Artificial Intelligence
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🔰 Machine Learning & Artificial Intelligence Free Resources

🔰 Learn Data Science, Deep Learning, Python with Tensorflow, Keras & many more

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Maths Required for Data Science
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ChatGPT going personal 😂
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😂😂
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Applications of Deep Learning
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Waiting for that HR

Who is looking for DeepSeek expert with 5 years of experience. 😅😂
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AI vs ML vs Neural Network vs Deep Learning
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Data Science Roadmap
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If I were to start my Machine Learning career from scratch (as an engineer), I'd focus here (no specific order):

1. SQL
2. Python
3. ML fundamentals
4. DSA
5. Testing
6. Prob, stats, lin. alg
7. Problem solving

And building as much as possible.
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How to revolutionize Hollywood with AI.

Unlock new possibilities:

1. Voice Cloning

Clone voices of Hollywood icons:

• Legally clone and use voices with permission.
• Recreate iconic voices for new projects.
• Preserve legendary performances for future generations.

2. Custom Voices

Create unique voices for your projects:

• Generate up to 20 seconds of dialogue.
• Select from preset voice options or create your own.

3. Lip Sync Tool

Bring still characters to life:

• Use ElevenLabs's Lip Sync tool.
• Select a face and add a script.
• Generate videos with synchronized lip movements.

AI is reshaping the industry, voice cloning is part of a broader trend.

Filmmakers can now recreate voices of iconic actors.
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Developer: I trained AI. (2015)

AI: Now I train you. (2024) 😂🔥

Free AI Resources: 👇
https://lnkd.in/dyEZQwXv
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Machine Learning Algorithms & Time Complexity
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Should we worry or relax 😂
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Open Source LLMs Part-1
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Machine Learning Roadmap 👆
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🏆AI/ML Engineer

Stage 1 – Python Basics
Stage 2 – Statistics & Probability
Stage 3 – Linear Algebra & Calculus
Stage 4 – Data Preprocessing
Stage 5 – Exploratory Data Analysis (EDA)
Stage 6 – Supervised Learning
Stage 7 – Unsupervised Learning
Stage 8 – Feature Engineering
Stage 9 – Model Evaluation & Tuning
Stage 10 – Deep Learning Basics
Stage 11 – Neural Networks & CNNs
Stage 12 – RNNs & LSTMs
Stage 13 – NLP Fundamentals
Stage 14 – Deployment (Flask, Docker)
Stage 15 – Build projects

ENJOY LEARNING 👍👍
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Emotional damage 😂
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To start with Machine Learning:

1. Learn Python
2. Practice using Google Colab


Take these free courses:

https://t.me/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://t.me/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.✌️✌️
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