❇️ مجموعه 10 کورس رایگان در حوزه دیتاساینس و یادگیری ماشین

1️⃣ Machine Learning
(University of Washington)
2️⃣ Machine Learning
(University of Wisconsin-Madison)
3️⃣ Algorithms (in journalism)
(Columbia University )
4️⃣ Practical Deep Learning
(Yandex Data School)
5️⃣ Big Data in 30 Hours
(Krakow Technical University )
6️⃣ Deep Reinforcement Learning Bootcamp
(UC Berkeley(& others))
7️⃣ Introduction to Artificial intelligence
(University of Washington)
8️⃣ Brains, Minds and Machines Summer Course
(MIT)
9️⃣ Design and Analysis of Algorithms
(MIT)
🔟 Natural Language Processing
(University of Washington)
لینک:
https://goo.gl/Riybxs

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


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@machinelearning_tuts
@drivelesscar
@autonomousvehicle

Machine Learning Guide: 20 Free ODSC Resources to Learn Machine Learning: https://lnkd.in/ejqejpA

#BigData #DataScience #DataScientists #AI #DeepLearning


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@machinelearning_tuts

❇️ مجموعه 10 کورس رایگان در حوزه دیتاساینس و یادگیری ماشین

1️⃣ Machine Learning
(University of Washington)
2️⃣ Machine Learning
(University of Wisconsin-Madison)
3️⃣ Algorithms (in journalism)
(Columbia University )
4️⃣ Practical Deep Learning
(Yandex Data School)
5️⃣ Big Data in 30 Hours
(Krakow Technical University )
6️⃣ Deep Reinforcement Learning Bootcamp
(UC Berkeley(& others))
7️⃣ Introduction to Artificial intelligence
(University of Washington)
8️⃣ Brains, Minds and Machines Summer Course
(MIT)
9️⃣ Design and Analysis of Algorithms
(MIT)
🔟 Natural Language Processing
(University of Washington)
لینک:
https://goo.gl/Riybxs

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


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@machinelearning_tuts
@drivelesscar
@autonomousvehicle

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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Via: @cedeeplearning
Credit goes to: https://goo.gl/Riybxs
also check our other social media handles:
https://linktr.ee/cedeeplearning

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

✅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.
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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

🔹Olympics Win Gold Medal For Big Data

Nearly 60 GB of information per second is expected to travel across British Telecom’s networks during the Olympic Games. Unsurprisingly social media has generated reams of content during the games. The estimated 845 million monthly active Facebook users are expected to be responsible for more than 15 terabytes of data each day, while Twitter is expecting over 13,000 tweets per second.

link: https://www.forbes.com/sites/netapp/2012/08/08/olympic-charter-big-data-airplane/#17ce8a38180f

📌Via: @cedeeplearning

📌Other Social Media: https://linktr.ee/cedeeplearning
#bigdata
#machinelearning
#datascience

🔻Some points to visualizing your data

📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://policyviz.com/2018/08/07/dataviz-cheatsheet/

#visualization
#bigdata
#machinelearning
#datascience

🔻The 8 Data Science Skills

1. Programming Skills
2. Statistics
3. Machine Learning
4. Multivariable Calculus & Linear Algebra
5. Data Wrangling
6. Data Visualization & Communication
7. Software Engineering
8. Data Intuition

📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://blog.udacity.com/2014/11/data-science-job-skills.html

#datascience
#bigdata
#skill
#machineleraning

🔻BEST DATA PREPARATION TOOLS TO LOOK OUT FOR IN 2020

Businesses need to map data from different sources in order to get better insights. This process of mapping data is what we call data preparation. Therefore, we have brought you the top 10 Data Preparation tools to look out for in 2020:

1. Altair Monarch
2. Microsoft Power BI
3. Alteryx
4. Tableau Prep
5. Paxata
6. Trifaca
7. TMMData
8. TIBCO Software
9. SAP
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📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning

link: https://www.analyticsinsight.net/best-data-preparation-tools-to-look-out-for-in-2020/

#datatools
#datascience
#powerbi
#preparation
#insight
#bigdata

🔹⚙️ Everything about NVIDIA Deep Learning

Nvidia Deep Learning AI lets users pull insights from big data. This lets them realize their true value by utilizing them in creating solutions for current and forecasted problems. This allows them to arm themselves with the knowledge that can prove to be instrumental at a time when a challenge arises.

1. What is Nvidia Deep Learning AI?
2. Nvidia Deep Learning AI benefits
3. Overview of Nvidia Deep Learning AI features
4. Nvidia Deep Learning AI pricing
5. User satisfaction
6. Video
7. Technical details
8. Support details
9. User reviews
———————
📌Via: @cedeeplearning

https://reviews.financesonline.com/p/nvidia-deep-learning-ai/

#deeplearning #NVIDIA
#machinelearning
#bigdata #analytics
#neuralnetworks