How to become a data scientist?
Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician. - Josh Wills Here is a road map to follow:
Credits:Swami Chandrasekaran

Product Placement
Peter Solarz
he wasn't even looking at me and he found me
d e v o n
dirt enthusiast

Origami Around

Kiana Khansmith

PR's Tumblrdome

tannertan36
Acquired Stardust
taylor price
cherry valley forever
Lint Roller? I Barely Know Her
I'd rather be in outer space 🛸

Not today Justin

Kaledo Art
Claire Keane
AnasAbdin
seen from United States

seen from United Kingdom

seen from Slovakia

seen from Switzerland

seen from India
seen from Germany
seen from United States
seen from Brazil
seen from Austria
seen from Honduras
seen from United Kingdom
seen from United States
seen from Netherlands

seen from United States

seen from United Kingdom

seen from Ireland
seen from China

seen from Malaysia
seen from India

seen from India
@sdmax99-blog
How to become a data scientist?
Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician. - Josh Wills Here is a road map to follow:
Credits:Swami Chandrasekaran

Anya is live and ready to show you everything. Watch her strip, dance, and perform exclusive shows just for you. Interact in real-time and make your fantasies come true.
Free to watch • No registration required • HD streaming
Perceptual Intelligence capabilities of AI at work
Link : http://how-old.net
Which is your favorite Machine Learning Algorithm?
Like with movies, I don’t have one favorite machine learning (ML) algorithm, but a few favorites, each for its own reason. Here are some of my top few algorithms and models:
Most elegant: The Perceptron algorithm. Developed back in the 50s by Rosenblatt and colleagues, this extremely simple algorithm can be viewed as the foundation for some of the most successful classifiers today, including suport vector machines and logistic regression, solved using stochastic gradient descent. The convergence proof for the Perceptron algorithm is one of the most elegant pieces of math I’ve seen in ML. Â
Most useful: Boosting, especially boosted decision trees. This intuitive approach allows you to build highly accurate ML models, by combining many simple ones. Boosting is one of the most practical methods in ML, it’s widely used in industry, can handle a wide variety of data types, and can be implemented at scale. I recommend checking out XGBoost for really scalable implementation of boosted trees. Boosting also lends itselft to very elegant proofs.
Biggest comeback: Convolutional neural network deep learning This type of neural network has been around since the early 80s. Although there was a decline in interest in them from the late nineties to late 2000s, they have seen an amazing comeback in the last 5 years. In particular, convolutional neural networks form the core of the deep learning models that have been having a huge impact, especially in computer vision and speech recognition.
Most beautiful algorithm: Dynamic programming (e.g., Viterbi, forward-backward, variable elimination & belief propagation algorithms). Dynamic programming is one of the most elegant algorithmic techniques in computer science, since it allows you to search through an exponentially-large space to find the optional solution. This idea has been applied in various ways in ML, especially for graphical models, such as hidden Markov models, Bayesian networks and Markov networks.
Unbeatable baseline: Nearest-neighbor algorithm. Often, when you are trying to write a paper, you want to show that “your cuve is better than my curve”. :) One way to do that is to introduce a baseline approach, and show that your method is more accurate. Well… nearest-neighbor is the simplest baseline to implement, so often folks will try first, thinking they’ll easily beat it and show their method is awesome. To their surprise, nearest-neighbor can be extremely hard to beat! In fact, if you have enough data, nearest neighbor is extremely powerful! And, this method is really useful in practice.
as answered by Carlos Guestrin, Amazon Professor of Machine Learning in Computer Science & CEO of Dato, Inc.
What are the top 10 data mining or machine learning algorithms?
Identifying the top 10 algorithms in the abstract is a pretty complicated exercise unless there is a clear dimension to make the comparison. Popularity? Usefulness? Research merit? Let me tackle this from a pretty subjective point of view: If I were interviewing you for a Data Mining position what would be the top 10 algorithms I would expect you to know in order of priority?
Linear regression
Logistic regression
k-means
SVMs
Random Forests
Matrix Factorization/SVD
Gradient Boosted Decision Trees/Machines
Naive Bayes
Artificial Neural Networks
For the last one I'd let you pick one of the following:
Bayesian Networks
Elastic Nets
Any other clustering algo besides k-means
LDA
Conditional Random Fields
HDPs or other Bayesian non-parametric model
Again, a pretty subjective list, but I think it is quite representative of what you need to do real data mining work in industry.
Source : www.quora.com