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Machine Learning With Python The Complete Guide




Python and Machine Learning Professional Institute


Technology Learners


Online and Classroom Sessions


Week Days and Week Ends

Duration :

Fast Track and Regular 60 Days

Python and Machine Learning Objectives

•Understand the concepts in Python and Machine Learning
•Learn to manage application state with Python and Machine Learning.
•You will learn basics of programming in Python and Machine Learning
•Students will learn how to build apps using Python and Machine Learning.Learn or brush up with the basics of Python and Machine Learning
•Will be able to write error free programs in Python and Machine Learning
•Learn the Ins and Outs of Python and Machine Learning in few Hours
•Students will have a solid understanding on how to create Python and Machine Learning App.
•Get to know tips and tricks to work more quickly and effectively in Python and Machine Learning.

machine learning with python the complete guide Course Highlights

•Additional Sessions for Doubt Clearing
• First step to landing an entry-level job
•Job Placement Assistance with Good Companies
•Best Opportunity To Both Learn And Work From Home
• Greater productivity and increased workforce morale
•Hands On Experience – will be provided during the course to practice
•One-on-one training, online training, team or Corporate training can be provided
•This Instructor-led classroom course is designed with an aim to build theoretical knowledge supplemented by ample hands-on lab exercises

Who are eligible for Python and Machine Learning

•.Net Developer, SilverLight, MVC3, Entity Framework 4, WCF, SQL/PLSQL, c#, SQL Server 2008, HTML5, .Net
•Java Developer, Php Mysql, Zend 2.0, java j2ee struts hibernate spring, iOS, Android, html
•java, php, .net, 3dmodelling, unitydeveloper, androiddeveloper, gamedeveloper, Software Developer, Php, Java, Photoshop
•SAP CRM, SAP BI/BW, SAP FICO, .NET/MVC, JAVA/Spring, Mobile Apps Developer, IOS Developer, ETL Testing, VSTS Testing, Oracle ERP Support, Peoplesoft
•Xml Publisher, Php Developer, Android Application Development, Html Tagging, E-publishing, Software Development


Introduction to Machine Learning
•Course Introduction
•Installing Dependencies
•Introduction to Supervised Learning
•Introduction to Unsupervised and Reinformcement Learning
•Introduction to Deep Learning
•Linear Regression with Machine Learning
•Introduction to Linear Regression using Machine Learning
•Understanding the Linear Regression Process
•Coding a Linear Regression with Machine Learning Model
•Visualizing Linear Regression with Machine Learning
•Random Forest Modeling
•Introduction to Random Forest Models
•Understanding Decision Trees
•Coding a Random Forest Model
•Visualizing a Random Forest Decision Tree
•Support Vector Machines
•Introduction to Support Vector Machines
•Understanding the SVM Kernel
•Coding a SVM Model
•Visualizing Classification Boundaries
•Naive Bayes
•Introduction to Naive Bayes
•Understanding Bayesian Probability
•Coding with Natural Language Processing
•Building Naive Bayes Classifier with NLP
•Validation Techniques
•Over and Under fitting
•Cross Validation Techinques
•Coding Cross Validation techniques
•K-Nearest Neighbors
•Introduction to KNNs
•Distance Measurements and KNNs
•Building a KNN Model
•Calculating Squared Error and Learning with KNN
•K-Means Clustering
•Introduction to Unsupervised Learning and K-Means Clustering
•Introduction to Heirarchical K-Means Clustering
•Building a K-Means Clustering Model
•Visualizing a Dendrogram
•Hidden Markov Models
•Introduction to Markov Chains
•Introduction to Latent Variables and HMM
•Coding a simple HMM
•Gaussian Mixture Models
•Introduction to GMMs and Distributions
•GMMs and Joint Probability Distributions
•Building a Simple GMM
•Visualizing Boundary Spaces with GMMs
•Collaborative Filtering
•Introduction to Collaborative Filtering
•Introduction to Model-Based CFs and Matrix Factorization
•Building a Memory Based CF Model
•Building a Model Based CF Model
•Project 1
•Project 2
•Project 3