Machine Learning with 9 Practical Applications in Saudia, Bahrain, Oman

About

Machine learning is becoming an increasingly important analytical tool, enabling businesses to extract meaningful information from raw data, offering accurate analyses and complex solutions to data-rich problems. The Machine Learning: Practical Applications course focuses on the practical applications of machine learning in modern business analytics and equips you with the technical skills and knowledge to apply machine learning techniques to real-world business problems.

Machine Learning with 9 Practical Applications (Best Course)

The course provides students with practical hands-on experience in training deep and machine learning models using real-world dataset. This course covers several technique in a practical manner, the projects include but not limited to:

Course Key Learning

Deep Learning Practical Applications

Machine Learning 9 Practical Applications

App-01 ARTIFICIAL NEURAL NETWORKS to predict car sales
App-02 DEEP NEURAL NETWORKS for image classification
App-03 LE-NET DEEP NETWORK to classify Traffic Signs
App-04 TRANSFER LEARNING for CNN image classification
App-05 PROPHET TIME SERIES to predict crime
App-06 PROPHET TIME SERIES to predict market conditions
App-07 NATURAL LANGUAGE PROCESSING MODEL to analyze Reviews
App-08 NATURAL LANGUAGE PROCESSING to develop spam filder
App-09 USER-BASED COLLABORATIVE FILTERING to develop recommender system

Course Key Outcomes

Gain insight into the business applications of machine learning
Develop the technical and practical skills to apply machine learning to solve real-world problems in your business context
Understand the fundamental principles of machine learning and the flow of the machine learning pipeline
Learn to code in R and apply machine learning techniques to various types of data
Maximise team productivity and unlock new efficiencies by implementing machine learning in business
Explore regression as a supervised machine learning technique to predict a continuous variable (response or target) from a set of other variables (features or predictors)
Discover how variable selection and shrinkage methods are used to improve the efficiency of a regression model when applied to complex data sets
Explore classification as a supervised machine learning technique to predict binary (or discrete) response variables from a set of features
Discover how tree-based methods and ensemble learning methods are applied to improve the accuracy of a prediction
Understand what neural networks are, its most successful applications, and how it can be used within a business context

Who should attend?

Consultant & programmers drive key transformation projects for organization
Professionals willing to develop career in Machine Learning /Data Sciences
IT Manager / Business Analyst / Data Analyst/ Data Scientist / Database Admin

Course Pre-Requisites & Credit Hours

Course Pre-Requisite – None
Credit Hours – 60 (Lectures 30 hrs + 30 hrs Exercises & Examples )
Course Duration 3 Months

Educational approach

Lecture sessions are illustrated with case studies, practical questions and examples
Practical exercises include Machine Learning, Data Visualization examples and discussions
Install and Configure you Big Data, Machine Learning platform using industry famous tools

International Student Fee : 600$| 2,250 AED| 2,250 SAR

Machine Learning /Data Scientist Professionals Job Market

Flexible Class Options

Evening Classes | Corporate Group Workshops
Week End Classes For Professionals SAT | SUN

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