Machine Learning with 9 Practical Applications

Machine Learning with 9 Practical Applications (Best Course)

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.

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 Simple Stock Price
• App-02 Simple Bioinformatics DNA Count
• App-03 EDA Basketball
• App-04 EDA Football
• App-05 EDA SP500 Stock Price
• App-06 EDA Cryptocurrency
• App-07 Classification Iris
• App-08 Classification Penguins
• App-09 Regression Boston Housing

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?

• 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

♋ Python Virtual Environments Download

Machine Learning /Data Scientist  Professionals Job Market

• Machine Learning/Data Scientist Jobs in Pakistan
• Machine Learning/Data Scientist Jobs in Dubai
• Machine Learning/Data Scientist Jobs in Canada
• Machine Learning/Data Scientist Jobs in Saudi Arabia
• Machine Learning/Data Scientist Jobs in Australia

Job Interview Questions 

• Data Sciences Job Interview Must Know Questions 
• Python Job Interview Questions and Answers
• Data Sciences Job Interview Questions and Answers
• Machine Learning Job Interview Questions
• RPA Job Interview Questions and Answers 

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