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Nov 25, 2017

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Jun 7, 2017

Priya commented on Takashi J. OZAKI's blog post 12 Statistical and Machine Learning Methods that Every Data Scientist Should Know

"Please suggest a link for budding data scientists"

Mar 8, 2017

Eljay Canoy commented on Takashi J. OZAKI's blog post In Japan, "Artificial Intelligence" comes to be a super star while "Data Scientist" is fading away

"I added "Deep Learning" and "Machine Learning" in the mix and found that Deep Learning has gained most popularity, followed by Machine Learning and then Artificial Intelligence. It's also interesting to see people's…"

Jan 30, 2017

Takashi J. OZAKI posted a blog post### In Japan, "Artificial Intelligence" comes to be a super star while "Data Scientist" is fading away

I published a post about the current status of "Data Scientist" in Japan, as a periodic follow-up analysis since two years ago. Its trend still remains, but it's beyond my anticipation at that time.Indeed growing trend of "Artificial Intelligence" in Japan is steeper than that in English, and "Data Scientist" is now getting to be forgotten by people, although in the global market data…See More

Jan 27, 2017

Takashi J. OZAKI's blog post was featured### 12 Statistical and Machine Learning Methods that Every Data Scientist Should Know

Below is my personal list of statistical and machine learning methods that every data scientist should know in 2016.Statistical Hypothesis Testing (t-test, chi-squared test & ANOVA)Multiple Regression (Linear Models)General Linear Models (GLM: Logistic Regression, Poisson Regression)Random ForestXgboost (eXtreme Gradient Boosted Trees)Deep LearningBayesian Modeling with MCMCword2vecK-means ClusteringGraph Theory & Network Analysis(A1) Latent Dirichlet Allocation & Topic Modeling(A2)…See More

Jan 14, 2017

Takashi J. OZAKI posted a blog post### 12 Statistical and Machine Learning Methods that Every Data Scientist Should Know

Below is my personal list of statistical and machine learning methods that every data scientist should know in 2016.Statistical Hypothesis Testing (t-test, chi-squared test & ANOVA)Multiple Regression (Linear Models)General Linear Models (GLM: Logistic Regression, Poisson Regression)Random ForestXgboost (eXtreme Gradient Boosted Trees)Deep LearningBayesian Modeling with MCMCword2vecK-means ClusteringGraph Theory & Network Analysis(A1) Latent Dirichlet Allocation & Topic Modeling(A2)…See More

Apr 20, 2016

Takashi J. OZAKI's blog post was featured### Overview and simple trial of Convolutional Neural Network with MXnet

Actually I've known about MXnet for weeks as one of the most popular library / packages in Kaggler, but just recently I heard bug fix has been almost done and some friends say the latest version looks stable, so at last I installed it.MXnet: https://github.com/dmlc/mxnetI think that the most important feature of MXnet is its implementation of…See More

Mar 30, 2016

Takashi J. OZAKI's blog post was featured### Multivariate modeling vs. univariate modeling along human intuition: predicting taste of wine

I wrote a blog post inspired by Jamie Goode's book "Wine Science: The Application of Science in Winemaking".In this book, Goode argued that reductionistic approach cannot explain relationship between chemical ingredients and taste of wine. Indeed, we know not all high (alcohol) wines are excellent, although in general high wines are believed to be good. Usually taste of wine is affected by a complicated balance of many components such as sweetness, acid, tannin, density or others that are given…See More

Nov 28, 2015

Takashi J. OZAKI's blog post was featured### R and Stan: introduction to Bayesian modeling

I wrote a series of blog posts on Bayesian modeling with R and Stan.Bayesian modeling with R and Stan (1): OverviewBayesian modeling with R and Stan (2): Installation and an easy exampleBayesian modeling…See More

Aug 18, 2015

Takashi J. OZAKI's blog post was featured### Even without any "golden feature", multivariate modeling can work

A/B testing is widely used for online marketing, management of Internet ads or any other usual analytics. In general, people use it in order to look for "golden features (metrics)" that are vital points for growth hacking. To validate A/B testing, statistical hypothesis tests such as t-test are used and people are trying to find any metric with a significant effect across conditions. If you successfully find a metric with a significant difference between design A and B of a click button, you'll…See More

Jun 20, 2015

Takashi J. OZAKI posted a blog post### Overfitting or generalized? Comparison of ML classifiers - a series of articles

In my own blog I wrote a series of articles about how major machine learning classifiers work, with some visualization of their decision boundaries on various datasets.Machine learning for package users with R (0): PrologueMachine learning for package users with R (1): Decision Tree…See More

Jun 5, 2015

Takashi J. OZAKI's blog post was featured### Overfitting or generalized? Comparison of ML classifiers - a series of articles

In my own blog I wrote a series of articles about how major machine learning classifiers work, with some visualization of their decision boundaries on various datasets.Machine learning for package users with R (0): PrologueMachine learning for package users with R (1): Decision Tree…See More

Jun 5, 2015

Takashi J. OZAKI's blog post was featured### Decision tree vs. linearly separable or non-separable pattern

As a part of a series of posts discussing how a machine learning classifier works, I ran decision tree to classify a XY-plane, trained with XOR patterns or linearly separable patterns.1. Simple (non-overlapped) XOR patternIt worked well. Its decision boundary was drawn almost perfectly parallel to the assumed true boundary, i.e. XY axes.2. Complex (overlapped) XOR pattern without…See More

Mar 25, 2015

Takashi J. OZAKI posted a blog post### Decision tree vs. linearly separable or non-separable pattern

As a part of a series of posts discussing how a machine learning classifier works, I ran decision tree to classify a XY-plane, trained with XOR patterns or linearly separable patterns.1. Simple (non-overlapped) XOR patternIt worked well. Its decision boundary was drawn almost perfectly parallel to the assumed true boundary, i.e. XY axes.2. Complex (overlapped) XOR pattern without…See More

Mar 23, 2015

- Short Bio:
- Ph. D. Data Scientist

*Expertise*

Data Science: mainly for digital marketing

Statistics: Multivariate modeling, including Bayesian modeling

Machine Learning: including Deep Learning

*Proficiency*

Programming language: R, Stan, Python, SQL, C, Matlab

Natural language: Japanese, English (business level)

*CV*

Jan 2016 - present: Data Scientist, Google

Jul 2013 - Dec 2015: Data Scientist, Recruit Communications Co., Ltd.

Jun 2012 - Jun 2013: Data Scientist, CyberAgent, Inc.

Apr 2006 - May 2012: Academic Researcher in Cognitive Neuroscience, in some universities or national research institutes

Mar 2006: Ph. D. in Frontier Sciences, The University of Tokyo

- My Website or LinkedIn Profile (URL):
- http://tjo-en.hatenablog.com/

- Field of Expertise:
- Business Analytics, Predictive Modeling, Data Mining, Econometrics, Web Analytics, Statistical Consulting, Artificial Intelligence

- Years of Experience in Analytical Role:
- 4 yr

- Professional Status:
- Technical

- Interests:
- Networking

- What is your Favorite Data Mining or Analytical Website?
- http://www.kdnuggets.com/

- Your Company:

- Industry:
- Technology

- Your Job Title:
- Data Scientist

- How did you find out about AnalyticBridge?
- I saw an article by Dr. Vincent Granville via Google search.

Posted on January 13, 2017 at 6:30am 1 Comment 0 Likes

I published a post about the current status of "Data Scientist" in Japan, as a periodic follow-up analysis since two years ago. Its trend still remains, but it's beyond my anticipation at that time.

Indeed growing trend of "Artificial Intelligence" in Japan is steeper than that in English, and "Data Scientist" is now getting to be…

ContinuePosted on January 8, 2017 at 6:30am 1 Comment 2 Likes

Below is my personal list of statistical and machine learning methods that every data scientist should know in 2016.

**Statistical Hypothesis Testing (t-test, chi-squared test & ANOVA)****Multiple Regression (Linear Models)****General Linear Models (GLM: Logistic Regression, Poisson Regression)****Random Forest****Xgboost (eXtreme Gradient Boosted Trees)****Deep Learning****Bayesian Modeling with…**

Posted on March 30, 2016 at 8:30am 0 Comments 0 Likes

Actually I've known about MXnet for weeks as one of the most popular library / packages in Kaggler, but just recently I heard bug fix has been almost done and some friends say the latest version looks stable, so at last I installed it.

MXnet: https://github.com/dmlc/mxnet

I think that the most important feature…

ContinuePosted on November 26, 2015 at 8:43am 0 Comments 0 Likes

I wrote a blog post inspired by Jamie Goode's book "Wine Science: The Application of Science in Winemaking".

In this book, Goode argued that reductionistic approach cannot explain relationship between chemical ingredients and taste of wine. Indeed, we know not all high (alcohol) wines are excellent, although in general high wines are believed to be good. Usually taste of wine is affected by a complicated balance of many components such as sweetness, acid, tannin,…

Continue- At 8:16am on May 16, 2013, Takashi J. OZAKI said…
Hi Paul (id: paulagyiri),

I'd be happy if you contact me via LinkedIn (http://jp.linkedin.com/in/tjozaki).

Thanks,

-TJO

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