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Python’s Role in Machine Learning. Python has a crucial role in Machine Learning because Python provides libraries like NumPy, Pandas, Scikit-learn, TensorFlow, and Keras. These libraries offer tools and functions essential for data manipulation, analysis, and building machine learning models.
This course will give you an introduction to machine learning with the Python programming language. You will learn about supervised learning, unsupervised learning, deep learning, image processing, and generative adversarial networks.
Dive into Machine Learning with Python! This IBM course on Coursera covers supervised vs unsupervised learning, classification algorithms, clustering, and hands-on labs using SciPy and scikit-learn. Gain job-ready skills and earn a certificate in Machine Learning.
In this step-by-step tutorial you will: Download and install Python SciPy and get the most useful package for machine learning in Python. Load a dataset and understand it’s structure using statistical summaries and data visualization. Create 6 machine learning models, pick the best and build confidence that the accuracy is reliable.
In this comprehensive guide, we will delve into the core concepts of machine learning, explore key algorithms, and learn how to implement them using popular Python libraries like NumPy, Pandas, Matplotlib, and Scikit-Learn. By the end, you’ll have the know.
You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them.
You’ll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them.
Testimonials. Recommendations. What you'll learn. Build ML models with NumPy & scikit-learn, build & train supervised models for prediction & binary classification tasks (linear, logistic regression) Build & train a neural network with TensorFlow to perform multi-class classification, & build & use decision trees & tree ensemble methods.
An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.
Put your data to work through machine learning with Python. Join Harvard University Instructor Pavlos Protopapas to learn how to use decision trees, the foundational algorithm for your understanding of machine learning and artificial intelligence. Featuring faculty from: Enroll Today. Self-Paced. Length. 6 weeks. 4-5 hours a week. Certificate Price