Price: $49.99
(as of Nov 30,2024 06:15:58 UTC – Details)

Publisher ‏ : ‎ Cambridge University Press; 1st edition (December 31, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 850 pages
ISBN-10 ‏ : ‎ 1009170244
ISBN-13 ‏ : ‎ 978-1009170246
Item Weight ‏ : ‎ 1.01 pounds
Dimensions ‏ : ‎ 6 x 1.68 x 9 inches

Machine Learning with Python: Principles and Practical Techniques

In recent years, machine learning has become an increasingly important field in the world of technology. As more and more companies look to leverage the power of data to make informed decisions, the demand for professionals with machine learning skills has skyrocketed.

Python has emerged as one of the most popular programming languages for machine learning due to its simplicity and versatility. In this post, we will explore the principles and practical techniques of machine learning using Python.

Principles of Machine Learning:
1. Supervised Learning: In supervised learning, the algorithm is trained on a labeled dataset, where the input data is paired with the correct output. The algorithm learns to map input data to the correct output based on the training examples.
2. Unsupervised Learning: In unsupervised learning, the algorithm is given an unlabeled dataset and tasked with finding patterns or structures within the data. Clustering and dimensionality reduction are common techniques used in unsupervised learning.
3. Reinforcement Learning: In reinforcement learning, the algorithm learns through trial and error by interacting with an environment and receiving feedback in the form of rewards or penalties.

Practical Techniques of Machine Learning in Python:
1. Data Preprocessing: Before training a machine learning model, it is essential to clean and preprocess the data. This may involve handling missing values, encoding categorical variables, and scaling the features.
2. Model Selection: There are various machine learning algorithms available in Python, such as linear regression, decision trees, random forests, and neural networks. It is crucial to select the right algorithm based on the nature of the problem and the characteristics of the data.
3. Model Evaluation: Once a model has been trained, it is important to evaluate its performance using metrics such as accuracy, precision, recall, and F1 score. Cross-validation techniques can help assess the model’s generalization ability.
4. Hyperparameter Tuning: Hyperparameters are parameters that are set before training a model. Grid search and random search are common techniques used to find the optimal hyperparameters for a machine learning model.
5. Deployment: After training and evaluating a machine learning model, it can be deployed in production environments using frameworks like TensorFlow, scikit-learn, or PyTorch.

In conclusion, machine learning with Python offers a powerful set of tools and techniques for solving complex problems in various domains. By understanding the principles and mastering the practical techniques of machine learning, you can unlock the potential of data-driven decision-making in your projects.
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