📈 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
❤4
Use Chat GPT to prepare for your next Interview
This could be the most helpful thing for people aspiring for new jobs.
A few prompts that can help you here are:
💡Prompt 1: Here is a Job denoscription of a job I am looking to apply for. Can you tell me what skills and questions should I prepare for? {Paste JD}
💡Prompt 2: Here is my resume. Can you tell me what optimization I can do to make it more likely to get selected for this interview? {Paste Resume in text}
💡Prompt 3: Act as an Interviewer for the role of a {product manager} at {Company}. Ask me 5 questions one by one, wait for my response, and then tell me how I did. You should give feedback in the following format: What was good, where are the gaps, and how to address the gaps?
💡Prompt 4: I am interviewing for this job given in the JD. Can you help me understand the company, its role, its products, main competitors, and challenges for the company?
💡Prompt 5: What are the few questions I should ask at the end of the interview which can help me learn about the culture of the company?
Free book to master ChatGPT: https://news.1rj.ru/str/InterviewBooks/166
ENJOY LEARNING 👍👍
This could be the most helpful thing for people aspiring for new jobs.
A few prompts that can help you here are:
💡Prompt 1: Here is a Job denoscription of a job I am looking to apply for. Can you tell me what skills and questions should I prepare for? {Paste JD}
💡Prompt 2: Here is my resume. Can you tell me what optimization I can do to make it more likely to get selected for this interview? {Paste Resume in text}
💡Prompt 3: Act as an Interviewer for the role of a {product manager} at {Company}. Ask me 5 questions one by one, wait for my response, and then tell me how I did. You should give feedback in the following format: What was good, where are the gaps, and how to address the gaps?
💡Prompt 4: I am interviewing for this job given in the JD. Can you help me understand the company, its role, its products, main competitors, and challenges for the company?
💡Prompt 5: What are the few questions I should ask at the end of the interview which can help me learn about the culture of the company?
Free book to master ChatGPT: https://news.1rj.ru/str/InterviewBooks/166
ENJOY LEARNING 👍👍
❤3💩1
Which AI is better — with a wrapper or without?
I liked a slide from the recent presentation Sequoia Capital AI Ascent 2025.
Big vendors after the launch of ChatGPT insisted that niche and industrial startups consuming AGI intelligence are unpromising.
They disdainfully called them AI wrappers, believing that they have no protection against competitors.
A couple of years later, we observe record growth precisely in such companies as Cursor ($300 million ARR), Loveable, Windsurf (bought by OpenAI for $3 billion), and thousands of new industrial startups — in finance, insurance, e-commerce, legal, accounting, healthcare, and other sectors.
Meanwhile, AI tokens are becoming the fastest depreciating technology and currency in history.
According to Sam Altman himself, over time the cost of AI will equal the cost of energy.
Giants have started giving access to their most powerful AI models to seize leadership in the new technological era.
I have already written about a unique market moment: when, being an expert in any subject area, you combine your knowledge with the growing capabilities of AI — and get a tool that solves real human problems for which clients are ready to pay immediately.
Now these very AI wrappers have turned into serious businesses.
The obvious challenge for entrepreneurs is that AI skills and specialists are increasing in value much faster than other segments of the IT market.
Startups and mature companies with subject matter experts already face difficulties attracting strong AI engineers, but at the same time, according to the results of the first internal hackathons, I see a rapid increase in the number of those who caught the trend and felt in AI a breath of fresh air in the enterprise world.
In the coming years, young founders and enthusiasts who live by technology, see trends ahead, and tirelessly experiment by creating new products will win rapidly.
Time to act!
I liked a slide from the recent presentation Sequoia Capital AI Ascent 2025.
Big vendors after the launch of ChatGPT insisted that niche and industrial startups consuming AGI intelligence are unpromising.
They disdainfully called them AI wrappers, believing that they have no protection against competitors.
A couple of years later, we observe record growth precisely in such companies as Cursor ($300 million ARR), Loveable, Windsurf (bought by OpenAI for $3 billion), and thousands of new industrial startups — in finance, insurance, e-commerce, legal, accounting, healthcare, and other sectors.
Meanwhile, AI tokens are becoming the fastest depreciating technology and currency in history.
According to Sam Altman himself, over time the cost of AI will equal the cost of energy.
Giants have started giving access to their most powerful AI models to seize leadership in the new technological era.
I have already written about a unique market moment: when, being an expert in any subject area, you combine your knowledge with the growing capabilities of AI — and get a tool that solves real human problems for which clients are ready to pay immediately.
Now these very AI wrappers have turned into serious businesses.
The obvious challenge for entrepreneurs is that AI skills and specialists are increasing in value much faster than other segments of the IT market.
Startups and mature companies with subject matter experts already face difficulties attracting strong AI engineers, but at the same time, according to the results of the first internal hackathons, I see a rapid increase in the number of those who caught the trend and felt in AI a breath of fresh air in the enterprise world.
In the coming years, young founders and enthusiasts who live by technology, see trends ahead, and tirelessly experiment by creating new products will win rapidly.
Time to act!
❤4👍1
𝗧𝗼𝗽 𝗖𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗛𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝘁𝘀😍
𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸𝘀:-👇
S&P Global :- https://pdlink.in/3ZddwVz
IBM :- https://pdlink.in/4kDmMKE
TVS Credit :- https://pdlink.in/4mI0JVc
Sutherland :- https://pdlink.in/4mGYBgg
Other Jobs :- https://pdlink.in/44qEIDu
Apply before the link expires 💫
𝗔𝗽𝗽𝗹𝘆 𝗟𝗶𝗻𝗸𝘀:-👇
S&P Global :- https://pdlink.in/3ZddwVz
IBM :- https://pdlink.in/4kDmMKE
TVS Credit :- https://pdlink.in/4mI0JVc
Sutherland :- https://pdlink.in/4mGYBgg
Other Jobs :- https://pdlink.in/44qEIDu
Apply before the link expires 💫
Artificial Intelligence (AI) is the simulation of human intelligence in machines that are designed to think, learn, and make decisions. From virtual assistants to self-driving cars, AI is transforming how we interact with technology.
Hers is the brief A-Z overview of the terms used in Artificial Intelligence World
A - Algorithm: A set of rules or instructions that an AI system follows to solve problems or make decisions.
B - Bias: Prejudice in AI systems due to skewed training data, leading to unfair outcomes.
C - Chatbot: AI software that can hold conversations with users via text or voice.
D - Deep Learning: A type of machine learning using layered neural networks to analyze data and make decisions.
E - Expert System: An AI that replicates the decision-making ability of a human expert in a specific domain.
F - Fine-Tuning: The process of refining a pre-trained model on a specific task or dataset.
G - Generative AI: AI that can create new content like text, images, audio, or code.
H - Heuristic: A rule-of-thumb or shortcut used by AI to make decisions efficiently.
I - Image Recognition: The ability of AI to detect and classify objects or features in an image.
J - Jupyter Notebook: A tool widely used in AI for interactive coding, data visualization, and documentation.
K - Knowledge Representation: How AI systems store, organize, and use information for reasoning.
L - LLM (Large Language Model): An AI trained on large text datasets to understand and generate human language (e.g., GPT-4).
M - Machine Learning: A branch of AI where systems learn from data instead of being explicitly programmed.
N - NLP (Natural Language Processing): AI's ability to understand, interpret, and generate human language.
O - Overfitting: When a model performs well on training data but poorly on unseen data due to memorizing instead of generalizing.
P - Prompt Engineering: Crafting effective inputs to steer generative AI toward desired responses.
Q - Q-Learning: A reinforcement learning algorithm that helps agents learn the best actions to take.
R - Reinforcement Learning: A type of learning where AI agents learn by interacting with environments and receiving rewards.
S - Supervised Learning: Machine learning where models are trained on labeled datasets.
T - Transformer: A neural network architecture powering models like GPT and BERT, crucial in NLP tasks.
U - Unsupervised Learning: A method where AI finds patterns in data without labeled outcomes.
V - Vision (Computer Vision): The field of AI that enables machines to interpret and process visual data.
W - Weak AI: AI designed to handle narrow tasks without consciousness or general intelligence.
X - Explainable AI (XAI): Techniques that make AI decision-making transparent and understandable to humans.
Y - YOLO (You Only Look Once): A popular real-time object detection algorithm in computer vision.
Z - Zero-shot Learning: The ability of AI to perform tasks it hasn’t been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
Hers is the brief A-Z overview of the terms used in Artificial Intelligence World
A - Algorithm: A set of rules or instructions that an AI system follows to solve problems or make decisions.
B - Bias: Prejudice in AI systems due to skewed training data, leading to unfair outcomes.
C - Chatbot: AI software that can hold conversations with users via text or voice.
D - Deep Learning: A type of machine learning using layered neural networks to analyze data and make decisions.
E - Expert System: An AI that replicates the decision-making ability of a human expert in a specific domain.
F - Fine-Tuning: The process of refining a pre-trained model on a specific task or dataset.
G - Generative AI: AI that can create new content like text, images, audio, or code.
H - Heuristic: A rule-of-thumb or shortcut used by AI to make decisions efficiently.
I - Image Recognition: The ability of AI to detect and classify objects or features in an image.
J - Jupyter Notebook: A tool widely used in AI for interactive coding, data visualization, and documentation.
K - Knowledge Representation: How AI systems store, organize, and use information for reasoning.
L - LLM (Large Language Model): An AI trained on large text datasets to understand and generate human language (e.g., GPT-4).
M - Machine Learning: A branch of AI where systems learn from data instead of being explicitly programmed.
N - NLP (Natural Language Processing): AI's ability to understand, interpret, and generate human language.
O - Overfitting: When a model performs well on training data but poorly on unseen data due to memorizing instead of generalizing.
P - Prompt Engineering: Crafting effective inputs to steer generative AI toward desired responses.
Q - Q-Learning: A reinforcement learning algorithm that helps agents learn the best actions to take.
R - Reinforcement Learning: A type of learning where AI agents learn by interacting with environments and receiving rewards.
S - Supervised Learning: Machine learning where models are trained on labeled datasets.
T - Transformer: A neural network architecture powering models like GPT and BERT, crucial in NLP tasks.
U - Unsupervised Learning: A method where AI finds patterns in data without labeled outcomes.
V - Vision (Computer Vision): The field of AI that enables machines to interpret and process visual data.
W - Weak AI: AI designed to handle narrow tasks without consciousness or general intelligence.
X - Explainable AI (XAI): Techniques that make AI decision-making transparent and understandable to humans.
Y - YOLO (You Only Look Once): A popular real-time object detection algorithm in computer vision.
Z - Zero-shot Learning: The ability of AI to perform tasks it hasn’t been explicitly trained on.
Credits: https://whatsapp.com/channel/0029Va4QUHa6rsQjhITHK82y
❤2
Forwarded from Artificial Intelligence
𝟰 𝗙𝗿𝗲𝗲 𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 𝘁𝗼 𝗕𝗼𝗼𝘀𝘁 𝗬𝗼𝘂𝗿 𝗥𝗲𝘀𝘂𝗺𝗲 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Want to Boost Your Resume with In-Demand Python Skills?👨💻
In today’s tech-driven world, Python is one of the most in-demand programming languages across data science, software development, and machine learning📊📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3Hnx3wh
Enjoy Learning ✅️
Want to Boost Your Resume with In-Demand Python Skills?👨💻
In today’s tech-driven world, Python is one of the most in-demand programming languages across data science, software development, and machine learning📊📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3Hnx3wh
Enjoy Learning ✅️
❤1
ChatGPT can help you land your dream job twice as fast. Here are 8 powerful ChatGPT prompts will 10X your interview chances.
Free book to master ChatGPT: https://news.1rj.ru/str/InterviewBooks/166
1. Customizing Your Resume ChatGPT prompt: "Can you make changes to my resume to fit the [Job Title] role at [Company]? Here's the job denoscription: [Paste Job Denoscription], and resume: [Paste Resume]."
2. Creating a Professional Summary ChatGPT prompt: "Using my resume, can you create a professional summary for me aligned to this [Job Title]." [Paste Resume]
3. Understanding Job Denoscriptions ChatGPT prompt: "What are the main responsibilities for this job? Please list the top three responsibilities required for [Job Title]." [Paste Job Denoscription]
4. Improving Your Resume Bullets ChatGPT prompt: "Please rewrite this bullet point from my resume using clear and impactful language while highlighting my accomplishments. [Paste Resume]"
5. Writing a LinkedIn Summary ChatGPT prompt: "Can you write a summary for my LinkedIn profile using my resume [Paste Resume]?"
6. Job Applications with ChatGPT ChatGPT prompt: "Can you identify my [Skills] experience from my resume [Paste Resume]? Please describe my specific [Skills] experience in conversational, clear language as if you were me."
7. Crafting Your Cover Letter ChatGPT prompt: "Can you write a personalized cover letter for the [Job Title] position at [Company]? Here's the job denoscription: [Paste Job Denoscription], and my current resume: [Paste Resume]."
8. Preparing for Interviews ChatGPT prompt: "What skills and experiences should I emphasize during an interview for the [Job Title] role in [Specific Industry]?"
ENJOY LEARNING 👍👍
Free book to master ChatGPT: https://news.1rj.ru/str/InterviewBooks/166
1. Customizing Your Resume ChatGPT prompt: "Can you make changes to my resume to fit the [Job Title] role at [Company]? Here's the job denoscription: [Paste Job Denoscription], and resume: [Paste Resume]."
2. Creating a Professional Summary ChatGPT prompt: "Using my resume, can you create a professional summary for me aligned to this [Job Title]." [Paste Resume]
3. Understanding Job Denoscriptions ChatGPT prompt: "What are the main responsibilities for this job? Please list the top three responsibilities required for [Job Title]." [Paste Job Denoscription]
4. Improving Your Resume Bullets ChatGPT prompt: "Please rewrite this bullet point from my resume using clear and impactful language while highlighting my accomplishments. [Paste Resume]"
5. Writing a LinkedIn Summary ChatGPT prompt: "Can you write a summary for my LinkedIn profile using my resume [Paste Resume]?"
6. Job Applications with ChatGPT ChatGPT prompt: "Can you identify my [Skills] experience from my resume [Paste Resume]? Please describe my specific [Skills] experience in conversational, clear language as if you were me."
7. Crafting Your Cover Letter ChatGPT prompt: "Can you write a personalized cover letter for the [Job Title] position at [Company]? Here's the job denoscription: [Paste Job Denoscription], and my current resume: [Paste Resume]."
8. Preparing for Interviews ChatGPT prompt: "What skills and experiences should I emphasize during an interview for the [Job Title] role in [Specific Industry]?"
ENJOY LEARNING 👍👍
❤5
Forwarded from Artificial Intelligence
𝗠𝗮𝘀𝘁𝗲𝗿 𝟲 𝗜𝗻-𝗗𝗲𝗺𝗮𝗻𝗱 𝗦𝗸𝗶𝗹𝗹𝘀 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘!😍
Want to boost your career with highly sought-after tech skills? These 6 YouTube channels will help you learn from scratch!👨💻
No need for expensive courses—start learning for FREE today!🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3Ddxd7P
Don’t miss this opportunity—start learning today and take your skills to the next level!✅️
Want to boost your career with highly sought-after tech skills? These 6 YouTube channels will help you learn from scratch!👨💻
No need for expensive courses—start learning for FREE today!🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3Ddxd7P
Don’t miss this opportunity—start learning today and take your skills to the next level!✅️
❤1
Forwarded from Python Projects & Resources
𝗧𝗵𝗲 𝗕𝗲𝘀𝘁 𝗙𝗿𝗲𝗲 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗖𝗵𝗲𝗮𝘁 𝗦𝗵𝗲𝗲𝘁 𝗼𝗻 𝗚𝗶𝘁𝗛𝘂𝗯 𝗘𝘃𝗲𝗿𝘆 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿 𝗦𝗵𝗼𝘂𝗹𝗱 𝗕𝗼𝗼𝗸𝗺𝗮𝗿𝗸😍
🧠Master Data Science Faster with This Free GitHub Cheat Sheet🚀
Whether you’re starting your data science journey or preparing for job interviews, having the right revision tool can make all the difference🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4klQmF3
Must-have resource for students and professionals✅️
🧠Master Data Science Faster with This Free GitHub Cheat Sheet🚀
Whether you’re starting your data science journey or preparing for job interviews, having the right revision tool can make all the difference🎯
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4klQmF3
Must-have resource for students and professionals✅️
❤1
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
"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
❤2
Forwarded from Python Projects & Resources
𝟱 𝗠𝘂𝘀𝘁-𝗙𝗼𝗹𝗹𝗼𝘄 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 𝗖𝗵𝗮𝗻𝗻𝗲𝗹𝘀 𝗳𝗼𝗿 𝗔𝘀𝗽𝗶𝗿𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁𝘀 𝗶𝗻 𝟮𝟬𝟮𝟱😍
Want to Become a Data Scientist in 2025? Start Here!🎯
If you’re serious about becoming a Data Scientist in 2025, the learning doesn’t have to be expensive — or boring!🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4kfBR5q
Perfect for beginners and aspiring pros✅️
Want to Become a Data Scientist in 2025? Start Here!🎯
If you’re serious about becoming a Data Scientist in 2025, the learning doesn’t have to be expensive — or boring!🚀
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4kfBR5q
Perfect for beginners and aspiring pros✅️
𝗛𝗼𝘄 𝘁𝗼 𝗚𝗲𝘁 𝗦𝘁𝗮𝗿𝘁𝗲𝗱 𝗶𝗻 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝗭𝗲𝗿𝗼 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲!🧠⚡
AI might sound complex. But guess what?
You don’t need a PhD or 5 years of experience to break into this field.
Here’s your 6-step beginner roadmap to launch your AI journey the smart way👇
🔹 𝗦𝘁𝗲𝗽 𝟭: Learn the Basics of Python (Your AI Superpower)
Python is the language of AI.
✅ Learn variables, loops, functions, and data structures
✅ Practice with platforms like W3Schools, SoloLearn, or Replit
✅ Understand NumPy & Pandas basics (they’ll be your go-to tools)
🔹 𝗦𝘁𝗲𝗽 𝟮: Understand What AI Really Is
Before diving deep, get clarity.
✅ What is AI vs ML vs Deep Learning?
✅ Learn core concepts like Supervised vs Unsupervised Learning
✅ Follow beginner-friendly YouTubers like “StatQuest” or “Codebasics”
🔹 𝗦𝘁𝗲𝗽 𝟯: Build Simple AI Projects (Even as a Beginner)
Start applying your skills with fun mini-projects:
✅ Spam Email Classifier
✅ House Price Predictor
✅ Rock-Paper-Scissors Game using AI
Pro Tip: Use scikit-learn for most of these!
🔹 𝗦𝘁𝗲𝗽 𝟰: Get Comfortable with Data (AI Runs on It!)
AI = Algorithms + Data
✅ Learn basic data cleaning with Pandas
✅ Explore simple datasets from Kaggle or UCI ML Repository
✅ Practice EDA (Exploratory Data Analysis) with Matplotlib & Seaborn
🔹 𝗦𝘁𝗲𝗽 𝟱: Take Free AI Courses (No Cost Learning)
You don’t need a fancy bootcamp to start learning.
✅ “AI For Everyone” by Andrew Ng (Coursera)
✅ “Machine Learning with Python” by IBM (edX)
✅ Kaggle’s Learn Track: Intro to ML
🔹 𝗦𝘁𝗲𝗽 𝟲: Join AI Communities & Share Your Work
✅ Join AI Discord servers, Reddit threads, and LinkedIn groups
✅ Post your projects on GitHub
✅ Engage in AI hackathons, challenges, and build in public
Your network = Your next opportunity.
🎯 𝗬𝗼𝘂𝗿 𝗙𝗶𝗿𝘀𝘁 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 = 𝗬𝗼𝘂𝗿 𝗘𝗻𝘁𝗿𝘆 𝗣𝗼𝗶𝗻𝘁
It’s not about knowing everything—it’s about starting.
Consistency will compound.
You’ll go from “beginner” to “builder” faster than you think.
Free Artificial Intelligence Resources: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
#ai
AI might sound complex. But guess what?
You don’t need a PhD or 5 years of experience to break into this field.
Here’s your 6-step beginner roadmap to launch your AI journey the smart way👇
🔹 𝗦𝘁𝗲𝗽 𝟭: Learn the Basics of Python (Your AI Superpower)
Python is the language of AI.
✅ Learn variables, loops, functions, and data structures
✅ Practice with platforms like W3Schools, SoloLearn, or Replit
✅ Understand NumPy & Pandas basics (they’ll be your go-to tools)
🔹 𝗦𝘁𝗲𝗽 𝟮: Understand What AI Really Is
Before diving deep, get clarity.
✅ What is AI vs ML vs Deep Learning?
✅ Learn core concepts like Supervised vs Unsupervised Learning
✅ Follow beginner-friendly YouTubers like “StatQuest” or “Codebasics”
🔹 𝗦𝘁𝗲𝗽 𝟯: Build Simple AI Projects (Even as a Beginner)
Start applying your skills with fun mini-projects:
✅ Spam Email Classifier
✅ House Price Predictor
✅ Rock-Paper-Scissors Game using AI
Pro Tip: Use scikit-learn for most of these!
🔹 𝗦𝘁𝗲𝗽 𝟰: Get Comfortable with Data (AI Runs on It!)
AI = Algorithms + Data
✅ Learn basic data cleaning with Pandas
✅ Explore simple datasets from Kaggle or UCI ML Repository
✅ Practice EDA (Exploratory Data Analysis) with Matplotlib & Seaborn
🔹 𝗦𝘁𝗲𝗽 𝟱: Take Free AI Courses (No Cost Learning)
You don’t need a fancy bootcamp to start learning.
✅ “AI For Everyone” by Andrew Ng (Coursera)
✅ “Machine Learning with Python” by IBM (edX)
✅ Kaggle’s Learn Track: Intro to ML
🔹 𝗦𝘁𝗲𝗽 𝟲: Join AI Communities & Share Your Work
✅ Join AI Discord servers, Reddit threads, and LinkedIn groups
✅ Post your projects on GitHub
✅ Engage in AI hackathons, challenges, and build in public
Your network = Your next opportunity.
🎯 𝗬𝗼𝘂𝗿 𝗙𝗶𝗿𝘀𝘁 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 = 𝗬𝗼𝘂𝗿 𝗘𝗻𝘁𝗿𝘆 𝗣𝗼𝗶𝗻𝘁
It’s not about knowing everything—it’s about starting.
Consistency will compound.
You’ll go from “beginner” to “builder” faster than you think.
Free Artificial Intelligence Resources: https://whatsapp.com/channel/0029VaoePz73bbV94yTh6V2E
#ai
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Forwarded from Artificial Intelligence
🎓 𝗟𝗲𝗮𝗿𝗻 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗳𝗿𝗼𝗺 𝗛𝗮𝗿𝘃𝗮𝗿𝗱, 𝗦𝘁𝗮𝗻𝗳𝗼𝗿𝗱, 𝗠𝗜𝗧 & 𝗚𝗼𝗼𝗴𝗹𝗲😍
Why pay thousands when you can access world-class Computer Science courses for free? 🌐
Top institutions like Harvard, Stanford, MIT, and Google offer high-quality learning resources to help you master in-demand tech skills👨🎓📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3ZyQpFd
Perfect for students, self-learners, and career switchers✅️
Why pay thousands when you can access world-class Computer Science courses for free? 🌐
Top institutions like Harvard, Stanford, MIT, and Google offer high-quality learning resources to help you master in-demand tech skills👨🎓📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3ZyQpFd
Perfect for students, self-learners, and career switchers✅️
Why learn SQL if ChatGPT can write it?
A few reasons why you should still learn SQL:
1️⃣ An understanding of the nuances of SQL is necessary to ask the Large Language Model (”LLM”) the right questions to get a good response.
2️⃣ You have to double check the LLMs response. Sometimes I get answers that uses features that have been deprecated (probably because the LLM was trained on older data). It still makes mistakes and overcomplicates problems.
3️⃣ Making changes to the query requires an understanding of SQL. Without it, you might get stuck. It's important to understand the query's purpose.
So what do I use these LLMs for?
I find it a good starting point for syntax or query structure. Like “how would I use a window function to get the latest record in a table?” But it doesn’t understand my company’s data models, table relationships, or business logic. This is where my SQL + business knowledge comes in.
A few reasons why you should still learn SQL:
1️⃣ An understanding of the nuances of SQL is necessary to ask the Large Language Model (”LLM”) the right questions to get a good response.
2️⃣ You have to double check the LLMs response. Sometimes I get answers that uses features that have been deprecated (probably because the LLM was trained on older data). It still makes mistakes and overcomplicates problems.
3️⃣ Making changes to the query requires an understanding of SQL. Without it, you might get stuck. It's important to understand the query's purpose.
So what do I use these LLMs for?
I find it a good starting point for syntax or query structure. Like “how would I use a window function to get the latest record in a table?” But it doesn’t understand my company’s data models, table relationships, or business logic. This is where my SQL + business knowledge comes in.
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ChatGPT Prompt to learn any skill
👇👇
(Tap on above text to copy)
👇👇
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)
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𝗟𝗲𝗮𝗿𝗻 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗳𝗼𝗿 𝗙𝗿𝗲𝗲 𝗼𝗻 𝗬𝗼𝘂𝗧𝘂𝗯𝗲 – 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗲 𝗣𝗹𝗮𝘆𝗹𝗶𝘀𝘁 𝗚𝘂𝗶𝗱𝗲😍
🎥 YouTube is the ultimate free classroom—and this is your Data Analytics syllabus in one post!👨💻
From Python and SQL to Power BI, Machine Learning, and Data Science, these carefully curated playlists will take you from complete beginner to job-ready✨️📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jzVggc
Enjoy Learning ✅️
🎥 YouTube is the ultimate free classroom—and this is your Data Analytics syllabus in one post!👨💻
From Python and SQL to Power BI, Machine Learning, and Data Science, these carefully curated playlists will take you from complete beginner to job-ready✨️📌
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/4jzVggc
Enjoy Learning ✅️
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How to get job as python fresher?
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies.
@crackingthecodinginterview
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
Python Interview Q&A: https://topmate.io/coding/898340
Like for more ❤️
ENJOY LEARNING 👍👍
1. Get Your Python Fundamentals Strong
You should have a clear understanding of Python syntax, statements, variables & operators, control structures, functions & modules, OOP concepts, exception handling, and various other concepts before going out for a Python interview.
2. Learn Python Frameworks
As a beginner, you’re recommended to start with Django as it is considered the standard framework for Python by many developers. An adequate amount of experience with frameworks will not only help you to dive deeper into the Python world but will also help you to stand out among other Python freshers.
3. Build Some Relevant Projects
You can start it by building several minor projects such as Number guessing game, Hangman Game, Website Blocker, and many others. Also, you can opt to build few advanced-level projects once you’ll learn several Python web frameworks and other trending technologies.
@crackingthecodinginterview
4. Get Exposure to Trending Technologies Using Python.
Python is being used with almost every latest tech trend whether it be Artificial Intelligence, Internet of Things (IOT), Cloud Computing, or any other. And getting exposure to these upcoming technologies using Python will not only make you industry-ready but will also give you an edge over others during a career opportunity.
5. Do an Internship & Grow Your Network.
You need to connect with those professionals who are already working in the same industry in which you are aspiring to get into such as Data Science, Machine learning, Web Development, etc.
Python Interview Q&A: https://topmate.io/coding/898340
Like for more ❤️
ENJOY LEARNING 👍👍
❤1
Forwarded from Artificial Intelligence
𝗦𝗤𝗟 𝟭𝟬𝟬% 𝗙𝗥𝗘𝗘 𝗖𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗖𝗼𝘂𝗿𝘀𝗲𝘀 😍
Looking to master SQL for Data Analytics or prep for your dream tech job? 💼
These 3 Free SQL resources will help you go from beginner to job-ready—without spending a single rupee! 📊✨
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3TcvfsA
💥 Start learning today and build the skills top companies want!✅️
Looking to master SQL for Data Analytics or prep for your dream tech job? 💼
These 3 Free SQL resources will help you go from beginner to job-ready—without spending a single rupee! 📊✨
𝐋𝐢𝐧𝐤👇:-
https://pdlink.in/3TcvfsA
💥 Start learning today and build the skills top companies want!✅️
5_6260478810370607322.pdf
2.6 MB
Pdf Resource:- How to get your first Data Science job
Source :- Springboard
Source :- Springboard
tom-lawry-ai-in-health-a-leader-s-guide-to-winning-in.pdf
9 MB
AI in Health
Tom Lawry, 2020
Tom Lawry, 2020
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