas - Useful Methods - Apply on Single Column
➢ Pandas - Useful Methods - Apply on Multiple Columns
➢ Pandas - Useful Methods - Statistical Information and Sorting
➢ Missing Data - Overview
➢ Missing Data - Pandas Operations
➢ GroupBy Operations - Part One
➢ GroupBy Operations - Part Two - MultiIndex
➢ Combining DataFrames - Concatenation
➢ Combining DataFrames - Inner Merge
➢ Combining DataFrames - Left and Right Merge
➢ Combining DataFrames - Outer Merge
➢ Pandas - Text Methods for String Data
➢ Pandas - Time Methods for Date and Time Data
➢ Pandas Input and Output - CSV Files
➢ Pandas Input and Output - HTML Tables
➢ Pandas Input and Output - Excel Files
➢ Pandas Input and Output - SQL Databases
➢ Pandas Pivot Tables
➢ Pandas Project Exercise Overview
➢ Pandas Project Exercise Solutions
➢ Introduction to Matplotlib
➢ Matplotlib Basics
➢ Matplotlib - Understanding the Figure Object
➢ Matplotlib - Implementing Figures and Axes
➢ Matplotlib - Figure Parameters
➢ Matplotlib-Subplots Functionality
➢ Matplotlib Styling - Legends
➢ Matplotlib Styling - Colors and Styles
➢ Advanced Matplotlib Commands (Optional)
➢ Matplotlib Exercise Questions Overview
➢ Matplotlib Exercise Questions – Solutions
➢ Introduction to Seaborn
➢ Scatterplots with Seaborn
➢ Distribution Plots - Part One - Understanding Plot Types
➢ Distribution Plots - Part Two - Coding with Seaborn
➢ Categorical Plots - Statistics within Categories - Understanding Plot Types
➢ Categorical Plots - Statistics within Categories - Coding with Seaborn
➢ Categorical Plots - Distributions within Categories - Understanding Plot Types
➢ Categorical Plots - Distributions within Categories - Coding with Seaborn
➢ Seaborn - Comparison Plots - Understanding the Plot Types
➢ Seaborn - Comparison Plots - Coding with Seaborn
➢ Seaborn Grid Plots
➢ Seaborn - Matrix Plots
➢ Seaborn Plot Exercises Overview
➢ Seaborn Plot Exercises Solutions
➢ Capstone Project overview
➢ Capstone Project Solutions - Part One
➢ Capstone Project Solutions - Part Two
➢ Capstone Project Solutions - Part Three
➢ Introduction to Machine Learning Overview Section
➢ Why Machine Learning?
➢ Types of Machine Learning Algorithms
➢ Supervised Machine Learning Process
➢ Companion Book - Introduction to Statistical Learning
➢ Introduction to Linear Regression Section
➢ Linear Regression -Algorithm History
➢ Linear Regression - Understanding Ordinary Least Squares
➢ Linear Regression - Cost Functions
➢ Linear Regression - Gradient Descent
➢ Python coding Simple Linear Regression
➢ Overview of Scikit-Learn and Python
➢ Linear Regression - Scikit-Learn Train Test Split
➢ Linear Regression - Scikit-Learn Performance Evaluation - Regression
➢ Linear Regression - Residual Plots
➢ Linear Regression - Model Deployment and Coefficient Interpretation
➢ Polynomial Regression - Theory and Motivation
➢ Polynomial Regression - Creating Polynomial Features
➢ Polynomial Regression - Training and Evaluation
➢ Bias Variance Trade-Off
➢ Polynomial Regression - Choosing Degree of Polynomial
➢ Polynomial Regression - Model Deployment
➢ Regularization Overview
➢ Feature Scaling
➢ Introduction to Cross Validation
➢ Regularization Data Setup
➢ L2 Regularization -Ridge Regression - Theory
➢ L2 Regularization - Ridge Regression - Python Implementation
➢ L1 Regularization - Lasso Regression - Background and Implementation
➢ L1 and L2 Regularization - Elastic Net
➢ Linear Regression Project - Data Overview
➢ A note from Jose on Feature Engineering and Data Preparation
➢ Introduction to Feature Engineering and Data Preparation
➢ Dealing with Outliers
➢ Dealing with Missing Data : Part One - Evaluation of Missing Data
➢ Dealing with Missing Data : Part Two - Filling or Dropping data based on Rows
➢ Dealing with Missing Data : Part 3 - Fixing data based on Columns
➢ Dealing with Categorical Data - Encoding Options
➢ Section Overview and Introduction
➢ Cross Validation - Test | Train Split
➢ Cross Validation - Test | Validation | Train Split
➢ Cross Validation - cross_val_score
➢ Cross Validation - cross_validate
➢ Grid Search
➢ Linear Regression Project Overview
➢ Linear Regression Project – Solutions
➢ Early Bird Note on Downloading .zip for Logistic Regression Notes
➢ Introduction to Logistic Regression Section
➢ Logistic Regression - Theory and Intuition - Part One: The Logistic Function
➢ Logistic Regression - Theory and Intuition - Part Two: Linear to Logistic
➢ Logistic Regression - Theory and Intuition - Linear to Logistic Math
➢ Logistic Regression - Theory and Intuition - Best fit with Maximum Likelihood
➢ Logistic Regression with Scikit-Learn - Part One - EDA
➢ Logistic Regression with Scikit-Learn - Part Two - Model Training
➢ Classification Metrics - Confusion Matrix and Accuracy
➢ Classification Metrics - Precison, Recall, F1-Score
➢ Classification Metrics - ROC Curves
➢ Logistic Regression with Scikit-Learn - Part Three - Performance Evaluation
➢ Multi-Class Classification with Logistic Regression - Part One - Data and EDA
➢ Multi-Class Classification with Logistic Regression - Part Two - Model
➢ Logistic Regression Exercise Project Overview
➢ Logistic Regression Project Exercise – Solutions
➢ Introduction to KNN Section
➢ KNN Classification - Theory and Intuition
➢ KNN Coding with Python - Part One
➢ KNN Coding with Python - Part Two - Choosing K
➢ KNN Classification Project Exercise Overview
➢ KNN Classification Project Exercise Solutions
➢ Introduction to Support Vector Machines
➢ History of Support Vector Machines
➢ SVM - Theory and Intuition - Hyperplanes and Margins
➢ SVM - Theory and Intuition - Kernel Intuition
➢ SVM - Theory and Intuition - Kernel Trick and Mathematics
➢ SVM with Scikit-Learn and Python - Classification Part One
➢ SVM with Scikit-Learn and Python - Classification Part Two
➢ SVM with Scikit-Learn and Python - Regression Tasks
➢ Support Vector Machine Project Overview
➢ Support Vector Machine Project Solutions
➢ Introduction to Tree Based Methods
➢ Decision Tree - History
➢ Decision Tree - Terminology
➢ Decision Tree - Understanding Gini Impurity
➢ Constructing Decision Trees with Gini Impurity - Part One
➢ Constructing Decision Trees with Gini Impurity - Part Two
➢ Coding Decision Trees - Part One - The Data
➢ Coding Decision Trees - Part Two -Creating the Model
➢ Introduction to Random Forests Section
➢ Random Forests - History and Motivation
➢ Random Forests - Key Hyperparameters
➢ Random Forests - Number of Estimators and Features in Subsets
➢ Random Forests - Bootstrapping and Out-of-Bag Error
➢ Coding Classification with Random Forest Classifier - Part One
➢ Coding Classification with Random Forest Classifier - Part Two
➢ Coding Regression with Random Forest Regressor - Part One - Data
➢ Coding Regression with Random Forest Regressor - Part Two - Basic Models
➢ Coding Regression with Random Forest Regressor - Part Three - Polynomials
➢ Coding Regression with Random Forest Regressor - Part Four - Advanced Models
➢ Introduction to Boosting Section
➢ Boosting Methods - Motivation and History
➢ AdaBoost Theory and Intuition
➢ AdaBoost Coding Part One - The Data
➢ AdaBoost Coding Part Two - The Model
➢ Gradient Boosting Theory
➢ Gradient Boosting Coding Walkthrough
➢ Introduction to Supervised Learning Capstone Project
➢ Solution Walkthrough - Supervised Learning Project - Data and EDA
➢ Solution Walkthrough - Supervised Learning Project - Cohort Analysis
➢ Solution Walkthrough - Supervised Learning Project - Tree Models
➢ Introduction to NLP and Naive Bayes Section
➢ Naive Bayes Algorithm - Part One - Bayes Theorem
➢ Naive Bayes Algorithm - Part Two - Model Algorithm
➢ Feature Extraction from Text - Part One - Theory and Intuition
➢ Feature Extraction from Text - Coding Count Vectorization Manually
➢ Feature Extraction from Text - Coding with Scikit-Learn
➢ Natural Language Processing - Classification of Text - Part One
➢ Natural Language Processing - Classification of Text - Part Two
➢ Text Classification Project Exercise Overview
➢ Text Classification Project Exercise Solutions
➢ Unsupervised Learning Overview
➢ Introduction to K-Means Clustering Section
➢ Clustering General Overview
➢ K-Means Clustering Theory
➢ K-Means Clustering - Coding Part One
➢ K-Means Clustering Coding Part Two
➢ K-Means Clustering Coding Part Three
➢ K-Means Color Quantization - Part One
➢ K-Means Color Quantization - Part Two
➢ K-Means Clustering Exercise Overview
➢ K-Means Clustering Exercise Solution - Part One
➢ K-Means Clustering Exercise Solution - Part Two
➢ K-Means Clustering Exercise Solution - Part Three
➢ Introduction to Hierarchical Clustering
➢ Hierarchical Clustering - Theory and Intuition
➢ Hierarchical Clustering - Coding Part One - Data and Visualization
➢ Hierarchical Clustering - Coding Part Two - Scikit-Learn
➢ Introduction to DBSCAN Section
➢ DBSCAN - Theory and Intuition
➢ DBSCAN versus K-Means Clustering
➢ DBSCAN - Hyperparameter Theory
➢ DBSCAN - Hyperparameter Tuning Methods
➢ DBSCAN - Outlier Project Exercise Overview
➢ DBSCAN - Outlier Project Exercise Solutions
➢ Introduction to Principal Component Analysis
➢ PCA Theory and Intuition - Part One
➢ PCA Theory and Intuition - Part Two
➢ PCA - Manual Implementation in Python
➢ PCA - SciKit-Learn
➢ PCA - Project Exercise Overview
➢ PCA - Project Exercise Solution
➢ Model Deployment Section Overview
➢ Model Deployment Considerations
➢ Model Persistence
➢ Model Deployment as an API - General Overview
➢ Note on Upcoming Video
➢ Model API - Creating the Script
➢ Testing the API
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