Sunday January 21st 2018

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‘Machine Learning’ Archives

How to learn complex concepts in Machine Learning

How to learn complex concepts in Machine Learning

In today’s ocean of information about Machine Learning and Artificial Intelligence, it is easy to feel lost, and to label those fields as impossible to learn. That’s why I decided to share my personal experience and guide you with some simple techniques that can boost your creativity and effectiveness. After applying them, your process of [...]

Using Jupyter notebooks and scikit-learn, you’ll predict whether a movie is likely to win an Oscar or be a box office hit.

Using Jupyter notebooks and scikit-learn, you’ll predict whether a movie is likely to win an Oscar or be a box office hit.

This talk is for engineers, data scientists, and movie lovers who want to learn how to scrape information from the Internet, and then use python libraries (and some domain knowledge) to answer interesting questions using that data.

PYCON UK 2017: Machine learning libraries you’d wish you’d known about

PYCON UK 2017: Machine learning libraries you’d wish you’d known about

Diagnosing, explaining and scaling machine learning is hard. I'll talk about a set of libraries that have helped me to understand when and how a model is failing, helped me communicate why it is working to non-technical users, automated the search for better models and helped me to scale my modeling. Ian Ozsvald

Transforming Categorical to Numerical: Encoding (Continuization)

Transforming Categorical to Numerical: Encoding (Continuization)

How to Transform Categorical values to Numerical Great explanation if we have categorical variables with many values binary encoding will tremendously increase the dimension of the data.

Using Gradient Boosting Machines in Python – by Albert Au Yeung

Using Gradient Boosting Machines in Python – by Albert Au Yeung

PyCon Hong Kong 2017 Talk Using Gradient Boosting Machines in Python - by Albert Au Yeung

Michael Hochster, PhD in Statistics, Stanford; Director of Research, Pandora – As a data scientist, how do you answer when non-technical people ask you “is your analysis result statistically significant?”

Michael Hochster, PhD in Statistics, Stanford; Director of Research, Pandora – As a data scientist, how do you answer when non-technical people ask you “is your analysis result statistically significant?”

Answer They are asking whether you have enough data to trust your results. I would try to answer the real question and not worry too much about whether the technical jargon is being used correctly (that’s my job, not theirs).

Tetiana Ivanova – How to become a Data Scientist in 6 months a hacker’s approach to career planning

Tetiana Ivanova – How to become a Data Scientist in 6 months a hacker’s approach to career planning

You don’t need a PhD or even a masters to do machine learning. On taking calculated risks and especially calculated exits from one’s comfort zone. Some notes on soul searching and how to choose a career that is also a passion. Reading list.

How to Become a Data Scientist in 2017? | Data Scientist Career | Data Science Future

How to Become a Data Scientist in 2017? | Data Scientist Career | Data Science Future

Jesse Steinweg-Woods is soon-to-be a Senior Data Scientist at tronc, working on recommender systems for articles and understanding customer behavior. Previously, he worked at Argo Group Insurance on new pricing models that took advantage of machine learning techniques. He received his PhD in Atmospheric Science from Texas A&M University, and his [...]

Select Features and Target in Scikit Learn

Select Features and Target in Scikit Learn

To do Machine Learning in SKlearn, as a first step we need to import following import pandas as pd import numpy as np Step 1. We read the file in Panadas Dataframe by pd.read_csv. In jupyter Note book we defined dataframe as df=pd.read_csv('C:\Data\glass.csv') In order to select features and target for machine learning we will use [...]

Pre-Modeling: Data Preprocessing and Feature Exploration in Python

Pre-Modeling: Data Preprocessing and Feature Exploration in Python

In my recent seach on building dummy variables for a loan dataset which I downloaded from kaggle I came across this tutorial on Data preprocessing and feature exploration - step critical building machine learning models models. Though there are still more information I am searching on creating dummy variable, however I like the way presenter [...]

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