FOUNDATIONS OF MACHINE LEARNING
by Bloomberg
Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning
🎬 30 video lessons with slides
⏰ 28 hours
https://bloomberg.github.io/foml/#home
#machinelearning #ml
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by Bloomberg
Understand the Concepts, Techniques and Mathematical Frameworks Used by Experts in Machine Learning
🎬 30 video lessons with slides
⏰ 28 hours
https://bloomberg.github.io/foml/#home
#machinelearning #ml
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Intro to Machine Learning
by Kaggle
Learn the core ideas in machine learning, and build your first models.
1 How Models Work
The first step if you're new to machine learning.
2 Basic Data Exploration
Load and understand your data.
3 Your First Machine Learning Model
Building your first model. Hurray!
4 Model Validation
Measure the performance of your model, so you can test and compare alternatives.
5 Underfitting and Overfitting
Fine-tune your model for better performance.
6 Random Forests
Using a more sophisticated machine learning algorithm.
7 Machine Learning Competitions
Enter the world of machine learning competitions to keep improving and see your progress.
🔗 Course link
#machinelearning #ml
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by Kaggle
Learn the core ideas in machine learning, and build your first models.
1 How Models Work
The first step if you're new to machine learning.
2 Basic Data Exploration
Load and understand your data.
3 Your First Machine Learning Model
Building your first model. Hurray!
4 Model Validation
Measure the performance of your model, so you can test and compare alternatives.
5 Underfitting and Overfitting
Fine-tune your model for better performance.
6 Random Forests
Using a more sophisticated machine learning algorithm.
7 Machine Learning Competitions
Enter the world of machine learning competitions to keep improving and see your progress.
🔗 Course link
#machinelearning #ml
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Kaggle
Learn Intro to Machine Learning Tutorials
Learn the core ideas in machine learning, and build your first models.
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Artificial Neural Networks (ANN) with Keras in Python and R
Rating ⭐️: 4.7 out of 5
Duration ⏰: 11 hours on-demand video
Students 👨🏫: 143,495
Created by: Start-Tech Academy
🔗 Course link
Note: Free coupon is inserted in URL. Courses are FREE FOR FIRST 1000 enrollments
#ai #ml #neural_networks #machine_learning #data_science #deep_learning
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Rating ⭐️: 4.7 out of 5
Duration ⏰: 11 hours on-demand video
Students 👨🏫: 143,495
Created by: Start-Tech Academy
🔗 Course link
Note: Free coupon is inserted in URL. Courses are FREE FOR FIRST 1000 enrollments
#ai #ml #neural_networks #machine_learning #data_science #deep_learning
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Artificial Neural Networks (ANN) with Keras in Python and R
Rating ⭐️: 4.5 out of 5
Duration ⏰: 11 hours on-demand video
Students 👨🏫: 150,528
Created by: Start-Tech Academy
🔗 Course link
Linear Regression and Logistic Regression in Python
Rating ⭐️: 4.6 out of 5
Duration ⏰: 7.5 hours on-demand video
Students 👨🏫: 50,422
Created by: Start-Tech Academy
🔗 Course link
Support Vector Machines in Python: SVM Concepts & Code
Rating ⭐️: 4.7 out of 5
Duration ⏰: 6 hours on-demand video
Students 👨🏫: 80,685
Created by: Start-Tech Academy
🔗 Course link
Note: Free coupon is inserted in URL. Courses are FREE FOR FIRST 1000 enrollments
#ai #ml #neural_networks #machine_learning #data_science #deep_learning
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Rating ⭐️: 4.5 out of 5
Duration ⏰: 11 hours on-demand video
Students 👨🏫: 150,528
Created by: Start-Tech Academy
🔗 Course link
Linear Regression and Logistic Regression in Python
Rating ⭐️: 4.6 out of 5
Duration ⏰: 7.5 hours on-demand video
Students 👨🏫: 50,422
Created by: Start-Tech Academy
🔗 Course link
Support Vector Machines in Python: SVM Concepts & Code
Rating ⭐️: 4.7 out of 5
Duration ⏰: 6 hours on-demand video
Students 👨🏫: 80,685
Created by: Start-Tech Academy
🔗 Course link
Note: Free coupon is inserted in URL. Courses are FREE FOR FIRST 1000 enrollments
#ai #ml #neural_networks #machine_learning #data_science #deep_learning
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Udemy
Online Courses - Learn Anything, On Your Schedule | Udemy
Udemy is an online learning and teaching marketplace with over 213,000 courses and 62 million students. Learn programming, marketing, data science and more.
Image Recognition for Beginners using CNN in R Studio
Rating ⭐️: 4.3 out of 5
Duration ⏰: 11 hours on-demand video
Students 👨🏫: 76,420
Created by: Start-Tech Academy
What you will learn:
⭐️Get a solid understanding of Convolutional Neural Networks (CNN) and Deep Learning
⭐️Build an end-to-end Image recognition project in R
⭐️Learn usage of Keras and Tensorflow libraries
⭐️Use Artificial Neural Networks (ANN) to make predictions
🔗 Course link
Note: Free coupon is inserted in URL. Courses are FREE FOR FIRST 1000 enrollments
#ai #ml #neural_networks #machine_learning #data_science #deep_learning
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Rating ⭐️: 4.3 out of 5
Duration ⏰: 11 hours on-demand video
Students 👨🏫: 76,420
Created by: Start-Tech Academy
What you will learn:
⭐️Get a solid understanding of Convolutional Neural Networks (CNN) and Deep Learning
⭐️Build an end-to-end Image recognition project in R
⭐️Learn usage of Keras and Tensorflow libraries
⭐️Use Artificial Neural Networks (ANN) to make predictions
🔗 Course link
Note: Free coupon is inserted in URL. Courses are FREE FOR FIRST 1000 enrollments
#ai #ml #neural_networks #machine_learning #data_science #deep_learning
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Udemy
Image Recognition for Beginners using CNN in R Studio
Deep Learning based Convolutional Neural Networks (CNN) for Image recognition using Keras and Tensorflow in R Studio
ARTIFICIAL INTELLIGENCE FOR BEGINNERS
Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about Artificial Intelligence.
In this curriculum, you will learn:
⭐️Different approaches to Artificial Intelligence, including the "good old" symbolic approach with Knowledge Representation and reasoning (GOFAI).
⭐️Neural Networks and Deep Learning, which are at the core of modern AI. It illustrates the concepts behind these important topics using code in two of the most popular frameworks - TensorFlow and PyTorch.
⭐️Neural Architectures for working with images and text. It covers recent models but may lack a little bit on the state-of-the-art.
⭐️Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems.
Course Link
#ai #ml #neural_networks #machine_learning #data_science #deep_learning
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Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about Artificial Intelligence.
In this curriculum, you will learn:
⭐️Different approaches to Artificial Intelligence, including the "good old" symbolic approach with Knowledge Representation and reasoning (GOFAI).
⭐️Neural Networks and Deep Learning, which are at the core of modern AI. It illustrates the concepts behind these important topics using code in two of the most popular frameworks - TensorFlow and PyTorch.
⭐️Neural Architectures for working with images and text. It covers recent models but may lack a little bit on the state-of-the-art.
⭐️Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems.
Course Link
#ai #ml #neural_networks #machine_learning #data_science #deep_learning
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Machine Learning Engineer Learning Path
Course Link
Hey there!!
Check out this Machine Learning Course from Google.
Here's what you can learn from it.
👌A Tour of Google Cloud Hands-on Labs
👌Google Cloud Big Data and Machine Learning Fundamentals
👌How Google Does Machine Learning
👌Launching into Machine Learning
👌TensorFlow on Google Cloud
👌Feature Engineering
👌Machine Learning in the Enterprise
👌Production Machine Learning Systems
😃And a lot of interesting machine learning topics
Course Link
#ai #ml #neural_networks #machine_learning #data_science #deep_learning
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Course Link
Hey there!!
Check out this Machine Learning Course from Google.
Here's what you can learn from it.
👌A Tour of Google Cloud Hands-on Labs
👌Google Cloud Big Data and Machine Learning Fundamentals
👌How Google Does Machine Learning
👌Launching into Machine Learning
👌TensorFlow on Google Cloud
👌Feature Engineering
👌Machine Learning in the Enterprise
👌Production Machine Learning Systems
😃And a lot of interesting machine learning topics
Course Link
#ai #ml #neural_networks #machine_learning #data_science #deep_learning
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Data Science GitHub Repositories
freeCodeCamp
https://github.com/freeCodeCamp/freeCodeCamp
Data Science For Beginners
https://github.com/microsoft/Data-Science-For-Beginners
The Open Source Data Science Masters
https://github.com/datasciencemasters/
Free Data Science Books
https://github.com/chaconnewu/free-data-science-books
Data Science Curriculum
https://github.com/ossu/data-science
Awesome Data Science
https://github.com/academic/awesome-datascience
Data Science All Cheat Sheet
https://github.com/yash42828/Data-Science--All-Cheat-Sheet
Best of ML with Python
https://github.com/ml-tooling/best-of-ml-python
Data Science Interview Resources - Interview Questions
https://github.com/rbhatia46/Data-Science-Interview-Resources
#data_science #ml
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freeCodeCamp
https://github.com/freeCodeCamp/freeCodeCamp
Data Science For Beginners
https://github.com/microsoft/Data-Science-For-Beginners
The Open Source Data Science Masters
https://github.com/datasciencemasters/
Free Data Science Books
https://github.com/chaconnewu/free-data-science-books
Data Science Curriculum
https://github.com/ossu/data-science
Awesome Data Science
https://github.com/academic/awesome-datascience
Data Science All Cheat Sheet
https://github.com/yash42828/Data-Science--All-Cheat-Sheet
Best of ML with Python
https://github.com/ml-tooling/best-of-ml-python
Data Science Interview Resources - Interview Questions
https://github.com/rbhatia46/Data-Science-Interview-Resources
#data_science #ml
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GitHub
GitHub - freeCodeCamp/freeCodeCamp: freeCodeCamp.org's open-source codebase and curriculum. Learn to code for free.
freeCodeCamp.org's open-source codebase and curriculum. Learn to code for free. - freeCodeCamp/freeCodeCamp
Stanford Seminar on Machine Learning Explainability
Hey there ☺️. Are you interested in the Explainability of ML and everything going on around it from the basics to the latest research in it?
Here's a cool 😎 video from Stanford
Video Link
#stanford #ml
Hey there ☺️. Are you interested in the Explainability of ML and everything going on around it from the basics to the latest research in it?
Here's a cool 😎 video from Stanford
Video Link
#stanford #ml
1000 Data Science Projects
you can run on the browser with IPython.
Explore from 1000+ ready code templates to kickstart your AI projects
⭐️Classification
⭐️Regression
⭐️Clustering
🔗 Source link
#ai #ml #data_science #deep_learning
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you can run on the browser with IPython.
Explore from 1000+ ready code templates to kickstart your AI projects
⭐️Classification
⭐️Regression
⭐️Clustering
🔗 Source link
#ai #ml #data_science #deep_learning
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Data Science Engineering, your way
An introduction to different Data Science engineering concepts and Applications using Python and R
These series of tutorials on Data Science engineering will try to compare how different concepts in the discipline can be implemented in the two dominant ecosystems nowadays: R and Python.
We will do this from a neutral point of view. Our opinion is that each environment has good and bad things, and any data scientist should know how to use both in order to be as prepared as posible for job market or to start personal project.
To get a feeling of what is going on regarding this hot topic, we refer the reader to DataCamp's Data Science War infographic. Their infographic explores what the strengths of R are over Python and vice versa, and aims to provide a basic comparison between these two programming languages from a data science and statistics perspective.
Far from being a repetition from the previous, our series of tutorials will go hands-on into how to actually perform different data science taks such as working with data frames, doing aggregations, or creating different statistical models such in the areas of supervised and unsupervised learning.
We will use real-world datasets, and we will build some real data products. This will help us to quickly transfer what we learn here to actual data analysis situations.
Link
#ai #ml #data_science
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An introduction to different Data Science engineering concepts and Applications using Python and R
These series of tutorials on Data Science engineering will try to compare how different concepts in the discipline can be implemented in the two dominant ecosystems nowadays: R and Python.
We will do this from a neutral point of view. Our opinion is that each environment has good and bad things, and any data scientist should know how to use both in order to be as prepared as posible for job market or to start personal project.
To get a feeling of what is going on regarding this hot topic, we refer the reader to DataCamp's Data Science War infographic. Their infographic explores what the strengths of R are over Python and vice versa, and aims to provide a basic comparison between these two programming languages from a data science and statistics perspective.
Far from being a repetition from the previous, our series of tutorials will go hands-on into how to actually perform different data science taks such as working with data frames, doing aggregations, or creating different statistical models such in the areas of supervised and unsupervised learning.
We will use real-world datasets, and we will build some real data products. This will help us to quickly transfer what we learn here to actual data analysis situations.
Link
#ai #ml #data_science
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GitHub
GitHub - jadianes/data-science-your-way: Ways of doing Data Science Engineering and Machine Learning in R and Python
Ways of doing Data Science Engineering and Machine Learning in R and Python - GitHub - jadianes/data-science-your-way: Ways of doing Data Science Engineering and Machine Learning in R and Python
How To Label Data
At LightTag, we create tools to annotate data for natural language processing (NLP). At its core, the process of annotating at scale is a team effort. Managing the annotation process draws on the same principles as managing any other human endeavor. You need to clearly understand what needs to be done, articulate it repeatedly to your team, give them the tools and training to execute effectively, measure their performance against your goals, and help them improve over time. we will draw on our experience with various annotation projects to describe the seven distinct stages of an annotation life cycle that Jane will go through. We will explain the purpose of each stage, describe key considerations that should occur during each, and wrap each stage up with the assets you should expect to have at the end.
Link
#ml #data_science
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At LightTag, we create tools to annotate data for natural language processing (NLP). At its core, the process of annotating at scale is a team effort. Managing the annotation process draws on the same principles as managing any other human endeavor. You need to clearly understand what needs to be done, articulate it repeatedly to your team, give them the tools and training to execute effectively, measure their performance against your goals, and help them improve over time. we will draw on our experience with various annotation projects to describe the seven distinct stages of an annotation life cycle that Jane will go through. We will explain the purpose of each stage, describe key considerations that should occur during each, and wrap each stage up with the assets you should expect to have at the end.
Link
#ml #data_science
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www.lighttag.io
How To Label Data
Labeling Data makes or breaks an NLP project. We describe the seven stages of a successful labeling project
Your Guide to Latent Dirichlet Allocation
Latent Dirichlet Allocation (LDA) is a “generative probabilistic model” of a collection of composites made up of parts. Its uses include Natural Language Processing (NLP) and topic modelling, among others.
In terms of topic modelling, the composites are documents and the parts are words and/or phrases (phrases n words in length are referred to as n-grams).
But you could apply LDA to DNA and nucleotides, pizzas and toppings, molecules and atoms, employees and skills, or keyboards and crumbs.
The probabilistic topic model estimated by LDA consists of two tables (matrices). The first table describes the probability or chance of selecting a particular part when sampling a particular topic (category).
Link
#ml #data_science
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Latent Dirichlet Allocation (LDA) is a “generative probabilistic model” of a collection of composites made up of parts. Its uses include Natural Language Processing (NLP) and topic modelling, among others.
In terms of topic modelling, the composites are documents and the parts are words and/or phrases (phrases n words in length are referred to as n-grams).
But you could apply LDA to DNA and nucleotides, pizzas and toppings, molecules and atoms, employees and skills, or keyboards and crumbs.
The probabilistic topic model estimated by LDA consists of two tables (matrices). The first table describes the probability or chance of selecting a particular part when sampling a particular topic (category).
Link
#ml #data_science
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Artificial Neural Network for Regression
Rating ⭐️: 4.6 out of 5
Duration ⏰: 1hr 11min on-demand video
Students 👨🏫: 49,827
Created by: Hadelin de Ponteves, SuperDataScience Team, Ligency Team
🔗 Course link
#ai #ml #neural_networks #machine_learning #data_science #regression
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Rating ⭐️: 4.6 out of 5
Duration ⏰: 1hr 11min on-demand video
Students 👨🏫: 49,827
Created by: Hadelin de Ponteves, SuperDataScience Team, Ligency Team
🔗 Course link
#ai #ml #neural_networks #machine_learning #data_science #regression
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Udemy
Data Manipulation in Python: Master Python, Numpy & Pandas
Learn Python, NumPy & Pandas for Data Science: Master essential data manipulation for data science in python
🔥FREE COURSE ON GENERATIVE AI🔥
Interested in learning about GENERATIVE AI?🔥
Here's a free course from Google.
Link
#generative #ai #ml #ai
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Interested in learning about GENERATIVE AI?🔥
Here's a free course from Google.
Link
#generative #ai #ml #ai
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ML For Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Creator: microsoft
Stars ⭐️: 53.7k
Forked By: 11.3k
https://github.com/microsoft/ML-For-Beginners
#microsoft #ml
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12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
Creator: microsoft
Stars ⭐️: 53.7k
Forked By: 11.3k
https://github.com/microsoft/ML-For-Beginners
#microsoft #ml
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GitHub
GitHub - microsoft/ML-For-Beginners: 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all - microsoft/ML-For-Beginners
handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Creator: Aurélien Geron
Stars ⭐️: 25.7k
Forked By: 12.2k
https://github.com/ageron/handson-ml2
#Jupyter #ml
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A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Creator: Aurélien Geron
Stars ⭐️: 25.7k
Forked By: 12.2k
https://github.com/ageron/handson-ml2
#Jupyter #ml
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GitHub
GitHub - ageron/handson-ml2: A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep…
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2. - ageron/handson-ml2
Made-With-ML
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Creator: Goku Mohandas
Stars ⭐️: 34.8k
Forked By: 5.6k
https://github.com/GokuMohandas/Made-With-ML
#ml #machinelarning #datascience
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Learn how to design, develop, deploy and iterate on production-grade ML applications.
Creator: Goku Mohandas
Stars ⭐️: 34.8k
Forked By: 5.6k
https://github.com/GokuMohandas/Made-With-ML
#ml #machinelarning #datascience
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool repositories.
*This channel belongs to @bigdataspecialist group
GitHub
GitHub - GokuMohandas/Made-With-ML: Learn how to design, develop, deploy and iterate on production-grade ML applications.
Learn how to design, develop, deploy and iterate on production-grade ML applications. - GokuMohandas/Made-With-ML
AutoML_Alex
State-of-the art Automated Machine Learning python library for Tabular Data
Creator: Alex Lekov
Stars ⭐️: 191
Forked By: 41
https://github.com/Alex-Lekov/AutoML_Alex
#ml #machinelarning #datascience
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool repositories.
*This channel belongs to @bigdataspecialist group
State-of-the art Automated Machine Learning python library for Tabular Data
Creator: Alex Lekov
Stars ⭐️: 191
Forked By: 41
https://github.com/Alex-Lekov/AutoML_Alex
#ml #machinelarning #datascience
➖➖➖➖➖➖➖➖➖➖➖➖➖➖
Join @datascience_bds for more cool repositories.
*This channel belongs to @bigdataspecialist group
GitHub
GitHub - Alex-Lekov/AutoML_Alex: State-of-the art Automated Machine Learning python library for Tabular Data
State-of-the art Automated Machine Learning python library for Tabular Data - Alex-Lekov/AutoML_Alex