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D3 Basics
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Fundamentals of Data Visualization
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validation with scikit-learn
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evaluation with scikit-learn
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PCA with scikit-learn
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Feature Selection with scikit-learn
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Text Learning with scikit-learn
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Feature Scaling with scikit-learn
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K-Means with scikit-learn
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Outliers with scikit-learn
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Regression with scikit-learn
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Datasets and Question
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Random Forest with scikit-learn
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Decision Trees with scikit-learn
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Support Vector Machine with scikit-learn
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Wrangling with OpenStreetMap Data
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Naive Bayes
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Diamonds Analysis
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Exploratory Data Analysis on Facebook
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Explore Many Variables
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Exploring Two Variables
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Boxplot, histogram, visualization in R sysntax
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R Basics
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Intro to EDA
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Openstreetmap Data
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Analyzing Data
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Wrangling with Various Data Formats
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Intro
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Auditing the data
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Cleaning the data
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Scraping from Web
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XML Parsing
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JSON Wrangling
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CSV Wrangling
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Fundamentals
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Bayesian Learning
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Problem Set 2
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Bayesian Inference
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Joint Distribution
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Infinite Hypothesis Spaces
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Pac Learning
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Learning Theory
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First Kaggle Competition
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Boosting, Post-decessor
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Support Vector Machines
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Boosting
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Boosting, Pre-decesor
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kNN
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Instance Based Learning and Others
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Tools for Neural Networks & Others
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Neural Networks & Perceptron
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Polynomial Regression
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Regression
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ID3
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Decision Trees
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Classification vs Regression
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Definition
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Introduction to pandas and Numpy
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Final Project
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Joshua intro and advice, addition, Recap and Conclusion
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More Map Reduce
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Counting Words
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Intro Map Reduce
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Advice, Recap & Conclusion
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Visualizing Time Series Data
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Data Types and Scales
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Visual Encodings
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Introducing Don and Rishiraj, advice on Communicate Findings
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Collaborative filtering algorithm
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Collaborative filtering
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Intro to Data Visualization
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Conclusion, other advice, assignment and Recap
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Coeffecient Determination
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Linear Regression, Gradient Descent, Cost Function
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Machine Learning
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Non-parametric test
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t-test
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Introduction and Statistic(Rigor)
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Conclusion and Project for Data Wrangling
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How to handle missing data
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Sanity Check for Missing Values
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APIs
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Queries
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Database Schema
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Summary
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Ceiling Analysis: What Part of the Pipeline to Work on Next
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Getting Lots of Data and Artificial Data Synthesis
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Sliding Windows
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Problem Description and Pipeline
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Map-reduce and data-parallelism
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Online Learning
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Stochastic Gradient Descent Convergence
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Mini Batch Gradient Descent
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Aadhaar Data and Relational Databases
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CSV
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Data Formats
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Data Wrangling, Analyze Messy Data and Nick's Experiences
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Introduction of Data Wrangling
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Advice for Data Scientist and Recap
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Project Intro for Titanic
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Pandas and Dataframes
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Note from Intro to Data Science
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Normal Equation
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Features And Polynomial Regression
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Gradient Descent
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Multiple Variables
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Neat Tricks
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Cost Function
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Model Representation
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Introduction
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Stochastic Gradient Descent
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Learning With Large Datasets
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Server Computers (AD Ex.)
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Implementation detail: Mean Normalization
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Vectorization: Low Rank Matrix Factorization
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Content-based Recommendation
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Problem Formulation
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Choosing what features to use
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Anomaly Detection vs Supervised Learning
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Developing and evaluating an anomaly detection system
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Algorithm
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Gaussian Distribution
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Problem Motivation
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Advice for Applying PCA
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Reconstruction from compressed representation
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Choosing the Number of Principal Components
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Principal Component Analysis Algorithm
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Principal Component Analysis problem formulation
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Motivation II : Data Visualization
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Motivation I : Data Compression
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K-means algorithm
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Unsupervised Learning: Introduction
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Using SVM
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Kernels II
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Kernels I
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Large Margin Intuition
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Optimization Objective
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Data for Machine Learning
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Trading of Precision & Recall
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Error metrics for Skewed Classes
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Error Analysis
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Prioritizing What to Work On
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Deciding What to Do Next (Revisited)
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Learning Curves
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Regularization and Bias/Variance
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Diagnosing bias vs. variance
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Model selection and training/validation/test sets
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Evaluating a hyphotesis
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Deciding what to Try Next
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Autonomous Driving (Examples)
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Putting it together
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Random Initialization
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Gradient Checking
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Implementation note: Unrolling parameters
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Backpropagation Intuition
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Backpropagation Algorithm
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Multi-class Classification
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Examples & Intuition ll
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Examples & Intuition l
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Model Representation ll
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Model Representation l
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Neurons & the brain
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Non-linear hypothesis
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Regularized Logistic Regression
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Regularized Linear Regression
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The problem of overfitting
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multiclass classification
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Advanced Optimization
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Simplified cost function and gradient descent
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Decision Boundary
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Classification
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hypothesis representation