What does it mean to be a data scientist? After all, there are many different skills that fall under the umbrella of data science. The professionals’ job role was logically related to their proficiency in different skills.
link: https://www.business2community.com/big-data/investigating-data-scientists-their-skills-and-team-makeup-01335085

via: @cedeeplearning

🔹Design your Neural Networks
What’s a good learning rate? How many hidden layers should your network have? Is dropout actually useful? Why are your gradients vanishing?
link: https://towardsdatascience.com/designing-your-neural-networks-a5e4617027ed

#neuralnetwork
#machinelearning

via: @cedeeplearning

Tinker With a Neural Network Right Here in Your Browser

This was created by Daniel Smilkov and Shan Carter. This is a continuation of many people’s previous work — most notably Andrej Karpathy’s #convnet.js demo and Chris Olah’s articles about neural networks.

Via: @cedeeplearning

https://playground.tensorflow.org/
#visualization #neural_networks

The advent of AI in #Architecture is still in its early days but offers promising results. More than a mere opportunity, such potential represents for us a major step ahead, about to reshape the architectural discipline.

Via: @cedeeplearning

Check this interesting post out:
https://towardsdatascience.com/ai-architecture-f9d78c6958e0

🔹Deconstructing Data Science: Breaking The Complex Craft Into It’s Simplest Parts

link: https://medium.com/the-mission/deconstructing-data-science-breaking-the-complex-craft-into-its-simplest-parts-15b15420df21
#Datascience
#datastructure

via: @cedeeplearning

🔹Here are 50 Companies Leading the #AI Revolution

link: https://fortune.com/2017/02/23/artificial-intelligence-companies/?xid=soc_socialflow_facebook_FORTUNE

via: @cedeeplearning

Here are 10 #courses to help with your spring learning season. Courses range from introductory #machinelearning to #deeplearning to natural language processing and beyond.

This collection comes courtesy of Columbia University, Krakow Technical University, MIT, UC Berkeley, University of Washington, University of Wisconsin–Madison, and Yandex Data School.

1️⃣ Machine Learning
🏛 (University of Washington)
This course is designed to provide a thorough grounding in the fundamental methodologies and algorithms of machine learning.

2️⃣ Machine Learning
🏛 (University of Wisconsin-Madison)
This course will cover the key concepts of machine learning, including classification, regression analysis, clustering, and dimensionality reduction.

3️⃣ Algorithms (in journalism)
🏛 (Columbia University )
This is a course on algorithmic data analysis in journalism, and also the journalistic analysis of algorithms used in society. The major topics are text processing, visualization of high dimensional data, regression, machine learning, algorithmic bias and accountability, monte carlo simulation, and election prediction.

4️⃣ Practical Deep Learning
🏛 (Yandex Data School)
Yandex Data School

5️⃣ Big Data in 30 Hours
🏛 (Krakow Technical University )
The goal of this technical, hands-on class is to introduce practical Data Engineering and Data Science to technical personnel (corporate, academic or students), during 15 lectures (2 hours each)

6️⃣ Deep Reinforcement Learning Bootcamp
🏛 (UC Berkeley(& others))
Reinforcement learning considers the problem of learning to act and is poised to power next generation AI systems, which will need to go beyond input-output pattern recognition (as has sufficed for speech, vision, machine translation) but will have to generate intelligent behavior

7️⃣ Introduction to Artificial intelligence
🏛 (University of Washington)


8️⃣ Brains, Minds and Machines Summer Course(MIT)
🏛 (MIT)
This course explores the problem of intelligence—its nature, how it is produced by the brain and how it could be replicated in machines—using an approach that integrates cognitive science, which studies the mind; neuroscience, which studies the brain; and computer science and artificial intelligence, which study the computations needed to develop intelligent machines

9️⃣ Design and Analysis of Algorithms
🏛 (MIT)
This is an intermediate algorithms course with an emphasis on teaching techniques for the design and analysis of efficient algorithms, emphasizing methods of application

🔟 Natural Language Processing
🏛 (University of Washington)
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also check our other social media handles:
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#MachineLearning #DataScience #Course #DeepLearning #BigData #AI

🔹Laying the Groundwork for the Production of Your Machine Learning Models

link: https://www.rocketsource.co/blog/machine-learning-models/

#machinelearning
#modle
#hierarcy

via: @cedeeplearning

#Predictive vs #Prescriptive #analytics

Via: @cedeeplearning

✅10 must-read books for ml and data science

1️⃣ Python Data Science Handbook
By Jake VanderPlas

The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages. Familiarity with Python as a language is assumed.

2️⃣ Neural Networks and Deep Learning
By Michael Nielsen

Neural Networks and Deep Learning is a free online book. The book will teach you about: Neural networks, Deep learning

3️⃣ Think Bayes
By Allen B. Downey

Think Bayes is an introduction to Bayesian statistics using computational methods.

4️⃣ Machine Learning & Big Data
By Kareem Alkaseer

The purpose behind it is to have a balance between theory and implementation for the software engineer to implement machine learning models comfortably without relying too much on libraries.

5️⃣ Statistical Learning with Sparsity: The Lasso and Generalizations
By Trevor Hastie, Robert Tibshirani, Martin Wainwright

This book descibes the important ideas in these areas in a common conceptual framework.


6️⃣ Statistical inference for data science
By Brian Caffo

This book is written as a companion book to the Statistical Inference Coursera class as part of the Data Science Specialization.


7️⃣ Convex Optimization
By Stephen Boyd and Lieven Vandenberghe

This book is about convex optimization, a special class of mathematical optimization problems, which includes least-squares and linear programming problems.

8️⃣ Natural Language Processing with Python
By Steven Bird, Ewan Klein, and Edward Loper

This is a book about Natural Language Processing. The book is based on the Python programming language together with an open source library called the Natural Language Toolkit (NLTK).

9️⃣ Automate the Boring Stuff with Python
By Al Sweigart

In Automate the Boring Stuff with Python, you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand-no prior programming experience required.

🔟 Social Media Mining: An Introduction
By Reza Zafarani, Mohammad Ali Abbasi and Huan Liu

Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining.
——————————————

Via: @cedeeplearning
Credit goes to: https://www.kdnuggets.com/author/matt-mayo
also check our other social media handles:
https://linktr.ee/cedeeplearning

#MachineLearning #DataScience #Course #DeepLearning #BigData #AI

What does a #DataScientist need to consider on a machine learning project?

Via: @cedeeplearning
Credit goes to: http://blog.bidmotion.com
also check our other social media handles:
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🔹Coding Deep Learning For Beginners

https://towardsdatascience.com/coding-deep-learning-for-beginners-types-of-machine-learning-b9e651e1ed9d

via: @cedeeplearning

Example of deep learning #algorithms for #clustering

link: https://www.datasciencecentral.com/profiles/blogs/deep-learning-definition-resources-comparison-with-machine-learni?overrideMobileRedirect=1

Via: @cedeeplearning

#Supervised ML VS #Unsupervised ML

In Supervised learning, you #train the machine using data which is well #"labeled." Unsupervised learning is a machine learning technique, where you do not need to supervise the model. Supervised learning allows you to collect data or produce a data output from the previous experience.

via: @cedeeplearning

🔹R vs Python: Which One is Better for Data Science?

link: https://statanalytica.com/blog/r-vs-python/
#R
#Python
#Data_science

via: @cedeeplearning

🔹Machine Learning tips and tricks #cheatsheet
Bias: The #bias of a model is the difference between the expected #prediction and the correct model that we try to predict for given data points.

Variance: The #variance of a model is the variability of the model prediction for given data points.

Bias/variance #tradeoff: The simpler the model, the higher the bias, and the more complex the model, the higher the variance.

from: stanford.edu
via: @cedeeplearning