Python + AI Entrepreneurship Roadmap
Stage 1 – Identify AI Opportunity (Solve Real-World Problems)
Stage 2 – Build Python/AI Skills (ML, Deep Learning)
Stage 3 – Design AI Product (Prototyping with Flask/TensorFlow)
Stage 4 – Validate AI Model (Data Collection & Training)
Stage 5 – Build MVP (Deploy AI App)
Stage 6 – Secure Funding (Pitch to Investors)
Stage 7 – Marketing & Growth (AI-Driven Campaigns)
Stage 8 – Scale Product (Optimize & Automate)
🏆 – Python AI Entrepreneur
Stage 1 – Identify AI Opportunity (Solve Real-World Problems)
Stage 2 – Build Python/AI Skills (ML, Deep Learning)
Stage 3 – Design AI Product (Prototyping with Flask/TensorFlow)
Stage 4 – Validate AI Model (Data Collection & Training)
Stage 5 – Build MVP (Deploy AI App)
Stage 6 – Secure Funding (Pitch to Investors)
Stage 7 – Marketing & Growth (AI-Driven Campaigns)
Stage 8 – Scale Product (Optimize & Automate)
🏆 – Python AI Entrepreneur
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Uber used RAG and AI agents to build its in-house Text-to-SQL, saving 140,000 hours annually in query writing time. 📈
Here’s how they built the system end-to-end:
The system is called QueryGPT and is built on top of multiple agents each handling a part of the pipeline.
1. First, the Intent Agent interprets user intent and figures out the domain workspace which is relevant to answer the question (e.g., Mobility, Billing, etc).
2. The Table Agent then selects suitable tables using an LLM, which users can also review and adjust.
3. Next, the Column Prune Agent filters out any unnecessary columns from large tables using RAG. This helps the schema fit within token limits.
4. Finally, QueryGPT uses Few-Shot Prompting with selected SQL samples and schemas to generate the query.
QueryGPT reduced query authoring time from 10 minutes to 3, saving over 140,000 hours annually!
Link to the full article
Here’s how they built the system end-to-end:
The system is called QueryGPT and is built on top of multiple agents each handling a part of the pipeline.
1. First, the Intent Agent interprets user intent and figures out the domain workspace which is relevant to answer the question (e.g., Mobility, Billing, etc).
2. The Table Agent then selects suitable tables using an LLM, which users can also review and adjust.
3. Next, the Column Prune Agent filters out any unnecessary columns from large tables using RAG. This helps the schema fit within token limits.
4. Finally, QueryGPT uses Few-Shot Prompting with selected SQL samples and schemas to generate the query.
QueryGPT reduced query authoring time from 10 minutes to 3, saving over 140,000 hours annually!
Link to the full article
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🔴 How to MASTER a programming language using ChatGPT: 📌
1. Can you provide some tips and best practices for writing clean and efficient code in [lang]?
2. What are some commonly asked interview questions about [lang]?
3. What are the advanced topics to learn in [lang]? Explain them to me with code examples.
4. Give me some practice questions along with solutions for [concept] in [lang].
5. What are some common mistakes that people make in [lang]?
6. Can you provide some tips and best practices for writing clean and efficient code in [lang]?
7. How can I optimize the performance of my code in [lang]?
8. What are some coding exercises or mini-projects I can do regularly to reinforce my understanding and application of [lang] concepts?
9. Are there any specific tools or frameworks that are commonly used in [lang]? How can I learn and utilize them effectively?
10. What are the debugging techniques and tools available in [lang] to help troubleshoot and fix code issues?
11. Are there any coding conventions or style guidelines that I should follow when writing code in [lang]?
12. How can I effectively collaborate with other developers in [lang] on a project?
13. What are some common data structures and algorithms that I should be familiar with in [lang]?
How to Create Resume using ChatGPT 👇👇
https://t.me/free4unow_backup/687
Master DSA 👇👇
https://t.me/dsabooks/156
Like for more ❤️
#ai
1. Can you provide some tips and best practices for writing clean and efficient code in [lang]?
2. What are some commonly asked interview questions about [lang]?
3. What are the advanced topics to learn in [lang]? Explain them to me with code examples.
4. Give me some practice questions along with solutions for [concept] in [lang].
5. What are some common mistakes that people make in [lang]?
6. Can you provide some tips and best practices for writing clean and efficient code in [lang]?
7. How can I optimize the performance of my code in [lang]?
8. What are some coding exercises or mini-projects I can do regularly to reinforce my understanding and application of [lang] concepts?
9. Are there any specific tools or frameworks that are commonly used in [lang]? How can I learn and utilize them effectively?
10. What are the debugging techniques and tools available in [lang] to help troubleshoot and fix code issues?
11. Are there any coding conventions or style guidelines that I should follow when writing code in [lang]?
12. How can I effectively collaborate with other developers in [lang] on a project?
13. What are some common data structures and algorithms that I should be familiar with in [lang]?
How to Create Resume using ChatGPT 👇👇
https://t.me/free4unow_backup/687
Master DSA 👇👇
https://t.me/dsabooks/156
Like for more ❤️
#ai
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Powerful Impacts of AI on the Job Market You Need to Know
Artificial Intelligence is not a recent innovation. Even though its current application is highly groundbreaking, It has been transforming jobs for decades. In what ways did AI transform jobs in the early years?
Initially, Artificial Intelligence and machine learning applied automation only to repetitive, manual tasks in industries such as manufacturing and retail.
However, with the increasing maturity of AI, the tasks it performed and took over became progressively more complex, shifting from finance and other healthcare-related sectors where human judgment came into the picture.
....read full article
Artificial Intelligence is not a recent innovation. Even though its current application is highly groundbreaking, It has been transforming jobs for decades. In what ways did AI transform jobs in the early years?
Initially, Artificial Intelligence and machine learning applied automation only to repetitive, manual tasks in industries such as manufacturing and retail.
However, with the increasing maturity of AI, the tasks it performed and took over became progressively more complex, shifting from finance and other healthcare-related sectors where human judgment came into the picture.
....read full article
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You can use ChatGPT to make money online.
Here are 10 prompts by ChatGPT
1. Develop Email Newsletters:
Make interesting email newsletters to keep audience updated and engaged.
Prompt: "I run a local community news website. Can you help me create a weekly email newsletter that highlights key local events, stories, and updates in a compelling way?"
2. Create Online Course Material:
Make detailed and educational online course content.
Prompt: "I'm creating an online course about basic programming for beginners. Can you help me generate a syllabus and detailed lesson plans that cover fundamental concepts in an easy-to-understand manner?"
Read more......
Here are 10 prompts by ChatGPT
1. Develop Email Newsletters:
Make interesting email newsletters to keep audience updated and engaged.
Prompt: "I run a local community news website. Can you help me create a weekly email newsletter that highlights key local events, stories, and updates in a compelling way?"
2. Create Online Course Material:
Make detailed and educational online course content.
Prompt: "I'm creating an online course about basic programming for beginners. Can you help me generate a syllabus and detailed lesson plans that cover fundamental concepts in an easy-to-understand manner?"
Read more......
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Machine Learning with Decision Trees and Random Forest 📝.pdf
1.8 MB
Machine Learning with Decision Trees and Random Forest 📝.pdf
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Data Scientist:
Focuses on data cleaning, preprocessing, and exploratory data analysis (EDA).
Utilizes statistical modeling, hypothesis testing, and machine learning model development.
AI Engineer: - Specializes in model deployment, integration, and optimizing model performance.
Focuses on data cleaning, preprocessing, and exploratory data analysis (EDA).
Utilizes statistical modeling, hypothesis testing, and machine learning model development.
AI Engineer: - Specializes in model deployment, integration, and optimizing model performance.
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Top 5 key developments happening today in the AI and tech space.
1. OpenAI raised $6.6 billion, reaching a valuation of $157 billion, highlighting investor interest in generative AI.
2. Nvidia reported record quarterly revenue of $30 billion, with a 154% increase in data center revenue driven by AI demand.
3. New AI coding assistants like Poolside AI ($626M) and Magic ($465M) are enhancing developer productivity through advanced tools.
4. The White House launched a task force to coordinate policies on AI regulation, focusing on economic and environmental concerns.
5. AI adoption is surging across industries, with significant growth seen in healthcare, finance, and customer service sectors.
1. OpenAI raised $6.6 billion, reaching a valuation of $157 billion, highlighting investor interest in generative AI.
2. Nvidia reported record quarterly revenue of $30 billion, with a 154% increase in data center revenue driven by AI demand.
3. New AI coding assistants like Poolside AI ($626M) and Magic ($465M) are enhancing developer productivity through advanced tools.
4. The White House launched a task force to coordinate policies on AI regulation, focusing on economic and environmental concerns.
5. AI adoption is surging across industries, with significant growth seen in healthcare, finance, and customer service sectors.
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AI as a life saver:
1. ChatGPT - thesis, essay, writing
2. Scite and perplexity - literature review
3. Consesus - latest research paper
4. Gemini - coding and technical
5. Claude AI - Analysis data, comparison data, literature review
1. ChatGPT - thesis, essay, writing
2. Scite and perplexity - literature review
3. Consesus - latest research paper
4. Gemini - coding and technical
5. Claude AI - Analysis data, comparison data, literature review
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Neural Networks and Deep Learning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:
1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.
Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.
Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.
2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.
These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.
Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.
3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.
Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.
4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.
LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.
5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.
Join for more: https://t.me/machinelearning_deeplearning
Neural networks and deep learning are integral parts of artificial intelligence (AI) and machine learning (ML). Here's an overview:
1.Neural Networks: Neural networks are computational models inspired by the human brain's structure and functioning. They consist of interconnected nodes (neurons) organized in layers: input layer, hidden layers, and output layer.
Each neuron receives input, processes it through an activation function, and passes the output to the next layer. Neurons in subsequent layers perform more complex computations based on previous layers' outputs.
Neural networks learn by adjusting weights and biases associated with connections between neurons through a process called training. This is typically done using optimization techniques like gradient descent and backpropagation.
2.Deep Learning : Deep learning is a subset of ML that uses neural networks with multiple layers (hence the term "deep"), allowing them to learn hierarchical representations of data.
These networks can automatically discover patterns, features, and representations in raw data, making them powerful for tasks like image recognition, natural language processing (NLP), speech recognition, and more.
Deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Transformer models have demonstrated exceptional performance in various domains.
3.Applications Computer Vision: Object detection, image classification, facial recognition, etc., leveraging CNNs.
Natural Language Processing (NLP) Language translation, sentiment analysis, chatbots, etc., utilizing RNNs, LSTMs, and Transformers.
Speech Recognition: Speech-to-text systems using deep neural networks.
4.Challenges and Advancements: Training deep neural networks often requires large amounts of data and computational resources. Techniques like transfer learning, regularization, and optimization algorithms aim to address these challenges.
LAdvancements in hardware (GPUs, TPUs), algorithms (improved architectures like GANs - Generative Adversarial Networks), and techniques (attention mechanisms) have significantly contributed to the success of deep learning.
5. Frameworks and Libraries: There are various open-source libraries and frameworks (TensorFlow, PyTorch, Keras, etc.) that provide tools and APIs for building, training, and deploying neural networks and deep learning models.
Join for more: https://t.me/machinelearning_deeplearning
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How to learn Artificial Intelligence from scratch
👇👇
https://medium.com/@data_analyst/how-to-learn-artificial-intelligence-from-scratch-d34ea18f70c1?sk=b139911f85a0d0c0ecd448a7fffe4c9d
👇👇
https://medium.com/@data_analyst/how-to-learn-artificial-intelligence-from-scratch-d34ea18f70c1?sk=b139911f85a0d0c0ecd448a7fffe4c9d
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5 Algorithms you must know as a data scientist 👩💻 🧑💻
1. Dimensionality Reduction
- PCA, t-SNE, LDA
2. Regression models
- Linesr regression, Kernel-based regression models, Lasso Regression, Ridge regression, Elastic-net regression
3. Classification models
- Binary classification- Logistic regression, SVM
- Multiclass classification- One versus one, one versus many
- Multilabel classification
4. Clustering models
- K Means clustering, Hierarchical clustering, DBSCAN, BIRCH models
5. Decision tree based models
- CART model, ensemble models(XGBoost, LightGBM, CatBoost)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/free4unow_backup
Like if you need similar content 😄👍
1. Dimensionality Reduction
- PCA, t-SNE, LDA
2. Regression models
- Linesr regression, Kernel-based regression models, Lasso Regression, Ridge regression, Elastic-net regression
3. Classification models
- Binary classification- Logistic regression, SVM
- Multiclass classification- One versus one, one versus many
- Multilabel classification
4. Clustering models
- K Means clustering, Hierarchical clustering, DBSCAN, BIRCH models
5. Decision tree based models
- CART model, ensemble models(XGBoost, LightGBM, CatBoost)
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
Credits: https://t.me/free4unow_backup
Like if you need similar content 😄👍
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Forwarded from Crypto Trends
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Introducing ChatGPT search
ChatGPT can now search the web in a much better way than before. You can get fast, timely answers with links to relevant web sources, which you would have previously needed to go to a search engine for. This blends the benefits of a natural language interface with the value of up-to-date sports scores, news, stock quotes, and more.
ChatGPT will choose to search the web based on what you ask, or you can manually choose to search by clicking the web search icon.
On mobile, the option will replace the existing “Refine my draft” shortcut. Instead of swiping to see options to polish.
Search will be available at chatgpt.com(opens in a new window), as well as on our desktop and mobile apps. All ChatGPT Plus and Team users, as well as SearchGPT waitlist users, will have access today. Enterprise and Edu users will get access in the next few weeks. We’ll roll out to all Free users over the coming months.
ChatGPT can now search the web in a much better way than before. You can get fast, timely answers with links to relevant web sources, which you would have previously needed to go to a search engine for. This blends the benefits of a natural language interface with the value of up-to-date sports scores, news, stock quotes, and more.
ChatGPT will choose to search the web based on what you ask, or you can manually choose to search by clicking the web search icon.
On mobile, the option will replace the existing “Refine my draft” shortcut. Instead of swiping to see options to polish.
Search will be available at chatgpt.com(opens in a new window), as well as on our desktop and mobile apps. All ChatGPT Plus and Team users, as well as SearchGPT waitlist users, will have access today. Enterprise and Edu users will get access in the next few weeks. We’ll roll out to all Free users over the coming months.
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