10 Things you need to become an AI/ML engineer:
1. Framing machine learning problems
2. Weak supervision and active learning
3. Processing, training, deploying, inference pipelines
4. Offline evaluation and testing in production
5. Performing error analysis. Where to work next
6. Distributed training. Data and model parallelism
7. Pruning, quantization, and knowledge distillation
8. Serving predictions. Online and batch inference
9. Monitoring models and data distribution shifts
10. Automatic retraining and evaluation of models
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best 👍👍
1. Framing machine learning problems
2. Weak supervision and active learning
3. Processing, training, deploying, inference pipelines
4. Offline evaluation and testing in production
5. Performing error analysis. Where to work next
6. Distributed training. Data and model parallelism
7. Pruning, quantization, and knowledge distillation
8. Serving predictions. Online and batch inference
9. Monitoring models and data distribution shifts
10. Automatic retraining and evaluation of models
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best 👍👍
👍8❤2👏1
Complete Roadmap to learn Machine Learning and Artificial Intelligence
👇👇
Week 1-2: Introduction to Machine Learning
- Learn the basics of Python programming language (if you are not already familiar with it)
- Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning
- Study linear algebra and calculus basics
- Complete online courses like Andrew Ng's Machine Learning course on Coursera
Week 3-4: Deep Learning Fundamentals
- Dive into neural networks and deep learning
- Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
- Implement deep learning models using frameworks like TensorFlow or PyTorch
- Complete online courses like Deep Learning Specialization on Coursera
Week 5-6: Natural Language Processing (NLP) and Computer Vision
- Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis
- Dive into computer vision concepts like image classification, object detection, and image segmentation
- Work on projects involving NLP and Computer Vision applications
Week 7-8: Reinforcement Learning and AI Applications
- Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks
- Explore AI applications in fields like healthcare, finance, and autonomous vehicles
- Work on a final project that combines different aspects of Machine Learning and AI
Additional Tips:
- Practice coding regularly to strengthen your programming skills
- Join online communities like Kaggle or GitHub to collaborate with other learners
- Read research papers and articles to stay updated on the latest advancements in the field
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of ML & AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn ML & AI 👇
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
👇👇
Week 1-2: Introduction to Machine Learning
- Learn the basics of Python programming language (if you are not already familiar with it)
- Understand the fundamentals of Machine Learning concepts such as supervised learning, unsupervised learning, and reinforcement learning
- Study linear algebra and calculus basics
- Complete online courses like Andrew Ng's Machine Learning course on Coursera
Week 3-4: Deep Learning Fundamentals
- Dive into neural networks and deep learning
- Learn about different types of neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
- Implement deep learning models using frameworks like TensorFlow or PyTorch
- Complete online courses like Deep Learning Specialization on Coursera
Week 5-6: Natural Language Processing (NLP) and Computer Vision
- Explore NLP techniques such as tokenization, word embeddings, and sentiment analysis
- Dive into computer vision concepts like image classification, object detection, and image segmentation
- Work on projects involving NLP and Computer Vision applications
Week 7-8: Reinforcement Learning and AI Applications
- Learn about Reinforcement Learning algorithms like Q-learning and Deep Q Networks
- Explore AI applications in fields like healthcare, finance, and autonomous vehicles
- Work on a final project that combines different aspects of Machine Learning and AI
Additional Tips:
- Practice coding regularly to strengthen your programming skills
- Join online communities like Kaggle or GitHub to collaborate with other learners
- Read research papers and articles to stay updated on the latest advancements in the field
Pro Tip: Roadmap won't help unless you start working on it consistently. Start working on projects as early as possible.
2 months are good as a starting point to get grasp the basics of ML & AI but mastering it is very difficult as AI keeps evolving every day.
Best Resources to learn ML & AI 👇
Learn Python for Free
Prompt Engineering Course
Prompt Engineering Guide
Data Science Course
Google Cloud Generative AI Path
Unlock the power of Generative AI Models
Machine Learning with Python Free Course
Machine Learning Free Book
Deep Learning Nanodegree Program with Real-world Projects
AI, Machine Learning and Deep Learning
Join @free4unow_backup for more free courses
ENJOY LEARNING👍👍
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Deep Learning Course – Math and Applications
👇👇
https://www.freecodecamp.org/news/deep-learning-course-math-and-applications
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best 👍👍
👇👇
https://www.freecodecamp.org/news/deep-learning-course-math-and-applications
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best 👍👍
👍5
This is a class from Harvard University:
"Introduction to Data Science with Python."
It's free. You should be familiar with Python to take this course.
The course is for beginners. It's for those who want to build a fundamental understanding of machine learning and artificial intelligence.
It covers some of these topics:
• Generalization and overfitting
• Model building, regularization, and evaluation
• Linear and logistic regression models
• k-Nearest Neighbor
• Scikit-Learn, NumPy, Pandas, and Matplotlib
Link: https://pll.harvard.edu/course/introduction-data-science-python
"Introduction to Data Science with Python."
It's free. You should be familiar with Python to take this course.
The course is for beginners. It's for those who want to build a fundamental understanding of machine learning and artificial intelligence.
It covers some of these topics:
• Generalization and overfitting
• Model building, regularization, and evaluation
• Linear and logistic regression models
• k-Nearest Neighbor
• Scikit-Learn, NumPy, Pandas, and Matplotlib
Link: https://pll.harvard.edu/course/introduction-data-science-python
👍13❤1👏1
How to Tailor Resume based on the Job Description 👇
To tailor your resume based on a job description:
1. Keyword Integration: Identify key words in the job description and incorporate them into your resume, especially in the skills and experience sections.
2. Relevant Experience: Highlight experiences that directly relate to the job requirements. Focus on accomplishments and skills relevant to the position.
3. Customize Objective or Summary: Tailor your resume objective or summary to align with the specific job, emphasizing how your skills and experience make you a strong fit.
4. Quantify Achievements: Use quantifiable metrics to showcase your achievements. Numbers stand out and provide concrete evidence of your impact.
5. Matched Skills Section: Create a skills section that mirrors the required skills in the job description. Be truthful, but emphasize the skills most relevant to the role.
6. Reorder Sections: Arrange resume sections to prioritize the most relevant information. If education is crucial, move it up; if experience is paramount, highlight it prominently.
7. Research the Company: Tailor your resume to the company culture and values. Showcase experiences that demonstrate your alignment with their mission.
8. Use Action Verbs: Start bullet points with strong action verbs to convey a sense of accomplishment and capability.
Join @getjobss for latest jobs and internship opportunities
Share with your friends if it helps 😄
To tailor your resume based on a job description:
1. Keyword Integration: Identify key words in the job description and incorporate them into your resume, especially in the skills and experience sections.
2. Relevant Experience: Highlight experiences that directly relate to the job requirements. Focus on accomplishments and skills relevant to the position.
3. Customize Objective or Summary: Tailor your resume objective or summary to align with the specific job, emphasizing how your skills and experience make you a strong fit.
4. Quantify Achievements: Use quantifiable metrics to showcase your achievements. Numbers stand out and provide concrete evidence of your impact.
5. Matched Skills Section: Create a skills section that mirrors the required skills in the job description. Be truthful, but emphasize the skills most relevant to the role.
6. Reorder Sections: Arrange resume sections to prioritize the most relevant information. If education is crucial, move it up; if experience is paramount, highlight it prominently.
7. Research the Company: Tailor your resume to the company culture and values. Showcase experiences that demonstrate your alignment with their mission.
8. Use Action Verbs: Start bullet points with strong action verbs to convey a sense of accomplishment and capability.
Join @getjobss for latest jobs and internship opportunities
Share with your friends if it helps 😄
👍9
ChatGPT Prompt to learn any skill
👇👇
(Tap on above text to copy)
👇👇
I am seeking to become an expert professional in [Making ChatGPT prompts perfectly]. I would like ChatGPT to provide me with a complete course on this subject, following the principles of Pareto principle and simulating the complexity, structure, duration, and quality of the information found in a college degree program at a prestigious university. The course should cover the following aspects: Course Duration: The course should be structured as a comprehensive program, spanning a duration equivalent to a full-time college degree program, typically four years. Curriculum Structure: The curriculum should be well-organized and divided into semesters or modules, progressing from beginner to advanced levels of proficiency. Each semester/module should have a logical flow and build upon the previous knowledge. Relevant and Accurate Information: The course should provide all the necessary and up-to-date information required to master the skill or knowledge area. It should cover both theoretical concepts and practical applications. Projects and Assignments: The course should include a series of hands-on projects and assignments that allow me to apply the knowledge gained. These projects should range in complexity, starting from basic exercises and gradually advancing to more challenging real-world applications. Learning Resources: ChatGPT should share a variety of learning resources, including textbooks, research papers, online tutorials, video lectures, practice exams, and any other relevant materials that can enhance the learning experience. Expert Guidance: ChatGPT should provide expert guidance throughout the course, answering questions, providing clarifications, and offering additional insights to deepen understanding. I understand that ChatGPT's responses will be generated based on the information it has been trained on and the knowledge it has up until September 2021. However, I expect the course to be as complete and accurate as possible within these limitations. Please provide the course syllabus, including a breakdown of topics to be covered in each semester/module, recommended learning resources, and any other relevant information(Tap on above text to copy)
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Breaking into ML Engineering can be very confusing in 2024!
Should I learn TensorFlow or PyTorch? Python or R? Scikit-learn or XGBoost? GCP or AWS? FastAPI or Streamlit?
Fundamental principles are more important than tools:
- understanding statistics and deep learning is more important than TensorFlow vs PyTorch.
- understanding functional and object-oriented programming is more important than Python or R.
- understanding feature engineering is more important than Scikit-learn vs XGBoost.
- understanding scalable and resilient architectures is more important than GCP or AWS.
- understanding models serving is more important than FastAPI or Streamlit.
Knowing these will allow you to pick up new emerging tools easily.
Stick to fundamentals first.
Join for more: https://t.me/machinelearning_deeplearning
All the best 👍👍
Should I learn TensorFlow or PyTorch? Python or R? Scikit-learn or XGBoost? GCP or AWS? FastAPI or Streamlit?
Fundamental principles are more important than tools:
- understanding statistics and deep learning is more important than TensorFlow vs PyTorch.
- understanding functional and object-oriented programming is more important than Python or R.
- understanding feature engineering is more important than Scikit-learn vs XGBoost.
- understanding scalable and resilient architectures is more important than GCP or AWS.
- understanding models serving is more important than FastAPI or Streamlit.
Knowing these will allow you to pick up new emerging tools easily.
Stick to fundamentals first.
Join for more: https://t.me/machinelearning_deeplearning
All the best 👍👍
👍18❤2🤡1
Unpopular opinion:
ChatGPT is only as smart as the user; if garbage goes in, garbage comes out.
ChatGPT is only as smart as the user; if garbage goes in, garbage comes out.
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