To start with Machine Learning:
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.iss.one/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.iss.one/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 ๐ 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.โ๏ธโ๏ธ
1. Learn Python
2. Practice using Google Colab
Take these free courses:
https://t.iss.one/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.iss.one/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 ๐ 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.โ๏ธโ๏ธ
๐11๐2โค1
Let's go from โWhat can AI do?โ to โHow can AI deliver scalable value?โ
Here are three things you should be watching out for:
๐ญ. ๐๐โ๐ ๐ ๐ฎ๐ฟ๐ธ๐ฒ๐ ๐๐ผ๐ฟ๐ฟ๐ฒ๐ฐ๐๐ถ๐ผ๐ป: Weโve seen this beforeโmarkets overhype technology, but only those who deliver real business outcomes thrive. This means a combination of strong product differentiation, technical excellence, a competitive business model, and a sustainable growth strategy.
๐ฎ. ๐ง๐ต๐ฒ ๐๐ฎ๐๐-๐ ๐ถ๐น๐ฒ ๐ฃ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ: The challenge isnโt building a model, itโs deploying it at scale. To succeed, startups/enterprises must ensure robust data pipelines, optimize for real-world latency, and design scalable infrastructure.
๐ฏ. ๐๐๐ฝ๐ฒ๐ฟ๐ฝ๐ฒ๐ฟ๐๐ผ๐ป๐ฎ๐น๐ถ๐๐ฎ๐๐ถ๐ผ๐ป & ๐ฅ๐ฒ๐๐ฝ๐ผ๐ป๐๐ถ๐ฏ๐น๐ฒ ๐๐: Whether in e-commerce recommendations or AI-driven healthcare, hyperpersonalization brings immense potential but also serious challenges around privacy, fairness, and transparency. Companies building B2B or B2C solutions must focus on building toolkits and guardrails that help them track how data is being collected, what use cases they are used for, and how the output is being processed to various target outcomes.
Here are three things you should be watching out for:
๐ญ. ๐๐โ๐ ๐ ๐ฎ๐ฟ๐ธ๐ฒ๐ ๐๐ผ๐ฟ๐ฟ๐ฒ๐ฐ๐๐ถ๐ผ๐ป: Weโve seen this beforeโmarkets overhype technology, but only those who deliver real business outcomes thrive. This means a combination of strong product differentiation, technical excellence, a competitive business model, and a sustainable growth strategy.
๐ฎ. ๐ง๐ต๐ฒ ๐๐ฎ๐๐-๐ ๐ถ๐น๐ฒ ๐ฃ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ: The challenge isnโt building a model, itโs deploying it at scale. To succeed, startups/enterprises must ensure robust data pipelines, optimize for real-world latency, and design scalable infrastructure.
๐ฏ. ๐๐๐ฝ๐ฒ๐ฟ๐ฝ๐ฒ๐ฟ๐๐ผ๐ป๐ฎ๐น๐ถ๐๐ฎ๐๐ถ๐ผ๐ป & ๐ฅ๐ฒ๐๐ฝ๐ผ๐ป๐๐ถ๐ฏ๐น๐ฒ ๐๐: Whether in e-commerce recommendations or AI-driven healthcare, hyperpersonalization brings immense potential but also serious challenges around privacy, fairness, and transparency. Companies building B2B or B2C solutions must focus on building toolkits and guardrails that help them track how data is being collected, what use cases they are used for, and how the output is being processed to various target outcomes.
๐5โค2
How do you start AI and ML ?
Where do you go to learn these skills? What courses are the best?
Thereโs no best answer๐ฅบ. Everyoneโs path will be different. Some people learn better with books, others learn better through videos.
Whatโs more important than how you start is why you start.
Start with why.
Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.
Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, youโve got something to turn to. Something to remind you why you started.
Got a why? Good. Time for some hard skills.
I can only recommend what Iโve tried every week new course lauch better than others its difficult to recommend any course
You can completed courses from (in order):
Treehouse / youtube( free) - Introduction to Python
Udacity - Deep Learning & AI Nanodegree
fast.ai - Part 1and Part 2
Theyโre all world class. Iโm a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If youโre an absolute beginner, start with some introductory Python courses and when youโre a bit more confident, move into data science, machine learning and AI.
Join for more: https://t.iss.one/machinelearning_deeplearning
Like for more โค๏ธ
All the best ๐๐
Where do you go to learn these skills? What courses are the best?
Thereโs no best answer๐ฅบ. Everyoneโs path will be different. Some people learn better with books, others learn better through videos.
Whatโs more important than how you start is why you start.
Start with why.
Why do you want to learn these skills?
Do you want to make money?
Do you want to build things?
Do you want to make a difference?
Again, no right reason. All are valid in their own way.
Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, youโve got something to turn to. Something to remind you why you started.
Got a why? Good. Time for some hard skills.
I can only recommend what Iโve tried every week new course lauch better than others its difficult to recommend any course
You can completed courses from (in order):
Treehouse / youtube( free) - Introduction to Python
Udacity - Deep Learning & AI Nanodegree
fast.ai - Part 1and Part 2
Theyโre all world class. Iโm a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.
If youโre an absolute beginner, start with some introductory Python courses and when youโre a bit more confident, move into data science, machine learning and AI.
Join for more: https://t.iss.one/machinelearning_deeplearning
Like for more โค๏ธ
All the best ๐๐
๐17โค4๐2
Prompt Engineering in itself does not warrant a separate job.
Most of the things you see online related to prompts (especially things said by people selling courses) is mostly just writing some crazy text to get ChatGPT to do some specific task. Most of these prompts are just been found by serendipity and are never used in any company. They may be fine for personal usage but no company is going to pay a person to try out prompts ๐ . Also a lot of these prompts don't work for any other LLMs apart from ChatGPT.
You have mostly two types of jobs in this field nowadays, one is more focused on training, optimizing and deploying models. For this knowing the architecture of LLMs is critical and a strong background in PyTorch, Jax and HuggingFace is required. Other engineering skills like System Design and building APIs is also important for some jobs. This is the work you would find in companies like OpenAI, Anthropic, Cohere etc.
The other is jobs where you build applications using LLMs (this comprises of majority of the companies that do LLM related work nowadays, both product based and service based). Roles in these companies are called Applied NLP Engineer or ML Engineer, sometimes even Data Scientist roles. For this you mostly need to understand how LLMs can be used for different applications as well as know the necessary frameworks for building LLM applications (Langchain/LlamaIndex/Haystack). Apart from this, you need to know LLM specific techniques for applications like Vector Search, RAG, Structured Text Generation. This is also where some part of your role involves prompt engineering. Its not the most crucial bit, but it is important in some cases, especially when you are limited in the other techniques.
Most of the things you see online related to prompts (especially things said by people selling courses) is mostly just writing some crazy text to get ChatGPT to do some specific task. Most of these prompts are just been found by serendipity and are never used in any company. They may be fine for personal usage but no company is going to pay a person to try out prompts ๐ . Also a lot of these prompts don't work for any other LLMs apart from ChatGPT.
You have mostly two types of jobs in this field nowadays, one is more focused on training, optimizing and deploying models. For this knowing the architecture of LLMs is critical and a strong background in PyTorch, Jax and HuggingFace is required. Other engineering skills like System Design and building APIs is also important for some jobs. This is the work you would find in companies like OpenAI, Anthropic, Cohere etc.
The other is jobs where you build applications using LLMs (this comprises of majority of the companies that do LLM related work nowadays, both product based and service based). Roles in these companies are called Applied NLP Engineer or ML Engineer, sometimes even Data Scientist roles. For this you mostly need to understand how LLMs can be used for different applications as well as know the necessary frameworks for building LLM applications (Langchain/LlamaIndex/Haystack). Apart from this, you need to know LLM specific techniques for applications like Vector Search, RAG, Structured Text Generation. This is also where some part of your role involves prompt engineering. Its not the most crucial bit, but it is important in some cases, especially when you are limited in the other techniques.
๐13โค5
For those who feel like they're not learning much and feeling demotivated. You should definitely read these lines from one of the book by Andrew Ng ๐
No one can cram everything they need to know over a weekend or even a month. Everyone I
know whoโs great at machine learning is a lifelong learner. Given how quickly our field is changing,
thereโs little choice but to keep learning if you want to keep up.
How can you maintain a steady pace of learning for years? If you can cultivate the habit of
learning a little bit every week, you can make significant progress with what feels like less effort.
Everyday it gets easier but you need to do it everyday โค๏ธ
No one can cram everything they need to know over a weekend or even a month. Everyone I
know whoโs great at machine learning is a lifelong learner. Given how quickly our field is changing,
thereโs little choice but to keep learning if you want to keep up.
How can you maintain a steady pace of learning for years? If you can cultivate the habit of
learning a little bit every week, you can make significant progress with what feels like less effort.
Everyday it gets easier but you need to do it everyday โค๏ธ
๐18โค12๐ฅ2๐ฅฐ1
5 Must-Learn Programming Languages for AI Careers in India
๐๐
https://datasimplifier.com/programming-languages-for-ai-careers/
๐๐
https://datasimplifier.com/programming-languages-for-ai-careers/
๐7โค2๐ฅ2
Software Engineers vs AI Engineers: ๐
Software engineers are often shocked when they learn of AI engineers' salaries. There are two reasons for this surprise.
1. The total compensation for AI engineers is jaw-dropping. You can check it out at AIPaygrad.es, which has manually verified data for AI engineers. The median overall compensation for a โNoviceโ is $328,350/year.
2. AI engineers are no smarter than software engineers. You figure this out only after a friend or acquaintance upskills and finds a lucrative AI job.
The biggest difference between Software and AI engineers is the demand for such roles. One role is declining, and the other is reaching stratospheric heights.
Here is an example.
Just last week, we saw an implosion of OpenAI after Sam Altman was unceremoniously removed from his CEO position. About 95% of their AI Engineers threatened to quit in protest. Rumor had it that these 700 engineers had an open job offer from Microsoft. ๐
Contrast this with the events a few months back. Microsoft laid off 10,000 Software Engineers while setting aside $10B to invest in OpenAI. They cut these jobs despite making stunning profits in 2023.
In conclusion, these events underline a significant shift in the tech industry. For software engineers, it's a call to adapt and possibly upskill in AI, while companies need to balance AI investments with nurturing their current talent. The future of tech hinges on flexibility and continuous learning for everyone involved."
Software engineers are often shocked when they learn of AI engineers' salaries. There are two reasons for this surprise.
1. The total compensation for AI engineers is jaw-dropping. You can check it out at AIPaygrad.es, which has manually verified data for AI engineers. The median overall compensation for a โNoviceโ is $328,350/year.
2. AI engineers are no smarter than software engineers. You figure this out only after a friend or acquaintance upskills and finds a lucrative AI job.
The biggest difference between Software and AI engineers is the demand for such roles. One role is declining, and the other is reaching stratospheric heights.
Here is an example.
Just last week, we saw an implosion of OpenAI after Sam Altman was unceremoniously removed from his CEO position. About 95% of their AI Engineers threatened to quit in protest. Rumor had it that these 700 engineers had an open job offer from Microsoft. ๐
Contrast this with the events a few months back. Microsoft laid off 10,000 Software Engineers while setting aside $10B to invest in OpenAI. They cut these jobs despite making stunning profits in 2023.
In conclusion, these events underline a significant shift in the tech industry. For software engineers, it's a call to adapt and possibly upskill in AI, while companies need to balance AI investments with nurturing their current talent. The future of tech hinges on flexibility and continuous learning for everyone involved."
๐15โค5๐1
๐จ๐๐จ๐จ๐ ๐ฅ๐ ๐๐๐ฒ๐ฌ $๐.๐ ๐๐ข๐ฅ๐ฅ๐ข๐จ๐ง ๐ญ๐จ ๐๐ซ๐ข๐ง๐ ๐๐๐๐ค ๐๐ ๐๐ข๐จ๐ง๐๐๐ซ ๐๐จ๐๐ฆ ๐๐ก๐๐ณ๐๐๐ซ ๐๐จ๐ซ ๐๐๐ฃ๐จ๐ซ ๐๐ ๐๐ซ๐จ๐ฃ๐๐๐ญ
Google has reportedly spent $2.7 billion to rehire AI expert Noam Shazeer, who left the company in 2021 after a disagreement. Shazeer, co-founder of Character.AI, will now help lead Googleโs next major AI initiative, Gemini. This strategic move also includes acquiring Character.AIโs technology, a top AI startup with a $1 billion valuation.
Shazeer had left Google after clashing over his AI chatbot, Meena, which he believed could replace Google Search. His return, along with the acquisition, signals Googleโs commitment to staying at the forefront of AI innovation.
Google has reportedly spent $2.7 billion to rehire AI expert Noam Shazeer, who left the company in 2021 after a disagreement. Shazeer, co-founder of Character.AI, will now help lead Googleโs next major AI initiative, Gemini. This strategic move also includes acquiring Character.AIโs technology, a top AI startup with a $1 billion valuation.
Shazeer had left Google after clashing over his AI chatbot, Meena, which he believed could replace Google Search. His return, along with the acquisition, signals Googleโs commitment to staying at the forefront of AI innovation.
๐8๐ฅ5โค2
Future Trends in Artificial Intelligence ๐๐
1. AI in healthcare: With the increasing demand for personalized medicine and precision healthcare, AI is expected to play a crucial role in analyzing large amounts of medical data to diagnose diseases, develop treatment plans, and predict patient outcomes.
2. AI in finance: AI-powered solutions are expected to revolutionize the financial industry by improving fraud detection, risk assessment, and customer service. Robo-advisors and algorithmic trading are also likely to become more prevalent.
3. AI in autonomous vehicles: The development of self-driving cars and other autonomous vehicles will rely heavily on AI technologies such as computer vision, natural language processing, and machine learning to navigate and make decisions in real-time.
4. AI in manufacturing: The use of AI and robotics in manufacturing processes is expected to increase efficiency, reduce errors, and enable the automation of complex tasks.
5. AI in customer service: Chatbots and virtual assistants powered by AI are anticipated to become more sophisticated, providing personalized and efficient customer support across various industries.
6. AI in agriculture: AI technologies can be used to optimize crop yields, monitor plant health, and automate farming processes, contributing to sustainable and efficient agricultural practices.
7. AI in cybersecurity: As cyber threats continue to evolve, AI-powered solutions will be crucial for detecting and responding to security breaches in real-time, as well as predicting and preventing future attacks.
Like for more โค๏ธ
Artificial Intelligence
1. AI in healthcare: With the increasing demand for personalized medicine and precision healthcare, AI is expected to play a crucial role in analyzing large amounts of medical data to diagnose diseases, develop treatment plans, and predict patient outcomes.
2. AI in finance: AI-powered solutions are expected to revolutionize the financial industry by improving fraud detection, risk assessment, and customer service. Robo-advisors and algorithmic trading are also likely to become more prevalent.
3. AI in autonomous vehicles: The development of self-driving cars and other autonomous vehicles will rely heavily on AI technologies such as computer vision, natural language processing, and machine learning to navigate and make decisions in real-time.
4. AI in manufacturing: The use of AI and robotics in manufacturing processes is expected to increase efficiency, reduce errors, and enable the automation of complex tasks.
5. AI in customer service: Chatbots and virtual assistants powered by AI are anticipated to become more sophisticated, providing personalized and efficient customer support across various industries.
6. AI in agriculture: AI technologies can be used to optimize crop yields, monitor plant health, and automate farming processes, contributing to sustainable and efficient agricultural practices.
7. AI in cybersecurity: As cyber threats continue to evolve, AI-powered solutions will be crucial for detecting and responding to security breaches in real-time, as well as predicting and preventing future attacks.
Like for more โค๏ธ
Artificial Intelligence
โค15๐4