The most common frameworks in data science/AI, that you must know about, are:
Scikit-learn: The Swiss Army Knife of Machine Learning! It is a Python library for machine learning which provides a variety of machine learning algorithms, including classification, regression, clustering, and dimensionality reduction.
TensorFlow: The Robo-Whiz! It is an open-source software library for machine learning and artificial intelligence. It is developed by Google and is used for a variety of tasks, including image recognition, natural language processing, and machine translation.
PyTorch: The AI Collaborator! It is another open-source machine learning library for Python. It is similar to TensorFlow, but it is focused on deep learning.
Pandas: The King of Data Manipulation! 🐼👑 With its powerful tools and royal elegance, it reigns over spreadsheets like a true monarch. It is a Python library for data manipulation and analysis. It provides a variety of data structures and functions for working with dataframes.
NumPy: The Einstein of Arrays! It is a Python library for scientific computing. It provides a high-performance multidimensional array object and a variety of mathematical functions.
Honorable mentions:
🌳 Random Forest: The Data Wizard of Oz! "Lions and tigers and bears, oh my!" Fear not, dear data explorers! Random Forest comes to your rescue, taming the wild data jungles and leading you to the Emerald Insights!
🔢 XGBoost: The Extreme Gradient Guru! Ready to boost your models to new heights? XGBoost is the adrenaline shot your algorithms need! Hold on tight as you ride the waves of boosting brilliance!
🕵️♀️ DeepAR: The Time Series Sleuth! When time is of the essence, DeepAR uncovers the hidden patterns in your time series data! Say goodbye to uncertainty and hello to forecasting finesse!
The best framework for you will depend on your specific needs and requirements. If you are just starting out with machine learning, Scikit-learn is a good option. It is easy to use and has a wide range of features. If you are looking for a more powerful and flexible framework, TensorFlow or PyTorch are good choices. If you need to work with large datasets, Pandas is a good option. And if you need to perform scientific computing, NumPy is a good choice.
We will dissect each one in upcoming posts, so stay tuned!