Posts by harijh2

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2018-04-05 00:00 Aadhaar Data and Relational Databases
2018-04-05 00:00 Advice for Applying PCA
2018-04-05 00:00 Advice for Data Scientist and Recap
2018-04-05 00:00 Advice, Recap & Conclusion
2018-04-05 00:00 Algorithm
2018-04-05 00:00 Analyzing Data
2018-04-05 00:00 Anomaly Detection vs Supervised Learning
2018-04-05 00:00 APIs
2018-04-05 00:00 Auditing the data
2018-04-05 00:00 Autonomous Driving (Examples)
2018-04-05 00:00 Backpropagation Algorithm
2018-04-05 00:00 Backpropagation Intuition
2018-04-05 00:00 Bayesian Inference
2018-04-05 00:00 Boosting
2018-04-05 00:00 Boosting, Post-decessor
2018-04-05 00:00 Boosting, Pre-decesor
2018-04-05 00:00 Boxplot, histogram, visualization in R sysntax
2018-04-05 00:00 Ceiling Analysis: What Part of the Pipeline to Work on Next
2018-04-05 00:00 Choosing the Number of Principal Components
2018-04-05 00:00 Choosing what features to use
2018-04-05 00:00 Classification vs Regression
2018-04-05 00:00 Cleaning the data
2018-04-05 00:00 Coeffecient Determination
2018-04-05 00:00 Conclusion and Project for Data Wrangling
2018-04-05 00:00 Conclusion, other advice, assignment and Recap
2018-04-05 00:00 Content-based Recommendation
2018-04-05 00:00 Cost Function
2018-04-05 00:00 Counting Words
2018-04-05 00:00 CSV
2018-04-05 00:00 CSV Wrangling
2018-04-05 00:00 Data for Machine Learning
2018-04-05 00:00 Data Formats
2018-04-05 00:00 Data Types and Scales
2018-04-05 00:00 Data Wrangling, Analyze Messy Data and Nick's Experiences
2018-04-05 00:00 Database Schema
2018-04-05 00:00 Decision Trees
2018-04-05 00:00 Definition
2018-04-05 00:00 Developing and evaluating an anomaly detection system
2018-04-05 00:00 Error Analysis
2018-04-05 00:00 Error metrics for Skewed Classes
2018-04-05 00:00 Features And Polynomial Regression
2018-04-05 00:00 Final Project
2018-04-05 00:00 Fundamentals
2018-04-05 00:00 Gaussian Distribution
2018-04-05 00:00 Getting Lots of Data and Artificial Data Synthesis
2018-04-05 00:00 Gradient Checking
2018-04-05 00:00 Gradient Descent
2018-04-05 00:00 How to handle missing data
2018-04-05 00:00 ID3
2018-04-05 00:00 Implementation detail: Mean Normalization
2018-04-05 00:00 Implementation note: Unrolling parameters
2018-04-05 00:00 Infinite Hypothesis Spaces
2018-04-05 00:00 Instance Based Learning and Others
2018-04-05 00:00 Intro
2018-04-05 00:00 Intro Map Reduce
2018-04-05 00:00 Intro to Data Visualization
2018-04-05 00:00 Intro to EDA
2018-04-05 00:00 Introducing Don and Rishiraj, advice on Communicate Findings
2018-04-05 00:00 Introduction and Statistic(Rigor)
2018-04-05 00:00 Introduction of Data Wrangling
2018-04-05 00:00 Introduction to pandas and Numpy
2018-04-05 00:00 Joint Distribution
2018-04-05 00:00 Joshua intro and advice, addition, Recap and Conclusion
2018-04-05 00:00 JSON Wrangling
2018-04-05 00:00 K-means algorithm
2018-04-05 00:00 Kernels I
2018-04-05 00:00 Kernels II
2018-04-05 00:00 kNN
2018-04-05 00:00 Large Margin Intuition
2018-04-05 00:00 Learning Theory
2018-04-05 00:00 Learning With Large Datasets
2018-04-05 00:00 Linear Regression, Gradient Descent, Cost Function
2018-04-05 00:00 Machine Learning
2018-04-05 00:00 Map-reduce and data-parallelism
2018-04-05 00:00 Mini Batch Gradient Descent
2018-04-05 00:00 Model Representation
2018-04-05 00:00 More Map Reduce
2018-04-05 00:00 Motivation I : Data Compression
2018-04-05 00:00 Motivation II : Data Visualization
2018-04-05 00:00 Multiple Variables
2018-04-05 00:00 Neat Tricks
2018-04-05 00:00 Neural Networks & Perceptron
2018-04-05 00:00 Non-parametric test
2018-04-05 00:00 Normal Equation
2018-04-05 00:00 Online Learning
2018-04-05 00:00 Openstreetmap Data
2018-04-05 00:00 Optimization Objective
2018-04-05 00:00 Pac Learning
2018-04-05 00:00 Pandas and Dataframes
2018-04-05 00:00 Polynomial Regression
2018-04-05 00:00 Principal Component Analysis Algorithm
2018-04-05 00:00 Principal Component Analysis problem formulation
2018-04-05 00:00 Prioritizing What to Work On
2018-04-05 00:00 Problem Description and Pipeline
2018-04-05 00:00 Problem Formulation
2018-04-05 00:00 Problem Motivation
2018-04-05 00:00 Problem Set 2
2018-04-05 00:00 Project Intro for Titanic
2018-04-05 00:00 Putting it together
2018-04-05 00:00 Queries
2018-04-05 00:00 R Basics
2018-04-05 00:00 Random Initialization
2018-04-05 00:00 Reconstruction from compressed representation
2018-04-05 00:00 Regression
2018-04-05 00:00 Sanity Check for Missing Values
2018-04-05 00:00 Scraping from Web
2018-04-05 00:00 Server Computers (AD Ex.)
2018-04-05 00:00 Sliding Windows
2018-04-05 00:00 Stochastic Gradient Descent
2018-04-05 00:00 Stochastic Gradient Descent Convergence
2018-04-05 00:00 Summary
2018-04-05 00:00 Support Vector Machines
2018-04-05 00:00 t-test
2018-04-05 00:00 Tools for Neural Networks & Others
2018-04-05 00:00 Trading of Precision & Recall
2018-04-05 00:00 Unsupervised Learning: Introduction
2018-04-05 00:00 Using SVM
2018-04-05 00:00 Vectorization: Low Rank Matrix Factorization
2018-04-05 00:00 Visual Encodings
2018-04-05 00:00 Visualizing Time Series Data
2018-04-05 00:00 XML Parsing