๐ฐ ๐๐ฟ๐ฒ๐ฒ ๐ ๐ถ๐ฐ๐ฟ๐ผ๐๐ผ๐ณ๐ ๐๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐๐ฒ ๐๐ ๐ง๐ฟ๐ฎ๐ถ๐ป๐ถ๐ป๐ด ๐ ๐ผ๐ฑ๐๐น๐ฒ๐ ๐๐ผ ๐๐ผ๐ผ๐๐ ๐ฌ๐ผ๐๐ฟ ๐ฆ๐ธ๐ถ๐น๐น๐๐
Generative AI is no longer just a buzzwordโitโs a career-maker๐งโ๐ป๐
Recruiters are actively looking for candidates with prompt engineering skills, hands-on AI experience, and the ability to use tools like GitHub Copilot and Azure OpenAI effectively.๐ฅ
๐๐ข๐ง๐ค๐:-
http://pdlink.in/4fKT5pL
If youโre looking to stand out in interviews, land AI-powered roles, or future-proof your career, this is your chance
Generative AI is no longer just a buzzwordโitโs a career-maker๐งโ๐ป๐
Recruiters are actively looking for candidates with prompt engineering skills, hands-on AI experience, and the ability to use tools like GitHub Copilot and Azure OpenAI effectively.๐ฅ
๐๐ข๐ง๐ค๐:-
http://pdlink.in/4fKT5pL
If youโre looking to stand out in interviews, land AI-powered roles, or future-proof your career, this is your chance
โค2
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.me/machinelearning_deeplearning
ENJOY LEARNING ๐๐
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.me/machinelearning_deeplearning
ENJOY LEARNING ๐๐
โค2