❇️ 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
----------
@machinelearning_tuts
@drivelesscar
@autonomousvehicle
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
----------
@machinelearning_tuts
@drivelesscar
@autonomousvehicle
#course #video #ml
This series is all about neural network programming and PyTorch! We will learn how to build neural networks with PyTorch, and we’ll find that we are super close to programming neural networks from scratch, as the experience of using PyTorch is as close as it gets to the real thing! After programming neural networks with PyTorch, it’s pretty easy to see how the process works from scratch. This will lead us to a much deeper understanding of neural networks and deep learning.
@machinelearning_tuts
https://www.youtube.com/playlist?list=PLZbbT5o_s2xrfNyHZsM6ufI0iZENK9xgG
This series is all about neural network programming and PyTorch! We will learn how to build neural networks with PyTorch, and we’ll find that we are super close to programming neural networks from scratch, as the experience of using PyTorch is as close as it gets to the real thing! After programming neural networks with PyTorch, it’s pretty easy to see how the process works from scratch. This will lead us to a much deeper understanding of neural networks and deep learning.
@machinelearning_tuts
https://www.youtube.com/playlist?list=PLZbbT5o_s2xrfNyHZsM6ufI0iZENK9xgG
YouTube
PyTorch - Python Deep Learning Neural Network API
This series is all about neural network programming and PyTorch! We'll start out with the basics of PyTorch and CUDA and understand why neural networks use G...
Introduction to Machine Learning for Coders!
#ml
#course
#jeremy_howard
#video
New machine learning course by Jeremy Howard.
These videos was made in San Francisco University.
Headlines:
1—Introduction to Random Forests
2—Random Forest Deep Dive
3—Performance, Validation and Model Interpretation
4—Feature Importance, Tree Interpreter
5—Extrapolation and RF from Scratch
6—Data Products and Live Coding
7—RF from Scratch and Gradient Descent
8—Gradient Descent and Logistic Regression
9—Regularization, Learning Rates and NLP
10— More NLP and Columnar Data
11—Embeddings
12— Complete Rossmann, Ethical Issues
@machinelearning_tuts
Course URL:
http://course.fast.ai/ml
Read more:
http://www.fast.ai/2018/09/26/ml-launch/
#ml
#course
#jeremy_howard
#video
New machine learning course by Jeremy Howard.
These videos was made in San Francisco University.
Headlines:
1—Introduction to Random Forests
2—Random Forest Deep Dive
3—Performance, Validation and Model Interpretation
4—Feature Importance, Tree Interpreter
5—Extrapolation and RF from Scratch
6—Data Products and Live Coding
7—RF from Scratch and Gradient Descent
8—Gradient Descent and Logistic Regression
9—Regularization, Learning Rates and NLP
10— More NLP and Columnar Data
11—Embeddings
12— Complete Rossmann, Ethical Issues
@machinelearning_tuts
Course URL:
http://course.fast.ai/ml
Read more:
http://www.fast.ai/2018/09/26/ml-launch/
You're on a journey to learn Data Science, Randy Lao is here to help you along the way!
watch free courses, download free books and learn more about machine learning every day...
#ml
#course
#resource
@machinelearning_tuts
http://www.claoudml.co/
watch free courses, download free books and learn more about machine learning every day...
#ml
#course
#resource
@machinelearning_tuts
http://www.claoudml.co/
Forwarded from Cutting Edge Deep Learning (Soran)
❇️ 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
----------
@machinelearning_tuts
@drivelesscar
@autonomousvehicle
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
----------
@machinelearning_tuts
@drivelesscar
@autonomousvehicle
Forwarded from Cutting Edge Deep Learning (Σ)
You're on a journey to learn Data Science, Randy Lao is here to help you along the way!
watch free courses, download free books and learn more about machine learning every day...
#ml
#course
#resource
@machinelearning_tuts
http://www.claoudml.co/
watch free courses, download free books and learn more about machine learning every day...
#ml
#course
#resource
@machinelearning_tuts
http://www.claoudml.co/
Cutting Edge Deep Learning
Photo
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)
——————————————
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
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)
——————————————
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
courses.cs.washington.edu
CSE 546
Machine Learning
Cutting Edge Deep Learning
Photo
✅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
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
GitHub
GitHub - jakevdp/PythonDataScienceHandbook: Python Data Science Handbook: full text in Jupyter Notebooks
Python Data Science Handbook: full text in Jupyter Notebooks - jakevdp/PythonDataScienceHandbook
🔹 Fundamentals of Data Analytics
——————
📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
#datasicence #analytics #machinelearning #math #skills #resume #datamining #course
——————
📌Via: @cedeeplearning
📌Other social media: https://linktr.ee/cedeeplearning
#datasicence #analytics #machinelearning #math #skills #resume #datamining #course