Powerful insights that help you make smarter decisions. http://datamart.org Mon, 18 Dec 2017 06:10:02 +0000 en-US hourly 1 https://wordpress.org/?v=4.7.9 How to learn complex concepts in Machine Learning http://datamart.org/2017/12/18/learn-complex-concepts-machine-learning/ Mon, 18 Dec 2017 06:10:02 +0000 http://datamart.org/?p=7163

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 […]

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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 learning will become much faster and more pleasant. Those steps led myself to the success. After I finished my Sociology studies, I decided to develop further my interest in multidimensional data analysis, that is currently very useful, especially in topics such as Machine Learning or Computational (Artificial) Intelligence.
So, let’s start our adventure!
Read More
https://medium.com/@ewelinawoloszyn

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Using Jupyter notebooks and scikit-learn, you’ll predict whether a movie is likely to win an Oscar or be a box office hit. http://datamart.org/2017/12/02/using-jupyter-notebooks-scikit-learn-youll-predict-whether-movie-likely-win-oscar-box-office-hit/ Sat, 02 Dec 2017 21:11:20 +0000 http://datamart.org/?p=7154

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.

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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.

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PYCON UK 2017: Machine learning libraries you’d wish you’d known about http://datamart.org/2017/12/01/pycon-uk-2017-machine-learning-libraries-youd-wish-youd-known/ Sat, 02 Dec 2017 01:33:11 +0000 http://datamart.org/?p=7151

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

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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

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Transforming Categorical to Numerical: Encoding (Continuization) http://datamart.org/2017/12/01/transforming-categorical-numerical-encoding-continuization/ Sat, 02 Dec 2017 00:09:28 +0000 http://datamart.org/?p=7148

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.

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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.

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Using Gradient Boosting Machines in Python – by Albert Au Yeung http://datamart.org/2017/11/30/using-gradient-boosting-machines-python-albert-au-yeung/ Fri, 01 Dec 2017 01:08:24 +0000 http://datamart.org/?p=7144

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

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PyCon Hong Kong 2017 Talk

Using Gradient Boosting Machines in Python – by Albert Au Yeung

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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?” http://datamart.org/2017/05/13/michael-hochster-phd-statistics-stanford-director-research-pandora-data-scientist-answer-non-technical-people-ask-analysis-result-statistically-significa/ Sat, 13 May 2017 08:48:34 +0000 http://datamart.org/?p=7141

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).

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datascient12Answer 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).

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Tetiana Ivanova – How to become a Data Scientist in 6 months a hacker’s approach to career planning http://datamart.org/2017/03/02/tetiana-ivanova-become-data-scientist-6-months-hackers-approach-career-planning/ Thu, 02 Mar 2017 23:17:56 +0000 http://datamart.org/?p=7137

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.

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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.

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How to Become a Data Scientist in 2017? | Data Scientist Career | Data Science Future http://datamart.org/2017/03/02/become-data-scientist-2017-data-scientist-career-data-science-future/ Thu, 02 Mar 2017 23:08:48 +0000 http://datamart.org/?p=7133

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 research focused on numerical […]

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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 research focused on numerical weather and climate prediction.

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Select Features and Target in Scikit Learn http://datamart.org/2017/02/22/select-features-target-scikit-learn/ Wed, 22 Feb 2017 15:30:26 +0000 http://datamart.org/?p=7122

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 […]

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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’)
Pic 1 Data impoort in SKLearn

In order to select features and target for machine learning we will use the following commands

X=df[list(df.columns)[:-1]] input
y = df[‘Type’]
X is (Features)
Y is (Target)
By using above command X=df[list(df.columns)[:-1]] we removed the Type Column from input features and then used the y = df[‘Type’] as (Target).
If check X by running X.info() we will have the following columns

Int64Index: 214 entries, 0 to 213
Data columns (total 9 columns):
RI 214 non-null float64
Na 214 non-null float64
Mg 214 non-null float64
Al 214 non-null float64
Si 214 non-null float64
K 214 non-null float64
Ca 214 non-null float64
Ba 214 non-null float64
Fe 214 non-null float64
dtypes: float64(9)
memory usage: 16.7 KB

you will notice ‘TYPE’ column which is target variable is not shown below as we used the [:-1] which removed the last column which was target column. This is very useful command if we enter -2 then last 2 columns will be removed and so on.;

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Pre-Modeling: Data Preprocessing and Feature Exploration in Python http://datamart.org/2017/02/13/pre-modeling-data-preprocessing-feature-exploration-python/ Mon, 13 Feb 2017 14:58:35 +0000 http://datamart.org/?p=7112

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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datapreIn 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 April Chen presented explained dummy variables, feature building and reducing feature. AS well as elaborating how automated methods can have pros and cons like unable to explain how they work, It very insightful presentation and must for every doing machine learning. Watch the tutorial here

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