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.

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ChatGPT Prompt to learn any skill
👇👇
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)

Please take it step by step!!

Me 😂

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 👍👍

Unpopular opinion:

ChatGPT is only as smart as the user; if garbage goes in, garbage comes out.

Some useful AI tools in 2024

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Complete guide to train chatgpt model