Dan | Machine Learning Engineer
@DanKornas
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🤖 ML Engineer 🔬 AI Educator 💻 Master AI from Zero to Advanced ➡️
Joined June 2021
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You have 4 months left till the end of 2024.
It's never too late to get into AI, so start right now.
I am starting a Complete Machine Learning/Data Science Bootcamp program.
If you are interested in participating, send me a message ✉️
🗺️ Here is the bootcamp roadmap plan 👇
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Want to learn SQL? 🖥️
Here is a map of all the most important SQL commands & functions you should know 👇
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SQL Tutorial - Full Database Course in 4 Hours
This course consists of a series of videos where you will be looking at database management basics and SQL using the MySQL RDBMS.
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Starting today, I'm counting down the last 60 days to the New Year by starting the
#60daysOfMachineLearning
challenge.
Every day I will be posting about Python, SQL, Data Science and Machine Learning to help you start learning about AI now!
Lets get started!
Day 1 - Python 🐍
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🐍Learn Python - Full Course for Beginners in 4 Hours
This course will give you a full introduction into all of the core concepts in Python. Follow along with the videos and you'll be a Python programmer in no time!
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Best YouTube channels for Machine Learning:
☑️ Python ➟ Derek Banas
☑️ SQL ➟ Programming with Mosh
☑️ Data Visualization➟ Corey Schafer
☑️ Machine Learning ➟ sentdex
☑️ Deep Learning ➟ Daniel Bourke
☑️ Mathematics ➟ 3Blue1Brown
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To become a Machine Learning Engineer:
• Python
• numpy, pandas, matplotlib
• TensorFlow or PyTorch
• Jupyter, Colab
• Analysis > Code
• 99%: Foundational algorithms
• 1%: Other algorithms
• Solve problems ← This is key
• Teaching = 2 × Learning
• Have fun!
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Study Python Programming and Computer Science at
@MIT
for FREE on Youtube 🥸
They offer an applied & beginner-friendly introduction to common computer science concepts & techniques in Python
Playlist:
Syllabus, slides, codes:
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👉 Machine Learning & Deep Learning Tutorials
This repository contains a topic-wise curated list of Machine Learning and Deep Learning tutorials, articles and other resources.
🔗
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Data Analysis with Python: Zero to Pandas
This is a practical, beginner-friendly, and coding-focused introduction to data analysis covering the basics of Python, Numpy, Pandas, data visualization and exploratory data analysis.
🔗
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👉 Deep Learning Paper Implementations 📃
A collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations and side-by-side notes.
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Don't just learn whatever's hot at the moment.
Make sure you understand the fundamentals of machine learning first.
I strongly recommend refreshing your linear algebra before getting into deep learning. 👇
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Seven Python Libraries for Different Uses 🐍👇
🤖Selenium ↣ Automation
📄Openpyxl ↣ Handling Excel sheets
🌐Tensorflow ↣ Machine Learning
🧮Numpy ↣ Mathematics
🐼Pandas ↣ Data Analysis
📸OpenCV ↣ Computer vision
🔦Pytorch ↣ Natural Language Processing
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🐍 Essential Cheat Sheets for Machine Learning and Deep Learning Engineers (with Python)📑
1. Keras
2. Numpy
3. Pandas
4. Scipy
5. Matplotlib
6. Scikit-learn
7. Neural Networks Zoo
8. ggplot2
9. PySpark
10. R Studio
11. Jupyter Notebook
12. Dask
Thread 🧵👇
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You don't need to spend a penny to learn Machine Learning.
Here are 5 GitHub repositories to learn Machine Learning for free 👇
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Day 2 of
#60daysOfMachineLearning
Because Python is still the most popular for ML, the next few days we will need to quickly go over the fundamentals.
But don't worry, once we finish Python, it will start to get more fun. 😉
So lets start off today with - Python Data Types
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PyTorch is one of the top skills needed in AI, powering cutting-edge technologies like OpenAI’s GPT models, Tesla’s Autopilot, Facebook’s AI research, and many of the applications at Google, Microsoft, and Amazon.
"Mastering PyTorch" has really helped me improve my PyTorch
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Unlock your potential as a data scientist with these Microsoft certifications
🧵 Check out these resources 👇
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Learn Matplotlib in 4 hours
@Matplotlib
allows us to create some attractive plots in order to visualize our data in easy to digest formats.
In this Python series you will master Matplotlib 👇
🔗
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Day 30 of
#60daysOfMachineLearning
🔷Pandas for Data Analysis 🔷
Pandas is a free Python library that is used heavily in the data science, data manipulation, and machine learning industries.
Learn Pandas for Data Analysis with 5 hours of tutorials 👇
🔗
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👉 Machine Learning for Production
This repository contains a curated list of awesome open source libraries that will help you deploy, monitor, version, scale, and secure your production machine learning.
🔗
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👉 NLP Course by
@huggingface
This course will teach you about natural language processing (NLP) using libraries from the Hugging Face ecosystem:
- Transformers
- Datasets
- Tokenizers
- Accelerate
🔗
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Its only been a couple of days and ChatGPT has revolutionized the internet.
Here is a simplified overview of how ChatGPT was trained 👇
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👉 From Zero to AI Research Scientist Full Resources Guide
This guide is designated to anybody with basic programming knowledge or a computer science background interested in becoming a Research Scientist with a target on Deep Learning and NLP.
🔗
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👉 Machine Learning Pipeline
An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch by
@Harvard
🔗
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SQL is one of the most important skills for any programmer, irrespective of technology, framework, and domain.
It is even said to be more popular than the mainstream programming languages like Java and Python.
Start future-proofing your career with these 5 awesome courses 👇
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👉 Machine Learning Pipeline
An in-depth machine learning tutorial introducing readers to a whole machine learning pipeline from scratch by
@Harvard
🔗
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Data structures and algorithms are one of the most important aspects of computer science. They are among the essential concepts in machine learning.
They are used to store data efficiently so that it takes up less space, while algorithms are used to process data.
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👉 Machine Learning Algorithms
A collection of minimal and clean implementations of machine learning algorithms. This repo is targeting people who want to learn internals of ml algorithms or implement them from scratch.
🔗
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👉 Introduction to Machine Learning
The course serves as a basic introduction to machine learning and covers key concepts
🔗
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👉 Pytorch
This repository has a collection of best tutorials, projects, libraries, videos, papers, books and anything related to the incredible PyTorch.
🔗
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There are thousands of machine learning algorithms available, yet most of them are useless.
A handful is all you'll ever need.
A nice starting point:
• Linear/Logistic Regression
• Decision Trees
• Neural Networks
• XGBoost
• Naive Bayes
• PCA
• KNN
• SVM
• t-SNE
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Unlock your potential as a data scientist with these Microsoft certifications
🧵 Check out these resources 👇
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We started off
#60daysOfMachineLearning
this week with the basics of data types in Python.
Here is a quick data types cheat sheet to use for review.
Save it for later 🔽👍
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Crush Machine Learning in the second half of 2022 🎯
🚀July - Python/SQL
🚀August - Data Viz in Pandas/Matplotlib
🚀September - Math/Stats
🚀October - Machine Learning
🚀November - Deep Learning
🚀December - Projects + Interview Prep
🤖New Year - Apply for ML Engineer roles
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🤖 Reinforcement Learning Lecture Series by
@DeepMind
This Deep Learning Lecture Series on Reinforcement Learning is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence.
🔗
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👉 Deep Learning Projects
In this repo, There are specific bite-sized projects to learn an aspect of deep learning, starting from scratch. The projects are in order from beginner to more advanced, but feel free to skip around.
🔗
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👉 Introduction to Machine Learning
The course serves as a basic introduction to machine learning and covers key concepts
🔗
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LEARN PYTHON AND MACHINE LEARNING
A simplified guide for your coding journey. 🧵👩💻
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Understand the math behind the following machine learning algorithms
- Linear Regression
- Logistic Regression
- Decision Trees
- Naive Bayes
- Gradient Boosted Trees
- CNN
Once you grok the math, you can intuitively sense why your model behaves the way it does
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👉 Machine Learning Tutorials
A collection of 100+ machine learning tutorials mostly written in python.
🔗
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Data Analysis with Python Course
Learn the basics of Python, Numpy, Pandas, Data Visualization, and Exploratory Data Analysis. By the end of the course, you will be able to build an end-to-end real-world course project.
🔗
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👉 Full Stack Deep Learning
Learn full-stack production deep learning:
🔹ML Projects
🔹Infrastructure and Tooling
🔹Experiment Managing
🔹Troubleshooting DNNs
🔹Data Management
🔹Data Labeling
🔹Monitoring ML Models
🔹Web Deployment
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👉 Stanford CS229: Machine Learning
Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition.
🔗 Link to course:
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⚙️ Must-have Chrome Extensions For Machine Learning Engineers And Data Scientists 🚀
Thread🧵👇
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👉 Deep Learning for Computer Vision (DL4CV)
Learn about modern methods for computer vision:
CNN
Advanced PyTorch
Understanding Neural Networks
RNN, Attention and ViT
Generative Models
GPU Fundamentals
🔗
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👉 Best of Machine Learning with Python
This list contains 880 awesome open-source projects on Data Visualization, NLP, Time Series, Machine Learning, Data Pipelines, Reinforcement Learning, Recommender Systems and more.
🔗
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👉 Foundations Of ML
Using this repo, You can learn the foundations of ML through intuitive explanations, clean code and visuals. Also, You can learn how to apply ML to build a production grade product to deliver value.
🔗
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👉 Machine Learning Residency
In this Github repo, you'll find curated AI and ML Residency Programs from top companies like Apple, Facebook, OpenAI, IBM, Uber, Microsoft, Google, NVIDIA, Intel and more.
🔗
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Data science will eventually become a low-code profession.
It's all about:
- Transforming the business problem into a machine learning problem
- Understanding how to transform the data
- Using low-code platforms to run smart experiments
- Analyzing models and predictions
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👉 Machine Learning Notebooks
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
🔗
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Machine Learning starter pack 📦
🔸GoogleColab: Code editor
🔸Pandas: Importing and manipulating data
🔸Numpy: For performing linear algebraic functions
🔸Scikit learn: Make machine learning models
🔸TensorFlow/ PyTorch: Making deep learning models
🔸Matplotlib: Visualizing data
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👉 Machine Learning Tutorials
A collection of 100+ machine learning tutorials mostly written in python.
🔗
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🤖 60 Days Of Deep Reinforcement Learning
In this repo, You'll find everything well arranged from articles, tutorials, YouTube videos, papers implementations, projects and codes.
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👉 Machine Learning From Scratch
Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. It aims to cover everything from linear regression to deep learning.
🔗
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👉
@Stanford
ML Systems
Stanford offers an exhilarating seminar series covering a diverse range of topics, all aimed at enhancing your knowledge in building machine learning systems - completely FREE!
🔗
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👉 Machine Learning From Scratch
Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. It aims to cover everything from linear regression to deep learning.
🔗
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👉 Full Stack Deep Learning
Learn full-stack production deep learning:
🔹ML Projects
🔹Infrastructure and Tooling
🔹Experiment Managing
🔹Troubleshooting DNNs
🔹Data Management
🔹Data Labeling
🔹Monitoring ML Models
🔹Web deployment
🔗
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Data structures and algorithms are one of the most important aspects of computer science. They are among the essential concepts in machine learning.
They are used to store data efficiently so that it takes up less space, while algorithms are used to process data.
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👉 Introduction to Deep Learning by
@MIT
An efficient and high-intensity bootcamp designed to teach you the fundamentals of deep learning as quickly as possible.
🔗
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As we kick off the first day of
#60daysOfMachineLearning
with Python, here are 5 websites and courses to learn the python programming language for data science and machine learning 🤖
Save this for later 👇 it can come in handy👍
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🤖 60 Days Of Deep Reinforcement Learning
In this repo, You'll find everything well arranged from articles, tutorials, youtube videos, papers implementations, projects and codes.
🔗
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👉 NLP Course by
@huggingface
This course will teach you about natural language processing (NLP) using libraries from the Hugging Face ecosystem:
- Transformers
- Datasets
- Tokenizers
- Accelerate
🔗
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Need to learn the fundamentals of Python programming? 🖥️
I got you covered 💪
Here are 30 days of Python clips to help you start learning 🎉
🧵👇
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👉 Machine Learning Algorithms
A collection of minimal and clean implementations of machine learning algorithms. This repo is targeting people who want to learn internals of ml algorithms or implement them from scratch.
🔗
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👉 Machine Learning Projects And Tutorials
In this repository you will find tutorials and projects related to Machine Learning.
🔗
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FREE Machine Learning Course from
@Harvard
Learn:
🔹Basics ML Algorithms
🔹Recommendation Systems
🔹Various tuning methods
🔗
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Day 52 of
#60daysOfMachineLearning
🔷 Deep Learning 🔷
Deep learning is a type of machine learning algorithm that uses deep neural networks to learn complex patterns and relationships in data.
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👉 Machine Learning Projects And Tutorials
In this repository you will find tutorials and projects related to Machine Learning.
🔗
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👉 Deep Learning Projects
In this repo, There are specific bite-sized projects to learn an aspect of deep learning, starting from scratch. The projects are in order from beginner to more advanced, but feel free to skip around.
🔗
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In the last couple of days of
#60daysOfMachineLearning
we went over the fundamentals of SQL.
If you really want to sharpen your SQL skills, I highly recommend going through this book 👇
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🐍 Awesome Python
A Github repository with a curated list of awesome Python frameworks, libraries, software and resources.
If you don't know which library or tool to use for your project, this is your go-to guide 👇
🔗
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👉 Awesome Tensorlfow
A curated list of awesome TensorFlow:
🔵 Tutorials
🔵 Models/Projects
🔵 Libraries
🔵 Tools/Utilities
🔵 Videos
🔵 Papers
🔵 Articles
🔵 Community
🔵 Books
and more.
🔗
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This is one of the best roadmaps for learning to become a Machine Learning Engineer I have seen. 🤖
Be sure to save this to start your ML journey 👇
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👉 Top Deep Learning Projects
This repository is a goldmine for anyone looking to dive into the top deep learning projects and papers. From CNNs to RNNs, it's got it all.
🔗
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🚀 Intro to Machine Learning from Kaggle
Learn the core ideas in machine learning, and build your first models.
🔗
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👉 From Zero to AI Research Scientist Full Resources Guide
This guide is for anybody with basic programming knowledge interested in becoming a Research Scientist in Deep Learning and NLP.
🔗
3
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🤖 Reinforcement Learning Lecture Series by
@DeepMind
This Deep Learning Lecture Series on Reinforcement Learning is a collaboration between DeepMind and the UCL Centre for Artificial Intelligence.
🔗
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A really great and free online book that covers all the basic tools that you will need for data science and machine learning:
- NumPy
- Seaborn
- Matplotlib
- Pandas
- Scikit-Learn
#66daysofdata
Python Data Science Handbook: …
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Day 30 of
#60daysOfMachineLearning
🔷 Pandas for Data Analysis 🔷
Pandas is a Python library that is used heavily in the data science, data manipulation, and machine learning.
🔗
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🚀 Kaggle Computer Vision Course
Build convolutional neural networks with TensorFlow and Keras
🔗
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🚀 Machine Learning Tutorials 🚀
🔗🔗
This repository is a goldmine 💎 for anyone looking to dive into the world of Machine Learning. It's packed with:
1️⃣ Comprehensive tutorials 📚 on a wide range of ML topics.
2️⃣ Useful Python 🐍 code snippets for
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🔘
@MIT
Deep Learning in Life Sciences
A course introducing foundations of ML for applications in genomics and the life sciences more broadly.
🔗 Course ➡️
🔗 Materials ➡️
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👉 Machine Learning Tutorials
A collection of 100+ machine learning tutorials mostly written in python.
🔗
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Understanding and interpreting machine learning models is more than just a skill—it's a necessity for responsible AI development. As machine learning engineers, our goal is not only to develop high-performance models but also to ensure they are transparent, interpretable, and
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👉 Awesome Computer Vision
Awesome Books, Courses, Papers, Software, Datasets, Pre-trained Computer Vision Models, Tutorials, Talks, Blogs, Links and Songs related to Computer Vision.
🔗
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WOW! So many new followers! 😲😅
I'm Dan, 👋
Im a Machine Learning Engineer and I share daily content & experience in:
🐍Python
🤖Machine Learning
👾Deep Learning
⚙️MLOps
Why don't you introduce yourself and share what your are currently learning or working on?
👇👇👇👇👇
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👉 Homemade Machine Learning
This github repos covers python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained.
🔗
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👉 AI Expert Roadmap
This ultimate repository for AI contains roadmaps for:
🔹 Artificial Intelligence
🔹 Machine Learning
🔹 Deep Learning
🔹 Data Engineer
🔹 Big Data
🔹 Data Science
🔗
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👉 Deep Learning for Computer Vision from Stanford
This lecture collection is a deep dive into details of deep learning architectures with a focus on learning end-to-end models for image classification.
🔗
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👉 Deep Learning Papers Reading Roadmap
If you are a newcomer to the Deep Learning area and don’t know which paper to read - Check out the reading roadmap of Deep Learning papers given in this repo!
🔗
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👉 Interactive Tools for Machine Learning
This is one of the best and most recommended github repo for using interactive and visualization tools that will help you understand various topics of machine learning.
🔗
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👉 Deep Learning for Computer Vision from Stanford
This lecture collection is a deep dive into details of deep learning architectures with a focus on learning end-to-end models for image classification.
🔗
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Building an effective machine learning model is only about 10% of the effort required, the remaining 90% involves developing and maintaining the supporting ML systems.
The Machine Learning Solutions Architect Handbook by David Ping is one of the best books that I have read so
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📊Data Science for Everyone
NYU Center for Data Science is releasing a new course book and video series which will cover statistics, programming, and machine learning approaches.
New videos will be released weekly 👇
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👉 Machine Learning for Beginners - A Curriculum
@Microsoft
has created a free MIT-approved learning course to teach students the basics of machine learning covering a lot of things.
🔗
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This GitHub repository is a treasure trove of computer science video courses, offering an unparalleled learning experience. From renowned universities to industry experts, the content is rich, diverse, and absolutely FREE!
Here's what you can explore:
Artificial Intelligence
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👉 Best of Machine Learning with Python
This list contains 880 awesome open-source projects on Data Visualization, NLP, Time Series, Machine Learning, Data Pipelines, Reinforcement Learning, Recommender Systems and more.
🔗
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