Introduction
This comprehensive book will provide you with an in-depth exploration of artificial intelligence (AI) systems and how they can be applied to scientific research. Through this book, you will gain a clear understanding of both the theoretical concepts behind AI and its practical applications.
In this book , we will cover topics such as machine learning algorithms, deep learning, natural language processing, computer vision, robotic process automation and more. We will also guide you through examples of real-world use cases in scientific research fields including astronomy, biology, data science, physics, and others. Alongside our training materials and lectures about these technical topics, we will also provide insights into the ethical considerations that come with using AI for scientific research purposes.
By the end of the book, you should have gained an understanding of the fundamentals of AI in scientific research and be well-positioned to implement these tools for your own work or projects.
Objectives
- Understand the fundamentals of Artificial Intelligence and its applications in scientific research.
- Identify different types of AI tools, techniques, and algorithms applicable to scientific research projects.
- Develop an understanding of various AI systems such as Machine Learning, Neural Networks, Natural Language Processing and Deep Learning Technologies.
- Learn how to select appropriate AI technologies for a given research problem and develop solutions using those technologies.
- Utilize modern tools and frameworks such as Scikit-Learn, TensorFlow, Keras, PyTorch etc., to build effective solutions for scientific challenges utilizing machine learning models and deep learning networks.
- Evaluate existing AI-based solutions to ensure quality outcomes of your results are achieved with accuracy in real-time scenarios.
- Develop an understanding on how AI resources can be used efficiently through effective utilization of cloud computing services such as Amazon Web Services or Google Cloud Platform etc.,
- Apply best practices in data preprocessing techniques such as feature engineering/ scaling / normalization/ dimensionality reduction/ selection etc., prior to modeling using ML algorithms or DL networks for generating actionable insight from data sets associated with scientific research problems and domains
Course Outline
Module 1: Introduction to AI for Scientific Research
-Overview of artificial intelligence (AI) concepts and its potential applications in scientific research
– Fundamentals of AI in scientific research
-Fundamentals of Artificial Intelligence and its applications in scientific research.
-Basics of machine learning and it uses in scientific research projects
– Basics of deep learning and it uses in scientific research projects
– Basics of natural language processing and it uses in scientific research projects
– Basics of reinforcement learning and it uses in scientific research projects
– Basics of robotics and it uses in scientific research projects
– Basics of computer vision and it uses in scientific research projects
– Basics of neural networks and it uses in scientific research project
-Understanding the importance of supervised problems algorithms to implement AI-based solutions to various problems
Understanding the importance of unsupervised problems algorithms to implement AI-based solutions to various problems.
Module 2: AI and algorithms applications
-Different types of AI tools Techniques, and algorithms applicable to scientific research projects.
Module 3: Utilize modern tools and frameworks.
– Scikit-Learn to build effective solutions for scientific challenges
-TensorFlow to build effective solutions for scientific challenges
-Keras to build effective solutions for scientific challenges
-PyTorch to build effective solutions for scientific challenges
-Utilizing machine learning models and deep learning networks.
Module 4: Types of AI Projects in Scientific Research
– Examples of how AI can be applied to a variety of scientific research tasks such as data analysis, pattern recognition, object detection etc.
– Identification of suitable types of machine learning models based on specific scientific research problems.
– Understanding the key design considerations while creating an AI model tailored towards specific objectives.
– AI technologies for a given research problem and develop solutions using those technologies
Module 5: Developing an AI Model for Scientific Research
-Understanding data pre-processing steps required prior to model development.
-Implementing different supervised/unsupervised algorithms along with feeding appropriate hyperparameters
– Using frameworks such as Keras/TensorFlow for model development and debugging
-How AI resources can be used efficiently through effective utilization of cloud computing services such as Amazon Web Services or Google Cloud Platform
-Best practices in data preprocessing techniques such as feature engineering/ scaling / normalization/ dimensionality reduction/ selection etc.
-Prior to modeling using ML algorithms
-DL networks for generating actionable insight from data sets associated with scientific research problems and domains
Module 6: Training & Evaluation Tactics for Your Model
– Examining techniques such as cross validation to maximize model accuracy and reduce overfitting issues
– Selection metrics best suited per the type of task being performed (accuracy score vs means squared error)
-Optimizing parameters including hidden layers’ size, activation functions optimization strategies etc.
Module 7: Exams
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