Artificial Intelligence for Learning
Donald Clark, 2024

Real-World Natural Language Processing
Masato Hagiwara, 2021

Step 7: Generative Models

Variational Autoencoders (VAEs):

Encoder and decoder networks.
Latent space representation.
Reparameterization trick.
KL divergence loss.
Applications (data generation, anomaly detection).

Generative Adversarial Networks (GANs):

Generator and discriminator networks.
Adversarial training.
Loss functions (minimax, Wasserstein).
DCGANs (Deep Convolutional GANs).
Applications (image generation, style transfer).

Artificial Intelligence

Step 8: Practical Applications and Projects

Identifying Real-World Problems:
Planning and Outlining Projects:
Choosing the Right Algorithm:
Overcoming Overfitting:
Building a Strong Portfolio

Artificial Intelligence

People: AI will rule the world

Meanwhile AI

Step 9: Career and Freelance Tips (1/2)

Searching for Internships and Jobs:

Using job portals and company websites.
Leveraging university career centers.
Joining professional associations.
Networking and referrals.
Crafting effective resumes and cover letters.

(2/2)

Preparing for Interviews:
Technical preparation (coding practice, concepts).
Behavioral preparation (STAR method).
Researching companies.
Mock interviews.
Useful apps for interview preparation.

Working with Freelance:
Finding opportunities on freelance platforms.
Building a strong profile and portfolio.
Managing projects and client communication.
Time management and payment methods.

Artificial Intelligence

Hope this helps you ☺️

Save significant time every day with these ChatGPT's 7 prompts:

1. Make Hard Topics Easier to Understand:

Prompt:
Divide the (topic) into smaller, simpler pieces.
Use comparisons and examples from everyday life to make the idea easier to grasp and more relevant.

More here 👇👇
ChatGPT Prompts

For working professionals willing to pivot their careers to AI:

Here are the steps you can take right now:

1. Learn the basics of AI
==================

You need to understand the differences among various AI jargons (e.g., what is the difference between statistical ML vs. deep learning? What exactly is an LLM?) and when to use which to solve a given business problem. Many fast-paced courses can teach you all of this without having to learn coding. (Shameless plug: I have a course that I will add in the comments section below)

2. Build an AI project in your current work
==============================

Find a problem statement in your current work that can be solved using AI and will deliver some value. Work on this during your extra hours, then showcase it to your management to get official approval to make it a full-fledged project.

3. Collaborate with the AI team in your company for inner sourcing
================================================

Many companies have the concept of inner sourcing where, say, an AI team is too busy and has a list of tasks they have opened on their GitHub repository that others can work on. Use this as an opportunity to do some real AI work and build rapport with the AI team.

4. Attend AI conferences
==================

By attending AI conferences, you will not only learn but also build a network with AI professionals who will help you in your AI career journey.

5. Attend an AI bootcamp at a university or online learning company
=================================================

Artificial Intelligence

👉Telegram Link: https://t.me/addlist/ID95piZJZa0wYzk5

Like for more ❤️

All the best 👍👍

If you want to get a job as a machine learning engineer, don’t start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc.

Yes, you might hear a lot about them or some other trending technology of the year...but guess what!

Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.

Instead, here are basic skills that will get you further than mastering any framework:


𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.

You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability

𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚 𝐚𝐧𝐝 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬 - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.

𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.

You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/

𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.

𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.

𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.

You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai

I love frameworks and libraries, and they can make anyone's job easier.

But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

All the best 👍👍

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 👍👍

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👍👍

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 👍👍

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

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 😄

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 👍👍