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.

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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

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#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

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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

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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

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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

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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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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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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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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

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

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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

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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

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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

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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


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🔥FREE COURSE ON GENERATIVE AI🔥

Interested in learning about GENERATIVE AI?🔥

Here's a free course from Google.

Link

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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

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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

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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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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

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Essential Machine Learning Algorithms for Data Scientists

Master essential machine learning algorithms and elevate your data science skills

Rating ⭐️: 4.6 out 5
Students 👨‍🎓 : 791
Duration ⏰ : 43min of on-demand video
Created by 👨‍🏫: Arunkumar Krishnan

🔗 Course Link

#ml #algorithm
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