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Learn Pytorch Classification | Best Tutorials and Courses


Taught By: Simplilearn
Duration: Self-Paced
Description: This tutorial is for absolute beginners who want themselves to get introduced to Scikit-Learn and begin with coding in Machine Learning. This tutorial covers the following topics that you will learn about: What is Scikit-Learn? What we can achieve using Scikit-Learn? Solving a real-life problem using Scikit-Learn.
Prerequisite: You need to have a basic knowledge of Python programming.
Taught By: edureka!
Duration: Self-Paced
Description: This tutorial introduces you to Machine Learning in Python. It will also take you through Regression and Clustering techniques along with a demo on SVM classification on the famous iris dataset. This tutorial will help you learn about the following topics: Machine Learning Overview, Introduction to Scikit-Learn, Install and Setup Scikit-Learn, Regression and Classification, Solving a real-life problem using Scikit-Learn.
Prerequisite: You need to have a basic knowledge of Python programming. Also, a basic level of knowledge in Machine Learning would be beneficial.
Duration: Self-Paced
Description: This is the official documentation of Scikit-Learn, which covers all the concepts and functionality of the Scikit-Learn library. This is a Beginner to Advanced learning resource. If you are a beginner in Machine Learning then you must study very carefully because some concepts might be difficult to grasp as you go through the documentation. If you are an advanced Machine Learning practitioner, no doubt this learning resource would prove to be very helpful for you.
Prerequisite: You need to have a basic knowledge of Python programming. Also, an introductory level of knowledge in Machine Learning would be beneficial.
Taught By: Andreas Mueller
Duration: Self-Paced
Description: This course is an exhaustive Machine Learningearning resource covering the following topics that you will learn about: Supervised Learning and Unsupervised Learning, Data Wrangling in Python, Solving a real-world problem using Scikit-Learn Cross-Validation, Hyperparameter Tuning, Scikit-Learn Pipelines, Regression, Support Vector Machines, Decision Trees, Random Forests, Ensemble Methods, Feature Selection, Hierarchical and Density-based clustering algorithms.
Prerequisite: You need to have a basic knowledge of Python programming.
Taught By: Sebastian Raschka
Duration: Self-Paced
Description: This book has a lot of features as given below: This book is great for Beginner and Intermediate learners. The book is very comprehensive and the coding examples are quite good. Preprocessing is covered quite well and good treatment of maths as well.
Prerequisite: You need to have a basic knowledge of Python programming.
Taught By: Aurélien Geron
Duration: Self-Paced
Description: This is one of the best books you can get for someone who is just starting out in Machine Learning, in its libraries such as Tensorflow. The book covers the basics in a very good manner.
Prerequisite: You need to have basic Python knowledge to understand the material in the book.
Taught By: Alex Aklson
Duration: Approx. 2 to 4 hours per week
Description: You will learn about Deep Learning basics, Artificial Neural Networks, Building Regression Models with Keras, Building Classification Models with Keras, Shallow and Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders.
Prerequisite: You need to know the basics of Machine Learning and Python programming.
Taught By: Soumith Chintala
Duration: Self-Paced
Description: You will learn about PyTorch basics, Autograd: Automatics Differentiation, Building Neural Networks with PyTorch, Training a Classifier with PyTorch.
Prerequisite: You need to know the basics of Machine Learning, Deep Learning, and Python programming.
Taught By: Luis Serrano
Duration: Approx. 2 months to complete
Description: You will learn about PyTorch basics, Use Pytorch to classify images, Convolutional Neural Networks, Style transfer, Recurrent Neural Networks, Natual Language Classification, Deploying with PyTorch.
Prerequisite: You need to know the basics of Machine Learning, Deep Learning, and Python programming.
Duration: Approx. 31 hours to complete
Description: You will learn about Tensors and Datasets, Linear Regression using PyTorch, Multiple Input Output Linear Regression, Logistic Regression for Classification, Softmax Regression, Shallow Neural Networks, Deep Networks, Convolutional Neural Networks.
Prerequisite: You need to know the basics of Machine Learning, Deep Learning, and Python programming.
Duration: Approx. 2 to 4 hours per week
Description: You will learn about Classification, Training Softmax with PyTorch, Basics of Neural Networks, Deep Neural Networks, Computer Vision Networks (Convolution, Max Pooling, etc.), Dimensionality Reduction, and Autoencoders.
Prerequisite: You need to know the basics of Machine Learning, Deep Learning, and Python programming.
Taught By: Kirill Eremenko
Duration: Approx. 44 hours to complete
Description: You will learn about data preprocessing in Python and R, Linear Regression, Polynomial Regression, Support Vector Regression, Decision Tree Regression, Random Forest Regression, Evaluation Regression Model Performance, Regression Model Selection, Logistic Regression, KNN, SVM, Naive Bayes, K-Means Clustering, Hierarchical Clustering, Association Rule Learning, Apriori, Reindorcemenr Learning, Natural Language Processing, Dimensionality Reduction, PCA, LDA, Model Selection and Boosting.
Prerequisite: Basics of Python programming would be helpful.
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