![]() ![]() Throughout the course, we will highlight scikit-learn best practices and give you the intuition to use scikit-learn in a methodologically sound way. The course will cover practical aspects through the use of Jupyter notebooks and regular exercises. The authors of the course are scikit-learn core developpers, they will be your guides throughout the training! Format The MOOC is completely free of charge, and all the course materials are also available on a GitHub repository. The training will be essentially practical, focusing on examples of applications with code executed by the participants. The course is more than a cookbook: it will teach you to be critical about each step of the design of a predictive modeling pipeline: from choices in data preprocessing, to choosing models, gaining insights on their failure modes and interpreting their predictions. Step-by-step and didactic lessons introduce the fundamental methodological and software tools of machine learning, and is as such a stepping stone to more advanced challenges in artificial intelligence, text mining, or data science. This course is an in-depth introduction to predictive modeling with scikit-learn. In this field, scikit-learn is a central tool: it is easily accessible, yet powerful, and naturally dovetails in the wider ecosystem of data-science tools based on the Python programming language. ![]() While Python makes deep learning easy, it will still. However, its no secret that Python’s best application is in deep learning and artificial intelligence tasks. It works in almost all fields, from web development to developing financial applications. Including principal component analysis, data whitening, shallow autoencoders, deep autoencoders, transfer learning with autoencoders, and autoencoder applications.įinally, you will test your skills in a final project.Predictive modeling is a pillar of modern data science. Python is famed as one of the best programming languages for its flexibility. You will finally learn about dimensionality reduction and autoencoders. We will then focus on Convolutional Neural Networks, training your model on a GPU and Transfer Learning (pre-trained models). You will then learn how to build and train deep neural networks-learning how to apply methods such as dropout, initialization, different types of optimizers and batch normalization. This will be followed by an in-depth introduction on how to construct Feed-forward neural networks in PyTorch, learning how to train these models, how to adjust hyperparameters such as activation functions and the number of neurons. You will first review multiclass classification, learning how to build and train a multiclass linear classifier in PyTorch. Also, you will learn how to train these models using state of the art methods. In this new Ebook written in the friendly Machine Learning Mastery style. Deep learning methods offer a lot of promise for time series forecasting, such as the automatic learning of temporal dependence and the automatic handling of temporal structures like trends and seasonality. In the first course, you learned the basics of PyTorch in this course, you will learn how to build deep neural networks in PyTorch. Predict the Future with MLPs, CNNs and LSTMs in Python. This course is the second part of a two-part course on how to develop Deep Learning models using Pytorch. If you do not have this pre-requiste knowledge, it is highly recommended you complete the PyTorch Basics for Machine Learning course prior to starting this course. NOTE: In order to be successful in completing this course, please ensure you are familiar with PyTorch Basics and have practical knowledge to apply it to Machine Learning. Enroll to learn more, complete the course and claim your badge! Please Note: Learners who successfully complete this IBM course can earn a skill badge - a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. ![]()
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