Welcome to this tutorial where we will be diving into the world of Artificial Intelligence (AI), learning about its key concepts, and understanding its terminology. This tutorial aims to make you familiar with the basics of AI and give you a solid foundation for further learning.
What you will learn:
- Different types of AI
- Understanding Machine Learning
- Key AI concepts and terminology
Prerequisites:
- Basic knowledge of programming (in any language)
- Understanding of basic math and statistics
AI is broadly classified into two types - Narrow AI and General AI.
Narrow AI: These are systems designed to perform a narrow task (e.g., facial recognition, voice command, etc.) and cannot outperform humans in tasks they're not specifically designed for.
Example: Siri, Alexa, Google Assistant, etc.
General AI: These systems can perform any intellectual task that a human being can. They can understand, learn, adapt, and implement knowledge in a wide array of tasks.
Example: Data analysis, problem-solving, complex decision making, etc.
Machine Learning (ML) is a subset of AI that focuses on the development of computer programs that can learn from and make decisions or predictions based on data.
Example: Netflix's recommendation system, Facebook's news feed, etc.
There are three types of machine learning - supervised learning, unsupervised learning, and reinforcement learning.
Here we'll use Python's scikit-learn library to create a simple linear regression model.
# Import necessary libraries
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import metrics
import pandas as pd
# Load dataset
dataset = pd.read_csv('student_scores.csv')
# Split data into 'attributes' and 'labels'
X = dataset['Hours'].values.reshape(-1,1)
y = dataset['Scores'].values.reshape(-1,1)
# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
# Train the algorithm
regressor = LinearRegression()
regressor.fit(X_train, y_train)
# Make predictions
y_pred = regressor.predict(X_test)
# Compare actual output values with predicted values
df = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten()})
print(df)
In this tutorial, we've learned about various types of AI, delved into machine learning, and understood key AI concepts and terminologies. We've also seen a practical code example of a simple machine learning model.
Next steps:
- Deepen your understanding of machine learning algorithms
- Learn about deep learning
- Start a project to apply your knowledge
Additional resources:
- Machine Learning Course by Andrew Ng
- Deep Learning Specialization
Exercise 1:
- Create a simple machine learning model using any dataset of your choice.
Exercise 2:
- Read about deep learning and summarize the key points
Solutions:
- Solutions to these exercises will depend on the dataset chosen and the understanding of deep learning.
Tips for further practice:
- Work on real-world datasets
- Participate in machine learning competitions on platforms like Kaggle.