Want to learn something new, but not able to find good tutorials and courses on that topic?


Know about some best learning resources, you would like to share with everyone?


Do you know? About 5 Million tutorials and courses get published everyday.

Asquero helps you find the best out of those million tutorials and courses.

Learn Machine Svm | Best Tutorials and Courses


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: Andrew Ng
Duration: Self-Paced
Description: You will learn about Machine Learning basics, Linear Regression with One Variable, Basics of Linear Algebra, Linear Regression with Multiple Variables, Logistic Regression, Regularization, Neural Networks, Support Vector Machines, Unsupervised Learning, Dimensionality Reduction, Anomaly Detection, Recommender Systems, Photo OCR, etc.
Prerequisite: Basic knowledge in Machine Learning would be helpful, but not required.
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.
Share with someone who needs it

What are your thoughts?