To start with Machine Learning:
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://news.1rj.ru/str/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://news.1rj.ru/str/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.✌️✌️
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://news.1rj.ru/str/datasciencefun/290
If you need a bit more time before diving deeper, finish the Kaggle tutorials.
At this point, you are ready to finish your first project: The Titanic Challenge on Kaggle.
If Math is not your strong suit, don't worry. I don't recommend you spend too much time learning Math before writing code. Instead, learn the concepts on-demand: Find what you need when needed.
From here, take the Machine Learning specialization in Coursera. It's more advanced, and it will stretch you out a bit.
The top universities worldwide have published their Machine Learning and Deep Learning classes online. Here are some of them:
https://news.1rj.ru/str/datasciencefree/259
Many different books will help you. The attached image will give you an idea of my favorite ones.
Finally, keep these three ideas in mind:
1. Start by working on solved problems so you can find help whenever you get stuck.
2. ChatGPT will help you make progress. Use it to summarize complex concepts and generate questions you can answer to practice.
3. Find a community on LinkedIn or 𝕏 and share your work. Ask questions, and help others.
During this time, you'll deal with a lot. Sometimes, you will feel it's impossible to keep up with everything happening, and you'll be right.
Here is the good news:
Most people understand a tiny fraction of the world of Machine Learning. You don't need more to build a fantastic career in space.
Focus on finding your path, and Write. More. Code.
That's how you win.✌️✌️
👍4
Entry-level AI/ML Jobs nowadays
- 3+ years of deploying GPT models without touching the keyboard.
- 5+ years of experience using TensorFlow, scikit-learn, etc.
- 4+ years of Python/Java experience.
- Graduate from a reputable university (TOP TIER UNIVERSITY) with a minimum GPA of 3.99/4.00.
- Expertise in Database System Management, Frontend Development, and System Integration.
- Proficiency in Python and one or more programming languages such as Java, Javanoscript, or GoLang is a plus
- 4+ years with training, fine-tuning, and deploying LLMs (e.g., GPT, LLAMA, mistral)
• Expertise in using Al development frameworks such as TensorFlow, PyTorch, LangChain, Hugging Face Transformers
- Must be a certified Kubernetes administrator.
- Ability to write production-ready code in less than 24 hours.
- Proven track record of solving world hunger with AI.
- Must have telepathic debugging skills.
- Willing to work weekends, holidays, and during full moons.
Oh, and the most important requirement: must be resilient in handling sudden revisions from the boss
- 3+ years of deploying GPT models without touching the keyboard.
- 5+ years of experience using TensorFlow, scikit-learn, etc.
- 4+ years of Python/Java experience.
- Graduate from a reputable university (TOP TIER UNIVERSITY) with a minimum GPA of 3.99/4.00.
- Expertise in Database System Management, Frontend Development, and System Integration.
- Proficiency in Python and one or more programming languages such as Java, Javanoscript, or GoLang is a plus
- 4+ years with training, fine-tuning, and deploying LLMs (e.g., GPT, LLAMA, mistral)
• Expertise in using Al development frameworks such as TensorFlow, PyTorch, LangChain, Hugging Face Transformers
- Must be a certified Kubernetes administrator.
- Ability to write production-ready code in less than 24 hours.
- Proven track record of solving world hunger with AI.
- Must have telepathic debugging skills.
- Willing to work weekends, holidays, and during full moons.
Oh, and the most important requirement: must be resilient in handling sudden revisions from the boss
👍7
Forwarded from SQL Programming Resources
𝗪𝗲𝗯 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
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Want to master web development? These free certification courses will help you build real-world full-stack skills:
✅ Web Design 🎨
✅ JavaScript ⚡
✅ Front-End Libraries 📚
✅ Back-End & APIs 🌐
✅ Databases 💾
💡 Start learning today and build your career for FREE! 🚀
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📈 Predictive Modeling for Future Stock Prices in Python: A Step-by-Step Guide
The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
The process of building a stock price prediction model using Python.
1. Import required modules
2. Obtaining historical data on stock prices
3. Selection of features.
4. Definition of features and target variable
5. Preparing data for training
6. Separation of data into training and test sets
7. Building and training the model
8. Making forecasts
9. Trading Strategy Testing
👍1