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 👍👍
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 👍👍
👍9
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 :)
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 :)
👍9❤1