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Learning Path Statistics For Machine Learning

Course

LEARNING PATH STATISTICS FOR MACHINE LEARNING

Category

Machine Learning Professional Institute

Eligibility

Freshers and Career Changers

Mode

Online and Offline Classes

Batches

Week Days and Week Ends

Duration :

60 Days

Machine Learning What will you learn?

•Learn to write Machine LearningFunctions.
•Students will learn widely used Machine Learning concepts
•Become a professional Machine Learning Engineer by learning Machine LearningHow to create your own Machine Learning components from scratch.
•Learn the Basic Concepts of Machine Learning with Practical Examples
•What is Machine Learning and How to Build apps using Machine Learning.
•Beginner to Advance Level: Learn to Plan, Design and Implement Machine LearningGo through all the steps to designing a game from start to finish.with this time saving course you will Learn Machine Learning and ready to use it

learning path statistics for machine learning Training Highlights

•Additional Sessions for Doubt Clearing
•Basic Training starting with fundamentals
•Get Certified at the Best Training Institute.
•The courses range from basic to advanced level
•Highly Experienced Trainer with 10+ Years in MNC Company
•Project manager can be assigned to track candidates’ performance
•Every class will be followed by practical assignments which aggregates to minimum 60 hours.
•We help the students in building the resume boost their knowledge by providing useful Interview tips

Who are eligible for Machine Learning

•c++, React.js, Java Fullstack, Core Java Data Structure, Java Micro-services, Devops, Microsoft Azure, Cloud Computing, Machine Learning, Automation Testing
•Developers, Architect, Business Analyst, Analytics, Core Java, Android, Android Sdk, Javascript, Front End, Angular Js, Html, Css, Software Engineering
•Java Developer, Production Support, Asp.Net, Oracle Applications, Pl Sql Developer, Hyperion Planning, Dot Net, UI Designer, UI Developer, MS CRM, Hardware
•networking, Test Cases, Automation Testing, perl, python, Protocol Testing, http, l4, l7, dns, tcp, ip, smtp, Cloud Computing, l3, l2, pig
•Sharepoint Architect, Mobile Architect, MSBI Module Lead, Filenet Developer, WBM, IBM BPM

LEARNING PATH STATISTICS FOR MACHINE LEARNING Syllabus

•Fundamentals of Statistical Modeling and Machine Learning Techniques
•The Course Overview
•Machine Learning
•Statistical Terminology for Model Building and Validation
•Bias Versus Variance TradeOff
•Linear Regression Versus Gradient Descent
•Machine Learning Losses
•Train Validation and Test Data
•CrossValidation and Grid Search
•Machine Learning Model Overview
•Compensating Factors in Machine Learning Models
•Simple Linear Regression from First Principles
•Simple Linear Regression Using Wine Quality Data
•MultiLinear Regression
•Linear Regression Model Ridge Regression
•Linear Regression Model Lasso Regression
•Maximum Likelihood Estimation
•Logistic Regression
•Random Forest
•Variable Importance Plot
•Test your knowledge
•Advanced Statistics for Machine Learning
•Artificial Neural Networks
•Forward Propagation and Back Propagation
•Optimization of Neural Networks
•ANN Classifier Applied on Handwritten Digits
•Introduction to Deep Learning
•Kmeans Clustering
•Principal Component Analysis
•Singular Value Decomposition
•Deep Autoencoders
•Deep Autoencoders Applied on Handwritten Digits
•Introduction to Reinforcement Learning
•Reinforcement Learning Basics
•Markov Decision Process and Bellman Equations
•Dynamic Programming
•Monte Carlo Methods
•Temporal Difference Learning