We have all experienced it: starting is the toughest part of any journey. So getting started in the ML field wouldn’t be any different.

This is why today I want to highlight some of the essential tools that every beginner — or person willing to get started — with ML should be using.

Jupyter Notebook

Jupyter Notebook is a blessing for any beginner willing to start to code professionally. It is an open-source web interface that allows you to create and share documents with code, equations, visualizations, and text. It’s widely used for data science, machine learning, and scientific computing, supporting languages like Python, R, and Julia

You can use it directly on your computer with the Anaconda distribution or take advantage of Google Colab, PyTorch Lightning or Kaggle Notebooks.

Its main advantages:

• Easy-to-use interface for beginners.
• Allows to integrate code with explanatory text, equations, and visualizations.
• Facilitates an interactive learning experience.
• A large community and extensive resources make learning easier.

So, if you are new to machine learning. Jupyter Notebook is a must-have because it helps you learn in a friendly way.

Hugging Face and Transformers

One of the most important – and used – tools of the last years is Hugging Face. It is an open-source hub for Natural Language Processing (NLP) and Large Language Models (LLMs).

It has the Transformers library, one of the most used libraries for natural language processing. Its main goal is to smooth the working process with LLMs and Generative AI. The variety of pre-trained models available in Transformers is vast, with foundational and fine-tuned models designed for tasks such as text classification, translation, question answering, and more.

• Some of its advantages are:
• Models exist for backends like PyTorch and TensorFlow
• Many pre-trained models that can be customized
• Easy-to-use APIs and great documentation
• Collaborative environment with an active community

If you want to get started, you should just visit their website and sign up.

LangChain

LangChain is a framework designed for AI engineers to integrate language models with various data sources and other LLMs. It also facilitates the development of context-aware reasoning applications, providing tools to create agents capable of complex problem-solving tasks.

Key Features:

• An interface for creating and managing agents, tools, and libraries.
• Support for reasoning applications, including tracing and evaluation.
• User-friendly for beginners, offering a comprehensive set of tools and templates to streamline the development of intelligent agents.

You can check out the LangChain quick start guide for a useful introductory tutorial

Scikit-learn

Scikit-learn is a great tool for machine learning in Python. It provides simple and efficient tools to implement and understand most machine-learning algorithms and models. With Scikit-learn, you can perform tasks like classification, regression, clustering, and dimensionality reduction with just a few lines of code.

Its key features are:

• Easy-to-use API and well-documented functions.
• It supports a wide range of ML models.
• Works seamlessly with other Python libraries like NumPy and pandas.

It helps you to analyze data and find patterns quickly.

Kaggle

Kaggle is the go-to place for both beginners and seasoned professionals. It consists of a platform with an infinite number (literally!) of datasets, competitions to upskill your knowledge, and a collaborative space to learn from other data professionals and ML enthusiasts. Furthermore, it provides a wide variety of resources such as tutorials, and code examples.

Its main features:

• Access many datasets with a wide variety of domains.
• Real-world challenges to gain experience (and some rewards!)
• An active community of learners and experts.
• Free notebooks to run code in the cloud

By leveraging Kaggle, you can experiment, learn, and share your work, making it an indispensable tool for any machine learning newbie.

 
You can make your first steps into the world of ML smoother, easier, and more enjoyable by leveraging these tools.

Author: Muhammad Talha Waseem