Python Machine Learning By Example

Unlock machine learning best practices with real-world use cases

Created by Yuxi (Hayden) Liu

Strengthen your Python machine learning skills by working with real-world examples and proven techniques. Explore advanced topics like NLP transformers, multimodal vision models, and neural networks using popular frameworks. Gain practical experience while learning best practices that help you solve complex machine learning problems with confidence.

Packt | Jul 2024 | 526 min

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LevelIntermediate

CategoriesData Science, Deep Learning Architectures and Frameworks, PyTorch, Python

1Getting Started with Machine Learning and Python
2Building a Movie Recommendation Engine
3Predicting Online Ad Click-Through with Tree-Based Algorithms
4Predicting Online Ad Click-Through with Logistic Regression
5Predicting Stock Prices with Regression Algorithms
6Predicting Stock Prices with Artificial Neural Networks
7Mining the 20 Newsgroups Dataset with Text Analysis Techniques
8Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling
9Recognizing Faces with Support Vector Machine
10Machine Learning Best Practices
11Categorizing Images of Clothing with Convolutional Neural Networks
12Making Predictions with Sequences Using Recurrent Neural Networks
13Advancing Language Understanding and Generation with Transformer Models
14Building An Image Search Engine Using Multimodal Models
15Making Decisions in Complex Environments with Reinforcement Learning

What You Will Learn

You will work through practical projects that use real data, applying advanced modeling techniques step by step. Each topic is explained with clear examples, so you can see how to use Python libraries like PyTorch and TensorFlow to build, train, and evaluate your own models. Best practices are woven throughout to help you develop strong habits.

Key Features

Apply NLP transformers and multimodal models to real-world data challenges
Build and fine-tune neural networks using PyTorch and TensorFlow
Master best practices to create robust and reliable machine learning solutions

Target Audience

If you are a data scientist, machine learning engineer, or analyst with a solid foundation in Python, this course is designed for you. It is especially valuable if you want to move beyond the basics and tackle more advanced machine learning projects. You will gain the skills needed to confidently apply modern techniques to real business or research problems.

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