Artificial Intelligence Tutorial | AI Tutorial for Beginners | Artificial Intelligence | Simplilearn

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Artificial intelligence includes reactive machines, limited memory AI, theory of mind AI, and self-aware AI, achieved through machine learning and deep learning, with applications like predicting diabetes risk. The tutorial covers AI basics, TensorFlow implementation, and model training, showcasing a 71% accuracy in predicting high diabetes risk.

Insights

  • Artificial intelligence encompasses various types, from reactive machines with task-specific functions to self-aware AI aiming to replicate human consciousness, achieved through machine learning and deep learning techniques.
  • Practical applications of AI include predicting airline ticket prices, home automation with sensors, and assessing diabetes risk through data analysis and model training in platforms like TensorFlow, showcasing the diverse and impactful uses of artificial intelligence in real-world scenarios.

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Recent questions

  • What is artificial intelligence?

    It is the creation of intelligent machines like humans.

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Summary

00:00

"AI Tutorial: Types, Applications, and Implementation"

  • Artificial intelligence tutorial by Richard Kirschner from Simply Learn team
  • Introduction to artificial intelligence, types, ways of achieving it, and applications
  • Definition of artificial intelligence as creating intelligent machines like humans
  • Reactive machines are task-specific without memory, like programmable coffee makers
  • Limited memory AI uses past experiences for decisions, like self-driving cars
  • Theory of mind AI can understand human emotions, still in development
  • Self-aware AI is future, super intelligent and conscious like humans
  • Achieving AI through machine learning, using algorithms to learn from data
  • Deep learning in AI mimics human brain's neural network for pattern recognition
  • Neural network model in deep learning with input, hidden, and output layers
  • Application of AI in predicting airline ticket prices using historical data
  • AI applications in homes like sensors turning on lights and TV based on presence
  • Use case of predicting diabetes risk using features like glucose and insulin levels
  • Code implementation in TensorFlow using Jupyter notebook for data analysis and prediction
  • TensorFlow modules imported, data loaded, explored, labeled, and model evaluated for accuracy.

13:43

Enhancing Data Analysis with Pandas in Python

  • To resolve a file issue, ensure it's in the correct folder or add the folder path; import pandas for data manipulation.
  • Pandas is a dataset package for Python, enhancing data analysis with columns and headers like an Excel spreadsheet.
  • Utilize pandas to read a CSV file, ensuring it's accessible either by downloading or requesting it.
  • Input the full path for the CSV file, which is comma-separated variables, to read it into a variable named "diabetes."
  • Use "diabetes.head" to display the first five lines of data neatly in a pandas data frame.
  • Normalize specific columns by applying a lambda function to scale values between 0 and 1 for fair analysis.
  • Define columns like "number of pregnancies" as numeric for TensorFlow to understand the data.
  • Assign categorical values like "group" (A, B, C, D) using TensorFlow's categorical column feature.
  • Visualize data with a histogram using matplotlib in a Jupyter notebook, showing age distribution in 20 bins.
  • Analyze the histogram to understand age distribution trends, crucial for data interpretation and modeling.

26:31

"Creating Age Buckets and Training Models"

  • Creating age buckets to categorize people for easier organization, like at a doctor's office.
  • Specific steps in TensorFlow, setting up categories and input functions for model training.
  • Detailed process of creating an input function and model in TensorFlow, with code examples.
  • Setting parameters like number of epochs and batch size for training the model effectively.
  • Explanation of the model creation in TensorFlow using tf.estimator.linearclassifier.
  • Training the model with steps and batch size, common in neural network models.
  • Running the model to train and receive information on tensor loss and global steps.
  • Running predictions on test data to evaluate the model's performance.
  • Evaluating the model's accuracy, with an example showing an accuracy of 71% in predicting high risk of diabetes.
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