Support Vector Machine With Python From Scratch




Python and Machine Learning Software Training


All Job Seekers


Online and Offline Classes


Week Days and Week Ends

Duration :

60 Days

Python and Machine Learning What will you learn?

•An overview about Python and Machine Learning concepts.
•Learn to manage application state with Python and Machine Learning.
•Become a professional Python and Machine Learning Engineer by learning Python and Machine LearningLearn how to structure a large-scale project using Python and Machine Learning.
•You will know how to design Python and Machine Learning from scratch.
•Learn how to write tests for error handling in Python and Machine Learning.
•How to focus on writing the correct code to execute Python and Machine Learning.
•How to handle different types of data inside a workflow using Python and Machine Learning.
•Learn the absolute basics about Python and Machine Learning from scratch and take your skills to another level

support vector machine with python from scratch Training Features

•Post training offline support available
•Certificate after completion of the course
•Software & others tools installation Guidance
•Online Training with 100% placement assistance
•Interview guidance and preparation study materials.
•Repeating of lectures allowed (based on seat availability)
•Every class will be followed by practical assignments which aggregates to minimum 60 hours.
•Lifetime access to our 24×7 online support team who will resolve all your technical queries, through ticket based tracking system.

Who are eligible for Python and Machine Learning

•Cms, Ecm, Documentum, Java, J2ee, Sap, Ui Development, Software Testing, Project Management, Cloud Computing, Oracle, Oracle E-business Suite, Bpm, Wcm
•It Software, .net c#, Java Developer, technical support engineer, Automation Testing, Software Engineer, java, Basis, Sap Basis, Netweaver, Solution
•Java, .Net, Selenium, QTP, DBA, PHP, Neoload, Manual Testing, Rest, Soap, Web Services, SQL, UI, Peoplesoft, Cloud
•PHP, OpenCart Developer, Magento Developer, Html, Javascript, Jquery, Css, Photoshop, html, css, bootstrap, javascript, jquery, Business Development
•Web Designing, Web Development, Software Development, Software Testing, Mobile Application Development, Cloud Computing, Business Development, Automotive


Introduction to course
•Introduction to the Course
•Why Machine Learning
•Why Support Vector Machine
•Course Overview
•Please give us some feedback and Review
•Link to the Python codes for the projects and the data
•Introduction to Machine Learning
•Introduction to Machine Learning, Learning Process and Supervised Learning
•UnSupervised Learning and Reinforcement Learning
•History and Future of Machine Learning
•Dataset, Label and Features
•Training Data,Testing Data and Outliers
•Model (Difference between Classification and Regression)
•Model (Function,Parameters,Hyperparameters)
•Training a model,Cost,Error,Loss,Risk,Accuracy
•Overfitting,Underfitting,Just RightOptimum (Part 1)
•Overfitting,Underfitting,Just RightOptimum (Part 2)
•Validation and Cross Validation,Generalization,Data Snooping,Validation Set
•Probability Distributions and Curse of Dimensionlity
•Small Sample Size problems,One Shot Learning
•Importance of Data in Machine Learning,Data Encoding and Preprocessing
•General Flow of a typical Machine Learning Project
•Introduction to Python
•Introduction to IDE,Hello World
•Introduction to Data Type, Numbers
•Variable and Operators (Numbers)
•Variables and Operators (Rational Operators and Functions)
•Variables and Operators (String)
•Variables and Operators (String and print Statement)
•Lists(Indexing,Slicing-Built in Lists Functions)
•Lists(Copying a List)
•Tuples(Indexing,Slicing,Built in Tuple Functions)
•Set(initialize,Built in Set Functions)
•Logical Operator,Decision Making,For Loops,While Loops,Functions
•Logical Operator,Decision Making,For Loops,While Loops,List Comprehension
•Calculator Project
•Support Vector Machine
•Introduction SVM
•Linear Discriminants
•Linear Discriminants higher spaces
•Linear Discriminants Decision Boundary
•Generalized Linear Model
•Feature Transformation
•Max Margin Linear Discriminant
•Hard Margin Vs Soft Margin
•Multiclass Extension
•SVM Vs Logistic Regression Sparsity
•SVM Optimization
•SVM Langrangian Dual
•Python Packages & Titanic DataSet
•Using Numpy, Pandas and Matplotlib (Part 1)
•Using Numpy, Pandas and Matplotlib (Part 2)
•Using Numpy, Pandas and Matplotlib (Part 3)
•Using Numpy, Pandas and Matplotlib (Part 4)
•DataSet Preprocessing
•SVM with Sklearn
•SVM without Sklearn (Part 1)
•SVM without Sklearn (Part 2)
•Optional SVM Section
•Optional SVM Optimization (Part 1)
•Optional SVM Optimization (Part 2)
•Optional SVM Optimization (Part 3)
•Optional SVM Optimization (Part 5)
•Optional SVM Optimization (Part 4)
•Optional SVM Optimization (Part 6)