10 Must-Know Python Libraries for Machine Learning in 2025
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Python is one of the most popular languages for machine learning, and it’s easy to see why. It’s simple to use, flexible and has a vast ecosystem of libraries that make building machine learning models both fast and easy. As we get further into 2025, new libraries continue to pop up, while the old favorites continue to improve.
In this article, we’ll look at 10 Python libraries you should know if you’re working with machine learning.
1. Scikit-learn
Scikit-learn is a popular machine learning library in Python that provides tools for data analysis. It supports many algorithms like classification, regression, and clustering. This makes it useful for several machine learning tasks.
Key Features:
- Built on top of NumPy, SciPy, and matplotlib
- Includes tools for preprocessing data, model selection, and evaluation
- Supports cross-validation, hyperparameter tuning, and feature extraction
2. TensorFlow
TensorFlow is an open-source machine learning framework developed by Google, primarily used for deep learning and neural networks. It provides both CPU and GPU computation for high performance and is widely utilized in research and production.
Key Features:
- Flexible ecosystem for research and production deployment
- Supports a variety of tasks, including image, text, and speech processing
- High-level API (Keras) for easy model building and deployment
3. PyTorch
PyTorch is an open-source deep learning framework developed by Facebook, known for its flexibility and ease of use. Unlike static graphs used in other frameworks, PyTorch uses dynamic computation graphs. It makes debugging easier and also helps with experimenting models.
Key Features:
- Supports dynamic computation graphs
- Provides high-performance acceleration using CPU and GPU
- Strong integration with Python and other scientific libraries
4. XGBoost
XGBoost is a popular machine learning algorithm known for its high performance and scalability. It implements gradient boosting to combine weak models into a strong model, using decision trees as base learners to minimize loss via gradient descent.
Key Features:
- Handles missing data and works well with large datasets
- Highly scalable and fast
- Used for both classification and regression tasks
5. LightGBM
LightGBM is a fast gradient boosting algorithm designed for large datasets and high-dimensional data. It uses decision trees as base models and employs histogram-based techniques to speed up training.
Key Features:
- Reduces memory usage and training time
- High accuracy and scalability
- Works well with categorical features
6. CatBoost
CatBoost is a gradient boosting algorithm developed by Yandex that excels in handling categorical features. It uses ordered boosting to reduce overfitting and supports automatic handling of missing values.
Key Features:
- Supports parallel and GPU-based computation
- Easy to use with minimal preprocessing required
- Known for fast training and high accuracy
7. Hugging Face Transformers
Hugging Face Transformers is a library for natural language processing (NLP) that provides pre-trained models for several tasks such as text classification, translation, and question answering. It simplifies using state-of-the-art models in NLP with minimal setup.
Key Features:
- Supports pre-trained models like BERT, GPT, and T5
- Built for easy fine-tuning on custom datasets
- Compatible with TensorFlow and PyTorch
8. FastAI
FastAI is a deep learning library built on top of PyTorch that focuses on ease of use and flexibility. It provides high-level abstractions that simplify training machine learning models. It emphasizes best practices and cutting-edge techniques.
Key Features:
- Pre-trained models for vision, text, and tabular data
- Powerful tools for data augmentation and model fine-tuning
- Designed for both beginners and experts with strong community support
9. JAX
JAX is a numerical computing library developed by Google that extends NumPy with automatic differentiation. It is designed for high-performance machine learning research, and it supports both CPU and GPU/TPU acceleration.
Key Features:
- High performance with just-in-time (JIT) compilation
- Supports array operations and linear algebra
- Flexible and efficient for custom deep learning models
10. Optuna
Optuna is an open-source optimization framework designed for hyperparameter tuning in machine learning. It automates the search for optimal model parameters using algorithms like tree-structured Parzen estimators (TPE).
Key Features:
- Supports parallelization of optimization tasks
- Provides visualization tools for tracking optimization progress
- Highly flexible and scalable, integrates well with other machine learning libraries
Final Thoughts
As machine learning continues to evolve rapidly in 2025, staying equipped with the right tools is more important than ever. The Python libraries highlighted in this list — ranging from foundational frameworks like TensorFlow and PyTorch to specialized tools like Hugging Face Transformers and Optuna — empower developers and researchers to build, optimize, and deploy cutting-edge models with efficiency and flexibility.
Nowadays, for me,the big problem is not libraries, but darasets. How can I use the entire content of Project Gutenberg to create a model to use with NLP?
That’s a *great* pivot, and you’re thinking like a real applied machine learning engineer now. If libraries aren’t a barrier anymore, and you’re ready to start working with **real-world text**, then using **Project Gutenberg** for **NLP** is a smart move—especially because you’re already strong in Python and have a background in applied math.
Let’s walk through how to **use Project Gutenberg to build an NLP model** — from dataset collection to model training.
—
## 🧠 What You Can Do with Project Gutenberg Data
Project Gutenberg is a goldmine of free eBooks in the public domain. You can use it for many NLP projects like:
| Task | Description | Model Type |
|—————————–|————————————————-|———————-|
| 📖 Text Generation | Generate Shakespeare-like or Dickens-like text | Language Modeling |
| 🧾 Text Classification | Classify books by author or genre | Classification |
| 🧹 Summarization | Summarize chapters or whole books | Sequence-to-sequence |
| 👥 Named Entity Recognition | Extract people, places, events | Sequence tagging |
| 🧠 Sentiment Analysis | Apply polarity scoring on sentences | Classification |
—
## 📦 Step-by-Step: Use Project Gutenberg for NLP
### **Step 1: Install
gutenbergor userequestsfor raw text**bash
pip install gutenberg
But the
gutenbergpackage has limitations. I suggest using the **raw text** from [https://www.gutenberg.org](https://www.gutenberg.org) instead.Here’s how to fetch a book:
pythonimport requests
url = "https://www.gutenberg.org/files/1342/1342-0.txt" # Pride and Prejudice
response = requests.get(url)
text = response.text
print(text[:1000]) # Preview first 1000 characters
—
### **Step 2: Clean the Text**
Books come with headers/footers. Clean them like this:
pythondef clean_gutenberg_text(text):
start = text.find("*** START OF THIS PROJECT GUTENBERG EBOOK")
end = text.find("*** END OF THIS PROJECT GUTENBERG EBOOK")
return text[start:end]
cleaned_text = clean_gutenberg_text(text)
—
### **Step 3: Tokenize and Preprocess**
Use
nltkorspaCy:bash
pip install nltk
pythonimport nltk
from nltk.tokenize import word_tokenize
nltk.download('punkt')
tokens = word_tokenize(cleaned_text.lower())
print(tokens[:20])
You can also remove stopwords, punctuation, etc.
—
### **Step 4: Choose a Project Idea**
Here are 3 practical beginner-friendly projects with Gutenberg data:
—
#### ✅ **1. Word Prediction Model**
Use n-grams to predict the next word.
pythonfrom nltk import bigrams, FreqDist
import random
bi_grams = list(bigrams(tokens))
freq = FreqDist(bi_grams)
def predict_next_word(word):
candidates = [(a, b) for (a, b) in freq if a == word]
if not candidates:
return None
return max(candidates, key=lambda x: freq[x])[1]
print(predict_next_word("elizabeth"))
—
#### ✅ **2. Text Generation (Character-Level)**
Use an LSTM in Keras for a character-based language model (like GPT-mini!).
—
#### ✅ **3. Author Classification**
Download 3-4 books each from 3 authors. Train a classifier (
Naive BayesorTF-IDF + SVM) to predict the author of a text excerpt.—
## 🗃 Where to Get More Books
Use a script to download multiple books from Gutenberg:
pythonbook_ids = [1342, 1661, 2701] # Add more IDs
books = {}
for book_id in book_ids:
url = f"https://www.gutenberg.org/files/{book_id}/{book_id}-0.txt"
text = requests.get(url).text
books[book_id] = clean_gutenberg_text(text)
—
## 🚀 Want to Train a Language Model?
If you want to go further and train a **Transformer (like GPT-2)** on Gutenberg data, we can walk through that using Hugging Face’s
transformerslibrary and prepare your dataset accordingly.—
## 📘 Final Tip
Once you’ve built your first NLP project, even something small:
– Push it to GitHub
– Include a README explaining the model and the dataset
– Show some visualizations or outputs
That *is* your portfolio.
—
I don’t know machine learning
I don’t know machine learning so I want to learn in this topic
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