Essential Tools, Libraries, and Frameworks to learn Artificial Intelligence

1. Programming Languages:

Python

R

Java

Julia


2. AI Frameworks:

TensorFlow

PyTorch

Keras

MXNet

Caffe


3. Machine Learning Libraries:

Scikit-learn: For classical machine learning models.

XGBoost: For boosting algorithms.

LightGBM: For gradient boosting models.


4. Deep Learning Tools:

TensorFlow

PyTorch

Keras

Theano


5. Natural Language Processing (NLP) Tools:

NLTK (Natural Language Toolkit)

SpaCy

Hugging Face Transformers

Gensim


6. Computer Vision Libraries:

OpenCV

DLIB

Detectron2


7. Reinforcement Learning Frameworks:

Stable-Baselines3

RLlib

OpenAI Gym


8. AI Development Platforms:

IBM Watson

Google AI Platform

Microsoft AI


9. Data Visualization Tools:

Matplotlib

Seaborn

Plotly

Tableau


10. Robotics Frameworks:

ROS (Robot Operating System)

MoveIt!


11. Big Data Tools for AI:

Apache Spark

Hadoop


12. Cloud Platforms for AI Deployment:

Google Cloud AI

AWS SageMaker

Microsoft Azure AI


13. Popular AI APIs and Services:

Google Cloud Vision API

Microsoft Azure Cognitive Services

IBM Watson AI APIs


14. Learning Resources and Communities:

Kaggle

GitHub AI Projects

Papers with Code


Share with credits: https://t.iss.one/machinelearning_deeplearning

ENJOY LEARNING 👍👍

Step-by-Step Guide to Land a Data Analyst Job ✅📈

Landing your first data analyst job might feel like climbing a mountain, but with the right steps, it’s absolutely achievable! Here are 11 actionable tips to simplify the journey and make it feel like less of a grind.

1. Master SQL
SQL is the bread and butter of data analytics. Start with basic queries like SELECT, WHERE, and JOIN, then move on to more advanced topics such as subqueries, window functions, and performance optimization. Knowing how to manipulate and retrieve data effectively is essential.

2. Next, Learn a BI Tool
Data visualization is critical to communicating insights. Get familiar with at least one popular Business Intelligence (BI) tool, like Power BI or Tableau. Master how to create dashboards and meaningful visualizations that tell the story behind the numbers.

3. Drink Lots of Tea or Coffee (for Focus)
Staying sharp while learning these tools and skills takes focus. Whatever keeps you energized—lean into it! The data world moves fast, so staying alert and ready is key.

4. Tackle Relevant Data Projects
Hands-on experience is what sets you apart. Start with personal projects or even freelance opportunities to practice real-world data analysis. From cleaning data sets to building dashboards, showcase how you approach problems and present solutions.

5. Create a Relevant Data Portfolio
Your portfolio is your proof of work. Include your SQL scripts, dashboards, case studies, and any insights derived from your data projects. Platforms like GitHub or Tableau Public are great for displaying your work.

6. Focus on Actionable Data Insights
It's not enough to just analyze data. Always aim to derive actionable insights that can drive business decisions. Ask yourself: "How can this data improve outcomes?"—and make sure to communicate that clearly.

7. Remember Imposter Syndrome is Normal
If you feel like you don’t belong, you’re not alone. Imposter syndrome is common, but what matters is that you push through it. Confidence builds as you gain more experience and knowledge.

8. Prove You’re a Problem-Solver (important)
Employers want to know if you can handle real-world data problems. Find ways to show off your critical thinking and ability to solve complex problems, whether it’s through personal projects or during interviews.

9. Develop Compelling Data Visualization Stories
Telling a story with data is a skill. Build a narrative around the data you analyze. Why does this insight matter? How can it be used to make better decisions? Great visuals plus a compelling story equal impact.

10. Engage with LinkedIn Posts from Fellow Analysts (optional)
Networking is vital in any field. Actively engage in conversations on LinkedIn—comment on posts, share your insights, and build relationships with others in the field. Visibility on professional platforms can lead to job opportunities.

11. Illustrate Your Analytical Impact with Metrics & KPIs
Show that your work delivers results. In your portfolio or resume, highlight specific metrics and key performance indicators (KPIs) you’ve influenced. This makes your value clear to potential employers.

BONUS TIP: Share Your Career Story & Insights via LinkedIn Posts. Let people know how you’re progressing, what you’ve learned, and what challenges you’ve overcome. Posting regularly helps position you as an aspiring data analyst who is active in the field.

Hope it helps :)

𝗢𝗿𝗱𝗲𝗿 𝗢𝗳 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 in SQL ↓

1 → FROM (Tables selected).
2 → WHERE (Filters applied).
3 → GROUP BY (Rows grouped).
4 → HAVING (Filter on grouped data).
5 → SELECT (Columns selected).
6 → ORDER BY (Sort the data).
7 → LIMIT (Restrict number of rows).

𝗖𝗼𝗺𝗺𝗼𝗻 𝗤𝘂𝗲𝗿𝗶𝗲𝘀 𝗧𝗼 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 ↓

↬ Find the second-highest salary:

SELECT MAX(Salary) FROM Employees WHERE Salary < (SELECT MAX(Salary) FROM Employees);

↬ Find duplicate records:

SELECT Name, COUNT(*)
FROM Emp
GROUP BY Name
HAVING COUNT(*) > 1;

Transform the way you interact with AI

These ChatGPT Prompting Hacks will save you hours and supercharge your creativity:

How to focus on work: 2 effective ways

1. Don't fight it.

Admitting that you're going through a difficult time in your life is the first step to breaking the deadlock. Admitting doesn't mean you've given up. On the contrary, it is a sign that you are aware of the situation and ready to act.

Remember, like in physics: the resistance of a conductor weakens the current. In life, it's the same way: the more you resist, the more you lose strength.

2. Remember, the choice is yours.

University of Pittsburgh research has shown that anxiety directly affects our cognitive abilities. To get rid of this feeling, it's important to remind yourself that you can control the situation and leave the choices up to you.

- What tasks are critical to get done today for a successful project?
- What can I do after work to gain strength?
- What do I need to give up at this point?

Important Generative AI Terms

Learning Python for data science can be a rewarding experience. Here are some steps you can follow to get started:

1. Learn the Basics of Python: Start by learning the basics of Python programming language such as syntax, data types, functions, loops, and conditional statements. There are many online resources available for free to learn Python.

2. Understand Data Structures and Libraries: Familiarize yourself with data structures like lists, dictionaries, tuples, and sets. Also, learn about popular Python libraries used in data science such as NumPy, Pandas, Matplotlib, and Scikit-learn.

3. Practice with Projects: Start working on small data science projects to apply your knowledge. You can find datasets online to practice your skills and build your portfolio.

4. Take Online Courses: Enroll in online courses specifically tailored for learning Python for data science. Websites like Coursera, Udemy, and DataCamp offer courses on Python programming for data science.

5. Join Data Science Communities: Join online communities and forums like Stack Overflow, Reddit, or Kaggle to connect with other data science enthusiasts and get help with any questions you may have.

6. Read Books: There are many great books available on Python for data science that can help you deepen your understanding of the subject. Some popular books include "Python for Data Analysis" by Wes McKinney and "Data Science from Scratch" by Joel Grus.

7. Practice Regularly: Practice is key to mastering any skill. Make sure to practice regularly and work on real-world data science problems to improve your skills.

Remember that learning Python for data science is a continuous process, so be patient and persistent in your efforts. Good luck!

Please react 👍❤️ if you guys want me to share more of this content...

Build AI Agents with Python
👇👇
https://t.iss.one/aichads/212?single

The Pomodoro Technique:

This time management technique involves working in focused 25-minute increments, followed by a 5-minute break.

After four cycles, take a longer break of 15-30 minutes.

This will help you stay focused and avoid burnout.

🖥 Free Courses on Large Language Models

▪ChatGPT Prompt Engineering for Developers

▪LangChain for LLM Application Development

▪Building Systems with the ChatGPT API

▪Google Cloud Generative AI Learning Path

▪Introduction to Large Language Models with Google Cloud

▪LLM University

▪Full Stack LLM Bootcamp

#ai #generativeai

Projects to boost your resume for data roles

Creating Virtual Environment for Python

» Download Python

First you need python installed in your local machine to create virtual environment.
Download Python from Here

» Steps to create '.env' folder (virtual environment for python)
1. Navigate to the folder where you want to make your project
Example:

cd D:/code/

2. Open terminal (local terminal, command prompt, or vs code terminal) in that folder

3. Now, use these commands

python --version # Type this and hit enter to verify the python version


# Now use these commands
python -m venv .env

4. Your virtual environment is created in that folder, now activate this virtual environment using this command.

Command for 'Command Prompt':

.\env\Scripts\activate

Command for 'Powershell':

.\env\Scripts\Activate.ps1

Command for Git Bash or WSL:

source \.env\bin\activate

If Powershell gives you error like File cannot be loaded because running scripts is disabled then use this command!

Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

5. Congratulations🎊 Your virtual environment activated now make your project


Happy Coding 👨‍💻