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Ryan Ahmed
In this hands-on project, we will build and train a simple deep neural network model to predict the approval of personal loan for a person based on features like age, experience, income, locations, family, education, exiting mortgage, credit card etc.
By the end of this project, you will be able to:
- Understand the applications of Artificial Intelligence and Machine Learning techniques in the banking industry
- Understand the theory and intuition behind Deep Neural Networks
- Import key Python libraries, dataset, and perform Exploratory Data Analysis.
Read more
In this hands-on project, we will build and train a simple deep neural network model to predict the approval of personal loan for a person based on features like age, experience, income, locations, family, education, exiting mortgage, credit card etc.
By the end of this project, you will be able to:
- Understand the applications of Artificial Intelligence and Machine Learning techniques in the banking industry
- Understand the theory and intuition behind Deep Neural Networks
- Import key Python libraries, dataset, and perform Exploratory Data Analysis.
Read more
In this hands-on project, we will build and train a simple deep neural network model to predict the approval of personal loan for a person based on features like age, experience, income, locations, family, education, exiting mortgage, credit card etc.
By the end of this project, you will be able to:
- Understand the applications of Artificial Intelligence and Machine Learning techniques in the banking industry
- Understand the theory and intuition behind Deep Neural Networks
- Import key Python libraries, dataset, and perform Exploratory Data Analysis.
- Perform data visualization using Seaborn.
- Standardize the data and split them into train and test datasets.
- Build a deep learning model using Keras with Tensorflow 2.0 as a back-end.
- Assess the performance of the model and ensure its generalization using various Key Performance Indicators (KPIs).
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/qnybyu/bank
What's inside
Syllabus
LOAN APPROVAL PREDICTION USING NEURAL NETWORKS
In this hands-on project, we will build and train a simple deep neural network model to predict the approval of personal loans for bank customers based on their features such as age, experience, income, locations, family, education, exiting mortgage, and credit card information.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
May be particularly useful for those with intermediate skills in AI and ML, helping them to hone and strengthen their current abilities
Suitable for learners with experience in the banking industry, as it focuses on the application of AI and ML in this specific context
Involves the practical implementation of AI and ML, providing learners with hands-on experience in these fields
Relying on Python, Keras, and Tensorflow, it uses popular and widely-adopted tools and technologies in the AI and ML industry
Provides a strong foundation in the theoretical principles of Deep Neural Networks, empowering learners with a comprehensive understanding of the subject
Designed for learners in the North American region, which may limit accessibility for those outside this area
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Save Bank Loan Approval Prediction With Artificial Neural Nets to your list so you can find it easily later:
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Reviews summary
Informative, strongly recommended for tech bank pros
Learners say this course on predicting bank loan approvals using artificial neural networks is informative and super helpful. It is strongly recommended for tech-based bank professionals who want to leverage their knowledge to make huge profits.
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Bank Loan Approval Prediction With Artificial Neural Nets with these
activities:
Review key financial concepts
Show steps
Review foundational financial concepts to refresh your knowledge and prepare for the course.
Browse courses on
Financial Concepts
Show steps
-
Revisit terms like 'interest', 'loan', and 'credit'.
-
Recall the principles of budgeting, saving, and investing.
Read 'Deep Learning for Coders with Fastai and PyTorch'
Show steps
Gain a comprehensive understanding of deep learning concepts and practical implementation through this recommended book.
View
Deep Learning for Coders with Fastai and...
on Amazon
Show steps
-
Read through the book to grasp the fundamentals of deep learning.
-
Work through the book's exercises to reinforce your understanding.
Solve data analysis and manipulation exercises
Show steps
Reinforce your data analysis and manipulation skills by solving practice exercises.
Browse courses on
Data Analysis
Show steps
-
Practice data cleaning and preprocessing using libraries like pandas or Numpy.
-
Analyze datasets to identify patterns and trends.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Keras and Tensorflow tutorials
Show steps
Familiarize yourself with the Keras and TensorFlow frameworks through guided tutorials to enhance your understanding of deep learning.
Browse courses on
Deep Learning Frameworks
Show steps
-
Follow tutorials on Keras' website to get started with model building.
-
Explore TensorFlow's documentation to learn about its core concepts and capabilities.
Assist fellow students in understanding course concepts
Show steps
Enhance your understanding by explaining concepts to others and providing guidance.
Show steps
-
Join online forums or study groups related to the course.
-
Answer questions and provide insights to fellow learners.
Build a mini deep learning project
Show steps
Apply your learning by creating a small-scale deep learning project to gain hands-on experience.
Show steps
-
Choose a simple dataset and define a problem statement.
-
Design and implement a deep learning model to address the problem.
-
Evaluate the performance of your model and make improvements as needed.
Attend workshops on deep learning applications in banking
Show steps
Expand your knowledge by attending workshops that showcase real-world applications of deep learning in the banking industry.
Show steps
-
Identify and register for relevant workshops.
-
Actively participate in the workshops and engage with professionals.
Organize course materials for future reference
Show steps
Enhance your ability to review and retain course content by organizing materials.
Show steps
-
Create a structured system for storing notes, assignments, and other materials.
-
Review and summarize key concepts periodically to reinforce your understanding.
Review key financial concepts
Show steps
Review foundational financial concepts to refresh your knowledge and prepare for the course.
Browse courses on
Financial Concepts
Show steps
Show steps
Show steps
- Revisit terms like 'interest', 'loan', and 'credit'.
- Recall the principles of budgeting, saving, and investing.
Read 'Deep Learning for Coders with Fastai and PyTorch'
Show steps
Gain a comprehensive understanding of deep learning concepts and practical implementation through this recommended book.
View
Deep Learning for Coders with Fastai and...
on Amazon
Show steps
Show steps
Show steps
- Read through the book to grasp the fundamentals of deep learning.
- Work through the book's exercises to reinforce your understanding.
Solve data analysis and manipulation exercises
Show steps
Reinforce your data analysis and manipulation skills by solving practice exercises.
Browse courses on
Data Analysis
Show steps
Show steps
Show steps
- Practice data cleaning and preprocessing using libraries like pandas or Numpy.
- Analyze datasets to identify patterns and trends.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Explore Keras and Tensorflow tutorials
Show steps
Familiarize yourself with the Keras and TensorFlow frameworks through guided tutorials to enhance your understanding of deep learning.
Browse courses on
Deep Learning Frameworks
Show steps
Show steps
Show steps
- Follow tutorials on Keras' website to get started with model building.
- Explore TensorFlow's documentation to learn about its core concepts and capabilities.
Assist fellow students in understanding course concepts
Show steps
Enhance your understanding by explaining concepts to others and providing guidance.
Show steps
Show steps
Show steps
- Join online forums or study groups related to the course.
- Answer questions and provide insights to fellow learners.
Build a mini deep learning project
Show steps
Apply your learning by creating a small-scale deep learning project to gain hands-on experience.
Show steps
Show steps
Show steps
- Choose a simple dataset and define a problem statement.
- Design and implement a deep learning model to address the problem.
- Evaluate the performance of your model and make improvements as needed.
Attend workshops on deep learning applications in banking
Show steps
Expand your knowledge by attending workshops that showcase real-world applications of deep learning in the banking industry.
Show steps
Show steps
Show steps
- Identify and register for relevant workshops.
- Actively participate in the workshops and engage with professionals.
Organize course materials for future reference
Show steps
Enhance your ability to review and retain course content by organizing materials.
Show steps
Show steps
Show steps
- Create a structured system for storing notes, assignments, and other materials.
- Review and summarize key concepts periodically to reinforce your understanding.
Career center
Learners who complete Bank Loan Approval Prediction With Artificial Neural Nets will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. The knowledge of deep neural networks and model assessment techniques in Bank Loan Approval Prediction With Artificial Neural Nets would be highly relevant for Machine Learning Engineers interested in building and deploying models in the finance industry.
Financial Modeler
Financial Modeler
Financial Modelers use mathematical and statistical models to forecast financial performance. The knowledge of neural networks and model assessment techniques in Bank Loan Approval Prediction With Artificial Neural Nets may be useful for Financial Modelers who want to apply these techniques to financial modeling in the banking industry.
Credit Analyst
Credit Analyst
Credit Analysts assess the creditworthiness of individuals and businesses. The knowledge of neural networks and data analysis techniques taught in Bank Loan Approval Prediction With Artificial Neural Nets may be useful for Credit Analysts looking to apply these techniques in the banking industry.
Risk Manager
Risk Manager
Risk Managers assess and manage financial risks. The knowledge of neural networks and data analysis techniques taught in Bank Loan Approval Prediction With Artificial Neural Nets may be useful for Risk Managers seeking to apply these techniques in the banking industry.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. The knowledge of neural networks and data visualization techniques from Bank Loan Approval Prediction With Artificial Neural Nets may be helpful for Quantitative Analysts looking to apply these techniques in the financial sector.
Statistician
Statistician
Statisticians collect, analyze, and interpret data. The understanding of neural networks and data visualization techniques from Bank Loan Approval Prediction With Artificial Neural Nets may be helpful for Statisticians wanting to enhance their skills in data analysis and modeling in the financial sector.
Actuary
Actuary
Actuaries use mathematical and statistical models to assess risk and uncertainty. The understanding of neural networks and data visualization techniques from Bank Loan Approval Prediction With Artificial Neural Nets may be useful for Actuaries wanting to enhance their skills in risk modeling in the financial sector.
Portfolio Manager
Portfolio Manager
Portfolio Managers manage investment portfolios. The knowledge of neural networks and model assessment techniques from Bank Loan Approval Prediction With Artificial Neural Nets may be beneficial for Portfolio Managers who want to apply these techniques to portfolio management in the financial sector.
Data Engineer
Data Engineer
Data Engineers design and build the infrastructure that allows data to be stored, processed, and analyzed. The knowledge of neural networks and data analysis techniques gained from Bank Loan Approval Prediction With Artificial Neural Nets may be useful for Data Engineers who want to gain a better understanding of how these techniques can be applied in the financial sector.
Business Analyst
Business Analyst
Business Analysts analyze business processes and identify areas for improvement. The understanding of data visualization and deep learning concepts in Bank Loan Approval Prediction With Artificial Neural Nets may be helpful for Business Analysts interested in applying these techniques to business analysis in the financial sector.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. The knowledge of neural networks and model assessment techniques in Bank Loan Approval Prediction With Artificial Neural Nets may be useful for Software Engineers who want to explore opportunities in the field of financial technology.
Data Analyst
Data Analyst
Data Analysts use their command of statistics and computer science to analyze data with the goal of uncovering meaningful insights and trends. The knowledge of data analysis and deep learning algorithms taught in Bank Loan Approval Prediction With Artificial Neural Nets may be useful for Data Analysts who want to gain a better understanding of how these techniques can be applied to data analysis in the financial industry.
Financial Analyst
Financial Analyst
Financial Analysts provide advice on financial decisions, investments, and business plans. The understanding of deep neural networks and data analysis in Bank Loan Approval Prediction With Artificial Neural Nets may be useful for Financial Analysts wanting to enhance their skills in data-driven decision-making.
Data Scientist
Data Scientist
Data Scientists are responsible for developing and implementing data-driven solutions to business problems. The expertise in neural networks and machine learning gained from Bank Loan Approval Prediction With Artificial Neural Nets may be useful for Data Scientists who want to explore opportunities in the banking sector.
Investment Analyst
Investment Analyst
Investment Analysts evaluate and make recommendations on investments. The understanding of deep neural networks and data visualization techniques in Bank Loan Approval Prediction With Artificial Neural Nets may be useful for Investment Analysts wanting to enhance their skills in investment analysis in the financial sector.
For more career information including salaries, visit:
OpenCourser.com/course/qnybyu/bank
Reading list
We've selected 11 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Bank Loan Approval Prediction With Artificial Neural Nets.
Provides introductory material on deep learning for those with little background in the field. The book covers a wide range of topics necessary to develop and implement deep neural networks, including a comprehensive overview of foundational deep learning concepts, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers. The coding examples are written in Python using the popular Keras library. There are also useful appendices that go over the Python and math fundamentals needed for deep learning.
Provides a comprehensive overview of machine learning with a focus on the Python ecosystem, including detailed coverage of deep learning using Keras and TensorFlow. This book will provide useful background for this course. The book covers a wide range of topics from data preparation to model evaluation, making it a more useful reference tool than a current resource to this course.
Introduces foundational deep learning concepts and covers how to code deep neural networks in Python using Keras. It should serve as a useful supplement to this course for those with little background in the field.
Provides a comprehensive overview of deep learning with a focus on the R programming language, specifically covering deep learning libraries like Keras and TensorFlow. This book will be helpful for those who prefer to use R for data science and machine learning.
Provides a practical introduction to deep learning using the fastai library, which is built on top of PyTorch. It will be particularly useful for those with a programming background who want to quickly get started with deep learning.
Provides a comprehensive overview of interpretable machine learning, including a chapter on deep learning. This book is more helpful as background reading than as a current reference.
Provides a comprehensive overview of data science, including a chapter on deep learning. This book is more helpful as background reading than as a current reference.
Provides a comprehensive overview of feature engineering for machine learning, including a chapter on deep learning. This book is more helpful as background reading than as a current reference.
Save
Provides a comprehensive overview of machine learning for business, including a chapter on deep learning. This book is more helpful as background reading than as a current reference.
Provides a comprehensive overview of TensorFlow for deep learning. This book is more helpful as background reading than as a current reference.
Provides a comprehensive overview of machine learning with Python, including a chapter on deep learning. This book is more helpful as background reading than as a current reference.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/qnybyu/bank
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
May be particularly useful for those with intermediate skills in AI and ML, helping them to hone and strengthen their current abilities
Suitable for learners with experience in the banking industry, as it focuses on the application of AI and ML in this specific context
Involves the practical implementation of AI and ML, providing learners with hands-on experience in these fields
Relying on Python, Keras, and Tensorflow, it uses popular and widely-adopted tools and technologies in the AI and ML industry
Provides a strong foundation in the theoretical principles of Deep Neural Networks, empowering learners with a comprehensive understanding of the subject
Designed for learners in the North American region, which may limit accessibility for those outside this area
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