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Artificial Intelligence (AI) in Healthcare Course
Artificial Intelligence (AI) in Healthcare Course
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2615 Learners4.4 1050 Ratings
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Live Online Classes starting on 25th May 2024
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Instructor-led AI in Healthcare live online Training Schedule
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Price 16,99915,299
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Why enroll for Artificial Intelligence (AI) in Healthcare Course?
Artificial Intelligence (AI) in Healthcare Course Benefits
The market for generative AI is projected to reach $1.3 trillion by 2032, and AI is predicted to displace 85 million jobs globally by 2025 according to Bloomberg and the World Economic Forum. Enrolling in this course is a strategic move for professionals in healthcare to tackle challenges, expand career opportunities, and stay competitive. The course supports professional and personal growth, equipping learners with tools to keep updated on AI in healthcare and drive positive change at the intersection of technology and healthcare.
Annual Salary
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Want to become a Clinical Data Scientist?
Annual Salary
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Want to become a Clinical Data Scientist?
Annual Salary
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Want to become a Clinical Data Scientist?
Why Artificial Intelligence (AI) in Healthcare Course from edureka
Live Interactive Learning
- World-Class Instructors
- Expert-Led Mentoring Sessions
- Instant doubt clearing
Lifetime Access
- Course Access Never Expires
- Free Access to Future Updates
- Unlimited Access to Course Content
24x7 Support
- One-On-One Learning Assistance
- Help Desk Support
- Resolve Doubts in Real-time
Hands-On Project Based Learning
- Industry-Relevant Projects
- Course Demo Dataset & Files
- Quizzes & Assignments
Industry Recognised Certification
- Edureka Training Certificate
- Graded Performance Certificate
- Certificate of Completion
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About your Artificial Intelligence (AI) in Healthcare Course
Artificial Intelligence (AI) in Healthcare Course Skills Covered
Tools Covered in Artificial Intelligence (AI) in Healthcare Course
Artificial Intelligence (AI) in Healthcare Course Curriculum
Curriculum Designed by Experts
Leveraging Generative AI in Healthcare
10 Topics
Topics
- Introduction to Generative AI
- Applications of Generative AI
- How healthcare professionals use Generative AI
- Why AI is needed in Healthcare
- Examples of AI in Healthcare
- Real-world case studies
- Challenges and Limitations
- Data Privacy and Security
- Generative AI: Benefits and Risks
- Ethical Considerations
![skill]( "Hands On Experience")
- Implementing Generative AI Use Cases
![skill]( "skill you will learn")
- Generative AI Fundamentals
- Ethical Insights into Generative AI
- Importance of Data Privacy and Security
- Balancing Benefits and Risks
Deep Learning
17 Topics
Topics
- What is Neuron
- What is Deep Learning
- What is ANN
- What is keras
- Introduction to Pandas
- Data Visualization with Pandas
- Data Preprocessing by Pandas
- Important terms in Neural Network
- What is Activation Function
- What is sigmoid function
- What is tanh function
- What is Rectified Linear Unit function
- What is Leaky ReLU function
- What is The Exponential Linear Unit Function
- What is The Swish function
- What is The softmax function
- Decoding all the activation functions
![skill]( "Hands On Experience")
- Data Visualization
- Data Preprocessing
- Activation Function Analysis in Neural Network Image Classification
![skill]( "skill you will learn")
- Neural Network Concepts
- Visualization Techniques
- Data Preprocessing
- Activation Function Understanding
- Neural Network Implementation
Predictive Model for Healthcare Diagnosis
24 Topics
Topics
- Problem Analysis
- Understanding the Library and Data Importation
- Data Analysis
- Data Preprocessing
- Handling Missing Data
- Splitting Data into Training, Testing, and Validation Sets
- Data Visualization
- Building Initial Model
- Evaluation Metrics: Confusion, Precision, and Recall
- One-hot Encoding
- Understanding Response Encoding Theory
- Implementing Response Encoding
- Evaluating Variation Column
- Evaluating Text Column
- Data Preparation for Machine Learning Models
- Feature Combination
- Naive Bayes Implementation
- K Nearest Neighbour Classification
- Logistic Regression Implementation
- Linear Support Vector Machines
- Random Forest with Response Coding
- Random Forest Classifier Implementation
- Stacking Model Implementation
- Maximum Voting Classifier
![skill]( "Hands On Experience")
- Building a predictive model to assist in diagnosing health
![skill]( "skill you will learn")
- Data Analysis
- Data Preprocessing
- Machine Learning Modeling
- Model Evaluation
- Feature Engineering
- Data Visualization
- Model Selection
Predicting Diabetes with Multilayer Perceptron
13 Topics
Topics
- Introduction to Multilayer Perceptron (MLP)
- Structure and Architecture of Multilayer Perceptron
- Backpropagation Algorithm for Training MLP
- Feature Selection and Data Preparation for Diabetes Prediction
- Building a Multilayer Perceptron Model for Diabetes Prediction
- Training and Tuning Hyperparameters of MLP
- Evaluating Model Performance: Accuracy, Precision, Recall, and F1 Score
- Handling Imbalanced Data in Diabetes Prediction
- Interpretability and Explainability of MLP Models
- Visualizing Model Predictions and Decision Boundaries
- Optimizing MLP Model for Real-world Deployment
- Ethical Considerations in Diabetes Prediction Using MLP
- Case Studies and Real-world Applications of MLP in Diabetes Prediction
![skill]( "Hands On Experience")
- Building a Multilayer Perceptron Model for Diabetes Prediction
![skill]( "skill you will learn")
- Understanding of Multilayer Perceptron (MLP)
- Data Preparation and Feature Selection
- Building and Training MLP Models
- Model Evaluation and Performance Metrics
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Certified Artificial Intelligence (AI) in Healthcare Course Details
About Artificial Intelligence (AI) in Healthcare Course
The Artificial Intelligence (AI) in Healthcare course offers a comprehensive exploration of how AI technologies are transforming the healthcare industry. Learners acquire the knowledge of generative AI's uses, ethical issues, and practical application in the healthcare sector. They also delve into Deep Learning, where they learn about neural networks, activation functions, and techniques for preprocessing data. Before moving on to predictive modeling for healthcare diagnosis, the course first covers data analysis, preprocessing, and the application of various machine learning algorithms. Finally, the learners will learn about the model building and ethical concerns associated with Multilayer Perceptron (MLP) and its application in diabetes prediction. Learners in the course acquire fundamental skills in data analysis, preprocessing, machine learning modeling, model evaluation, and ethical decision-making, which are necessary for using AI in healthcare settings.
What are the learning outcomes of this Artificial Intelligence (AI) in Healthcare Course?
Upon completing the Artificial Intelligence (AI) in Healthcare course, participants will attain the following learning outcomes:
- Comprehensive Understanding: Gain a deep understanding of AI technologies and their applications in healthcare, including Generative AI, Deep Learning, and predictive modeling.
- Practical Skills: Develop practical skills in data analysis, preprocessing, machine learning modeling, and model evaluation through hands-on exercises and real-world case studies.
- Ethical Considerations: Understand the ethical implications of AI in healthcare, including data privacy, security, and the responsible use of AI algorithms for patient care.
- Effective Implementation: Learn how to effectively implement AI techniques in healthcare settings to improve patient outcomes, streamline processes, and optimize healthcare delivery.
- Critical Thinking: Develop critical thinking skills to evaluate the benefits, risks, and limitations of AI technologies in healthcare and make informed decisions.
Why take up this Artificial Intelligence (AI) in Healthcare Course?
Enrolling in the Artificial Intelligence (AI) in Healthcare Course is a strategic decision for professionals seeking to address critical healthcare challenges, expand their career opportunities, and stay competitive in today's job market. Learners can unlock a variety of career paths, support healthcare innovation, and improve patient care and outcomes by becoming experts in AI technologies tailored to the healthcare industry. This course empowers people to drive positive change at the nexus of technology and healthcare while staying up to date on advancements in AI and its applications in the field.
Who should take up this Artificial Intelligence (AI) in Healthcare Course?
This course is tailored for:
- Healthcare Data Scientist
- AI Research Scientist
- Clinical Informatician
- Healthcare AI Engineer/Developer
- Medical Image Analyst
- Clinical Data Analyst
- Healthcare AI Consultant
- Healthcare AI Product Manager
- Clinical Decision Support Specialist
- Healthcare AI Ethicist
What are the prerequisites for this Artificial Intelligence (AI) in Healthcare Course?
There are no prerequisites required for this Artificial Intelligence (AI) in Healthcare course. However, it is beneficial to have:
- Basic Python Programming Knowledge
- Problem Solving Skills
- Fundamentals of Machine Learning
What are the system requirements for this Artificial Intelligence (AI) in Healthcare course?
The system requirements for a Artificial Intelligence (AI) in Healthcare course include:
- A laptop or desktop computer with a minimum of 8 GB RAM with Intel Core-i3 and above processor to run NLP and machine learning models is required.
- Stable and high-speed internet connection is necessary for accessing online course materials, videos, and software.
How will I execute the practicals in this Artificial Intelligence (AI) in Healthcare Course?
Practicals for this course will be implemented using Python, VS Code and Jupyter Notebook. Detailed step-by-step installation guides are available on the LMS. In case you come across any doubt, the 24*7 support team will promptly assist you.
Artificial Intelligence (AI) in Healthcare Course Projects
Building a Predictive Model to assist in Diagnosing Health
In healthcare, timely and accurate diagnosis plays a crucial role in patient care and treatment planning. However, because medical data is complex and human error can occur, diag....Building a Multilayer Perceptron Model for Diabetes Prediction
Diabetes is a chronic health condition characterized by high blood sugar levels, which can lead to serious complications if not managed effectively. Improving patient outcomes an....Artificial Intelligence (AI) in Healthcare Course Certification
To unlock the Edureka’s Artificial Intelligence (AI) in Healthcare course completion certificate, you must ensure the following:
- Completely participate in this Artificial Intelligence (AI) in Healthcare Course.
- Evaluation and completion of the quizzes and projects listed.
An Artificial Intelligence (AI) in Healthcare certification offers professionals a comprehensive pathway to enhance their expertise in leveraging AI technologies for healthcare applications. Through the completion of a certification program, individuals can enhance their professional skills in machine learning algorithms, data analysis, and healthcare informatics. This can lead to career advancement in roles such as clinical informatician, data scientist, AI researcher, or healthcare AI engineer. A certification shows a person's commitment to staying up to date with the latest developments and best practices in healthcare AI, as well as validating their knowledge and proficiency.Gaining this certification increases your marketability and credibility, opening doors for career advancement in AI-related roles across the healthcare industry.
After completing the Artificial Intelligence (AI) in Healthcare Certification, you will qualify for various job opportunities in the artificial intelligence field. These positions encompass roles such as:
- Healthcare Data Scientist
- AI Research Scientist
- Clinical Informatician
- Healthcare AI Engineer/Developer
- Medical Image Analyst
- Clinical Data Analyst
- Healthcare AI Consultant
- Healthcare AI Product Manager
- Clinical Decision Support Specialist
- Healthcare AI Ethicist
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Artificial Intelligence (AI) in Healthcare Course FAQs
What is the importance of AI in healthcare?
AI has the potential to revolutionize healthcare by improving diagnostic accuracy, personalizing treatment plans, optimizing resource allocation, and enhancing patient outcomes.
Do I need prior experience in AI or healthcare to enroll in this course?
While prior experience in either AI or healthcare is beneficial, the course is designed to accommodate learners with varying backgrounds. Basic knowledge of programming and fundamentals of machine learning is recommended.
Will I get placement assistance after completing this Artificial Intelligence (AI) in HealthcareCourse?
To help you in this endeavor, we have added a resume builder tool in your LMS. Now, you will be able to create a winning resume in just 3 easy steps. You will have unlimited access to use these templates across different roles and designations. All you need to do is, log in to your LMS and click on the "create your resume" option.
Who are the instructors for Artificial Intelligence (AI) in Healthcare Course?
All the instructors at edureka are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants.
What if I have more queries after completion of Artificial Intelligence (AI) in Healthcare Course?
Just give us a CALL at +91 98702 76459/1844 230 6365 (US Tollfree Number) OR email at sales@edureka.co.
What if I miss a class of Artificial Intelligence (AI) in Healthcare Course?
You will never miss a lecture at Edureka! You can choose either of the two options:
- View the recorded session of the class available in your LMS.
- You can attend the missed session, in any other live batch.
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