Udemy - Support Vector Machines in Python - SVM in Python 2019 [Course Drive]

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  • LanguageEnglish
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  • Last checkedApr. 29th '20
  • Date uploadedApr. 27th '20
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Support Vector Machines in Python - SVM in Python 2019

Learn Support Vector Machines in Python. Covers basic SVM models to Kernel-based advanced SVM models of Machine Learning






What you'll learn

• Get a solid understanding of Support Vector Machines (SVM)
• Understand the business scenarios where Support Vector Machines (SVM) is applicable
• Tune a machine learning model's hyperparameters and evaluate its performance.
• Use Support Vector Machines (SVM) to make predictions
• Implementation of SVM models in Python

Requirements

• Students will need to install Python and Anaconda software but we have a separate lecture to help you install the same

Description

You're looking for a complete Support Vector Machines course that teaches you everything you need to create a Support Vector Machines model in Python, right?
You've found the right Support Vector Machines techniques course!
How this course will help you?
A Verifiable Certificate of Completion is presented to all students who undertake this Machine learning advanced course.
If you are a business manager or an executive, or a student who wants to learn and apply machine learning in Real world problems of business, this course will give you a solid base for that by teaching you some of the advanced technique of machine learning, which are Support Vector Machines.
Why should you choose this course?
This course covers all the steps that one should take while solving a business problem through Decision tree.
Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.
What makes us qualified to teach you?
The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using machine learning techniques and we have used our experience to include the practical aspects of data analysis in this course
We are also the creators of some of the most popular online courses - with over 150,000 enrollments and thousands of 5-star reviews like these ones:
This is very good, i love the fact the all explanation given can be understood by a layman - Joshua
Thank you Author for this wonderful course. You are the best and this course is worth any price. - Daisy
Our Promise
Teaching our students is our job and we are committed to it. If you have any questions about the course content, practice sheet or anything related to any topic, you can always post a question in the course or send us a direct message.
Download Practice files, take Quizzes, and complete Assignments
With each lecture, there are class notes attached for you to follow along. You can also take quizzes to check your understanding of concepts. Each section contains a practice assignment for you to practically implement your learning.

Go ahead and click the enroll button, and I'll see you in lesson 1!

Cheers
Start-Tech Academy

Who this course is for:

• People pursuing a career in data science
• Working Professionals beginning their Data journey
• Statisticians needing more practical experience
• Anyone curious to master SVM technique from Beginner to Advanced in short span of time

Files:

Machine Learning Adv Support Vector Machines Svm Python Machine Learning Adv Support Vector Machines Svm Python 02 Machine Learning Basics
  • 011 Introduction to Machine Learning.mp4 (123.2 MB)
  • 011 Introduction to Machine Learning-en.srt (18.4 KB)
  • 012 Building a Machine Learning Model-en.srt (9.7 KB)
  • 012 Building a Machine Learning Model.mp4 (44.9 MB)
  • ReadMe.txt (0.2 KB)
  • Visit Coursedrive.org.url (0.1 KB)
    • 01 Setting up Python and Python Crash Course
    • 001 Installing Python and Anaconda-en.srt (2.6 KB)
    • 001 Installing Python and Anaconda.mp4 (18.6 MB)
    • 002 Course resources.html (0.9 KB)
    • 002 Files-svm-py.zip (1.8 MB)
    • 003 Opening Jupyter Notebook-en.srt (9.1 KB)
    • 003 Opening Jupyter Notebook.mp4 (73.0 MB)
    • 004 Introduction to Jupyter-en.srt (12.4 KB)
    • 004 Introduction to Jupyter.mp4 (50.9 MB)
    • 005 Arithmetic operators in Python Python Basics-en.srt (4.0 KB)
    • 005 Arithmetic operators in Python Python Basics.mp4 (15.9 MB)
    • 006 Strings in Python Python Basics-en.srt (16.4 KB)
    • 006 Strings in Python Python Basics.mp4 (80.0 MB)
    • 007 Lists Tuples and Directories Python Basics-en.srt (17.0 KB)
    • 007 Lists Tuples and Directories Python Basics.mp4 (73.2 MB)
    • 008 Working with Numpy Library of Python-en.srt (10.5 KB)
    • 008 Working with Numpy Library of Python.mp4 (53.8 MB)
    • 009 Customer.csv (64.0 KB)
    • 009 Working with Pandas Library of Python-en.srt (8.2 KB)
    • 009 Working with Pandas Library of Python.mp4 (56.1 MB)
    • 010 Working with Seaborn Library of Python-en.srt (7.5 KB)
    • 010 Working with Seaborn Library of Python.mp4 (48.6 MB)
    03 Maximum Margin Classifier
    • 013 Course flow-en.srt (1.7 KB)
    • 013 Course flow.mp4 (9.8 MB)
    • 013 Resources.zip (1.4 MB)
    • 014 The Concept of a Hyperplane-en.srt (4.8 KB)
    • 014 The Concept of a Hyperplane.mp4 (35.3 MB)
    • 015 Maximum Margin Classifier-en.srt (3.2 KB)
    • 015 Maximum Margin Classifier.mp4 (26.2 MB)
    • 016 Limitations of Maximum Margin Classifier-en.srt (2.4 KB)
    • 016 Limitations of Maximum Margin Classifier.mp4 (12.5 MB)
    04 Support Vector Classifier
    • 017 Support Vector classifiers-en.srt (9.7 KB)
    • 017 Support Vector classifiers.mp4 (64.1 MB)
    • 018 Limitations of Support Vector Classifiers-en.srt (1.6 KB)
    • 018 Limitations of Support Vector Classifiers.mp4 (13.0 MB)
    05 Support Vector Machines
    • 019 Kernel Based Support Vector Machines-en.srt (6.4 KB)
    • 019 Kernel Based Support Vector Machines.mp4 (45.7 MB)
    06 Creating Support Vector Machine Model in Python
    • 020 Regression and Classification Models-en.srt (0.8 KB)
    • 020 Regression and Classification Models.mp4 (5.2 MB)
    • 021 The Data set for the Regression problem-en.srt (3.0 KB)
    • 021 The Data set for the Regression problem.mp4 (41.7 MB)
    • 022 Importing data for regression model-en.srt (5.3 KB)
    • 022 Importing data for regression model.mp4 (32.2 MB)
    • 023 Missing value treatment-en.srt (3.1 KB)
    • 023 Missing value treatment.mp4 (22.3 MB)
    • 024 Dummy Variable creation-en.srt (4.7 KB)
    • 024 Dummy Variable creation.mp4 (31.7 MB)
    • 025 X-y Split-en.srt (3.8 KB)
    • 025 X-y Split.mp4 (19.3 MB)
    • 026 Test-Train Split-en.srt (5.8 KB)
    • 026 Test-Train Split.mp4 (27.5 MB)
    • 027 Standardizing the data-en.srt (6.2 KB)
    • 027 Standardizing the data.mp4 (47.3 MB)
    • 028 SVM based Regression Model in Python-en.srt (9.7 KB)
    • 028 SVM based Regression Model in Python.mp4 (79.8 MB)
    • 029 The Data set for the Classification problem-en.srt (1.8 KB)
    • 029 The Data set for the Classification problem.mp4 (22.0 MB)
    • 030 Classification model - Preprocessing-en.srt (8.2 KB)
    • 030 Classification model - Preprocessing.mp4 (54.5 MB)
    • 031 Classification model - Standardizing the data-en.srt (1.8 KB)
    • 031 Classification model - Standardizing the data.mp4 (11.9 MB)
    • 032 SVM Based classification model-en.srt (11.5 KB)
    • 032 SVM Based classification model.mp4 (78.5 MB)
    • 033 Hyper Parameter Tuning-en.srt (9.8 KB)
    • 033 Hyper Parameter Tuning.mp4 (70.8 MB)
    • 034 Polynomial Kernel with Hyperparameter Tuning-en.srt (4.1 KB)
    • 034 Polynomial Kernel with Hyperparameter Tuning.mp4 (22.9 MB)
    • 035 Radial Kernel with Hyperparameter Tuning-en.srt (6.6 KB)
    • 035 Radial Kernel with Hyperparameter Tuning.mp4 (45.7 MB)
    07 Bonus Section
    • 036 Bonus Lecture.html (2.4 KB)
    08 Appendix 1 Data Preprocessing
    • 037 Gathering Business Knowledge-en.srt (3.9 KB)
    • 037 Gathering Business Knowledge.mp4 (22.3 MB)
    • 038 Data Exploration-en.srt (3.6 KB)
    • 038 Data Exploration.mp4 (20.5 MB)
    • 039 The Dataset and the Data Dictionary-en.srt (7.8 KB)
    • 039 The Dataset and the Data Dictionary.mp4 (69.4 MB)
    • 040 Importing Data in Python-en.srt (5.6 KB)
    • 040 Importing Data in Python.mp4 (27.8 MB)
    • 041 Univariate analysis and EDD-en.srt (3.4 KB)
    • 041 Univariate analysis and EDD.mp4 (24.2 MB)
    • 042 EDD in Python-en.srt (10.4 KB)
    • 042 EDD in Python.mp4 (61.8 MB)
    • 043 Outlier Treatment-en.srt (4.5 KB)
    • 043 Outlier Treatment.mp4 (24.5 MB)
    • 044 Outlier Treatment in Python-en.srt (13.0 KB)
    • 044 Outlier Treatment in Python.mp4 (70.2 MB)
    • 045 Missing Value Imputation-en.srt (4.1 KB)
    • 045 Missing Value Imputation.mp4 (25.0 MB)
    • 046 Missing Value Imputation in Python-en.srt (4.1 KB)
    • 046 Missing Value Imputation in Python.mp4 (23.4 MB)
    • 047 Seasonality in Data-en.srt (3.8 KB)
    • 047 Seasonality in Data.mp4 (17.0 MB)
    • 048 Bi-variate analysis and Variable transformation-en.srt (18.3 KB)
    • 048 Bi-variate analysis and Variable transformation.mp4 (100.4 MB)
    • 049 Variable transformation and deletion in Pyt

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