Artificial Intelligence Tutorial | AI Tutorial for Beginners | Artificial Intelligence | Simplilearn
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Introduction
Welcome to the Artificial Intelligence (AI) tutorial! My name is Richard Kirschner, and I'm part of the Simplilearn team. Today, we are going to explore the fascinating world of AI, covering its definition, various types, methods for achieving it, and some real-world applications. Finally, we will dive into a hands-on use case to predict whether a person has diabetes using TensorFlow in a Python environment.
What is Artificial Intelligence?
Artificial Intelligence is a branch of computer science dedicated to creating intelligent machines that can perform tasks that would typically require human intelligence. These AI systems work and react like humans, making this a thrilling area of research and development.
Imagine a robot that can bring you a cup of coffee—seems outrageous, right? However, we already have automatic coffee pots and delivery devices like those from Amazon that showcase how close we are to this reality. AI is everywhere in today's world, driving innovations that enhance our lives.
Types of Artificial Intelligence
When we talk about AI, we categorize it into four main types:
Reactive Machines: These are the most basic types of AI that respond to specific inputs but do not have memory or the ability to learn from past experiences. For instance, traditional washing machines or programmable coffee makers fall into this category.
Limited Memory: This type uses past experiences and present data to make decisions. Self-driving cars exemplify limited memory AI, as they rely on data collected from past incidents to inform their actions.
Theory of Mind: This remains a theoretical concept as machines capable of socializing and understanding human emotions are still in development.
Self-Awareness: The future of AI lies in developing machines that can be sentient and conscious—resembling human thought processes. This type of AI is still a topic of ongoing research.
Achieving Artificial Intelligence
Today's primary arena for achieving AI is through Machine Learning. This method enables AI systems to learn from data using algorithms to identify patterns and generate insights. A subcategory known as Deep Learning mimics the neural network of the human brain, allowing AI to process complex data.
We also analyzed how a neural network works, highlighting the roles of input, hidden, and output layers in segregating features, such as identifying various categories in a pile of photographs.
Applications of Artificial Intelligence
AI is rapidly being integrated into a variety of applications that enhance daily life. For example, smart home devices can detect presence, enabling automated light switches and voice-activated TVs. These instances exemplify AI's real-time understanding and autonomous functioning.
Use Case: Predicting Diabetes Risk
Now, let's explore a particular case where we aim to predict if a person is at risk for diabetes. We begin with a statement to predict diabetes and move on to identify the key features to analyze, such as:
- Number of pregnancies
- Glucose concentration
- Blood pressure
- Age
- Insulin level
We then proceed with some code in Jupyter Notebook, imported along with essential libraries like Pandas and TensorFlow.
After data exploration and cleaning, we normalized the data and defined the features in TensorFlow to prepare for training the model, specified by the number of epochs and batch sizes.
Post-training, we proceed to predict diabetes risk using test data, allowing us to evaluate the model's accuracy once predictions are made. The resultant accuracy of the model was approximately 71%, showcasing its potential effectiveness as a predictive tool in healthcare.
Conclusion
In conclusion, we've covered the fundamentals of artificial intelligence—from its definitions, types, and how it is achieved, to hands-on coding with TensorFlow. Thanks for joining us today on this AI journey. For more information, visit Simplylearn.
- What was the main conclusion from the diabetes prediction use case?
- The diabetes prediction model achieved an accuracy of approximately 71%, showcasing its effectiveness in identifying individuals at risk for diabetes.
