Get Latest De

Email:info@onlinetrainings.in

Deep Learning Machine Learning Practical

Course

DEEP LEARNING MACHINE LEARNING PRACTICAL

Category

Machine Learning Professional Institute

Eligibility

Graduates and Technology Aspirants

Mode

Regular Offline and Online Live Training

Batches

Week Days and Week Ends

Duration :

2 Months

Machine Learning What will you learn?

•How To resolve errors in Machine Learning.
•Work with standard programming skills in Machine Learning.
•Learn how to use loop statement in Machine Learning.
•Cover all basic Concepts with in-depth description of Machine Learning.
•You will know how to configure a Machine Learning jobs.
•Learn Machine Learningat a minimal cost and enjoy the instructor support.
•Learn all the hooks and crooks of Machine Learning at your pace.
•Learn the basics of Machine Learning and get up and running quickly
•Learn Machine Learning from basic to advanced with examples and interactive sessions at peak.

deep learning machine learning practical Training Features

•Most comprehensive Industrry curriculum
•Certificate after completion of the course
•Get Certified at the Best Training Institute.
•Trainer support after completion of the course
•Facility of Lab on cloud available (based on booking)
•We also provide Cost Effective and Flexible Payment Schemes
•Every class will be followed by practical assignments which aggregates to minimum 60 hours.
•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 Machine Learning

•big data analytics, java, J2ee, Ui Development, user interface designing, Big Data, spark, scala, pyspark, python, cloudera, aws, Industry Marketing, business
•embedded platform software engineers, embedded multimedia developer, Middleware Developers, Android Middleware, device driver developers, c, c++, linux
•Java/j2ee, Microsoft, Erp, Cloud, Qa/testing, Automation Testing, Analytics, Machine Learning, Artificial Intelligence, Agile Project Management, Mobility
•PHP, MySQL JQuery, Application Developer, Net, .Net, Asp, Vb, Visual Basic, Vb Script, ASP.Net MVC, WCF, C#, SQL Server, .NET Framework, JQuery, XML, OOP
•Software Developer, quality testing engineers, Ui Developers, Oracle Apps, Sap Modules, Sharepoint Developers, Tibco Developers, .net Developers, java

DEEP LEARNING MACHINE LEARNING PRACTICAL Topics

•INTRODUCTION TO THE COURSE QUICK WIN IN FIRST MINS
•Welcome Message
•Course overview
•ML vs DL vs AI
•ML Deep Dive
•Download Course Materials
•ANACONDA AND JUPYTER INSTALLATION
•Download and Set up Anaconda
•What is Jupyter Notebook
•Install Tensorflow
•How to run a Jupyter Notebook
•PROJECT ARTIFICIAL NEURAL NETWORKS CAR SALES PREDICTION
•Theory Part
•Project Overview
•Import Data
•Data Visualization Cleaning
•Model Training
•Model Evaluation
•PROJECT DEEP NEURAL NETWORKS CIFAR CLASSIFICATION
•Problem Statement
•Data Vizualization
•Data Preparation
•Model Training Part
•Save the Model
•Image Augmentation Part
•PROJECT PROPHET TIME SERIES CHICAGO CRIME RATE
•Import Dataset
•Prepare the Data
•Make Predictions
•PROJECT PROPHET TIME SERIES AVOCADO MARKET
•Load Avocado Data
•Explore Dataset
•Make Predictions Part
•Make Predictions Part Region Specific
•Make Prediction Part
•PROJECT LENET DEEP NETWORK TRAFFIC SIGN CLASSIFICATION
•Load Data
•Data Exploration
•Data Normalization
•Model Training
•PROJECT NATURAL LANGUAGE PROCESSING EMAIL SPAM FILTER
•Naive Bayes Theory Part
•Spam Project Overview
•Visualize Dataset
•Count Vectorizer
•Testing
•PROJECT NATURAL LANGUAGE PROCESSING YELP REVIEWS
•Theory
•Load Dataset
•Visualize Dataset Part
•Exercise
•Apply NLP to Data
•Apply Count Vectorizer to Data
•Model Evaluation Part
•PROJECT USERBASED COLLABORATIVE FILTERING MOVIE RECOMMENDER SYSTEM
•Import Movie Dataset
•Collaborative Filter One Movie
•Full Movie Recomendation
•INTRODUCTION TO THE COURSE [QUICK WIN IN FIRST 10-12 MINS]
•Updates on Reviews
•BONUS: Learning Path
•ML vs. DL vs. AI
•BONUS: ML vs DL vs AI
•BONUS: 5 Benefits of Jupyter Notebook
•PROJECT #1: ARTIFICIAL NEURAL NETWORKS – CAR SALES PREDICTION
•Theory Part 1
•Theory Part 2
•Theory Part 3
•Theory Part 4
•Theory Part 5
•Model Training 1
•Model Training 2
•PROJECT #2: DEEP NEURAL NETWORKS – CIFAR-10 CLASSIFICATION
•Model Training Part 1
•Model Training Part 2
•Image Augmentation Part 1
•Image augmentation Part 2
•PROJECT #3: PROPHET TIME SERIES – CHICAGO CRIME RATE
•PROJECT #4: PROPHET TIME SERIES – AVOCADO MARKET
•Make Predictions Part 1
•Make Predictions Part 2 (Region Specific)
•Make Prediction Part 2.1
•PROJECT #5: LE-NET DEEP NETWORK – TRAFFIC SIGN CLASSIFICATION
•PROJECT #6: NATURAL LANGUAGE PROCESSING – E-MAIL SPAM FILTER
•Naive Bayes Theory Part 1
•Naive Bayes Theory Part 2
•PROJECT #7: NATURAL LANGUAGE PROCESSING – YELP REVIEWS
•Visualize Dataset Part 1
•Visualize Dataset Part 2
•Exercise #1
•Exercise #2
•Exercise #3
•Model Evaluation Part 1
•Model Evaluation Part 2
•PROJECT #8: USER-BASED COLLABORATIVE FILTERING – MOVIE RECOMMENDER SYSTEM
•***YOUR SPECIAL BONUS***