Artificial intelligence (AI) involves creating computer systems that perform tasks requiring human-like intelligence, such as learning and decision-making. It can be categorized into narrow AI, designed for specific tasks, and general AI, which is theoretical and would replicate human intelligence across various tasks. Machine learning, a subset of AI, focuses on algorithms that allow computers to learn from data, improving performance over time without explicit programming.

1. Artificial Intelligence and
Machine Learning

2. Artificial Intelligence
Artificial Intelligence (AI) refers to the development
of computer systems capable of performing tasks
that typically require human intelligence. These tasks
include learning, reasoning, problem-solving,
perception, understanding natural language, and
even interacting with the environment. AI
encompasses a broad range of techniques, including
machine learning, natural language processing,
computer vision, robotics, and more. The goal of AI
is to create systems that can replicate, supplement,
or even surpass human intelligence in various
domains, leading to advancements in fields such as
healthcare, finance, transportation, and
entertainment.
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3. Goals of AI
1. Automation: AI aims to automate repetitive or
mundane tasks, freeing up human labor for more
creative and strategic endeavors.
2. Decision Making: AI systems strive to make intelligent
decisions based on data and reasoning, often surpassing
human capabilities in terms of speed and accuracy.
3. Problem Solving: AI is utilized to tackle complex
problems that may be too difficult or time-consuming
for humans to solve alone, such as optimizing logistics
networks or designing new drugs.
4. Understanding and Communication: AI seeks to
enable machines to understand human language, both
written and spoken, and to communicate effectively
with humans.

4. 5. Learning and Adaptation: AI systems aim to learn from
data and experiences, continually improving their
performance over time and adapting to new situations and
environments.
6. Prediction and Forecasting: AI is employed to analyze data
and make predictions about future events or trends, helping
businesses and organizations make informed decisions.
7. Creativity and Innovation: AI explores the boundaries of
creativity by generating novel ideas, designs, and artworks,
often in collaboration with human creators.
8. Assistance and Support: AI technologies aim to assist and
support humans in various tasks, ranging from personal
assistants like chatbots to medical diagnosis systems and
autonomous vehicles.

5. • Two Types of Artificial
Intelligence
Narrow AI (Weak AI) and
General AI (Strong AI)
1. Narrow AI (Weak AI) : Narrow AI refers to AI systems that are
designed and trained for a specific task or a narrow set of tasks.
These systems excel at performing predefined tasks within a
limited domain, but they lack general human-like intelligence.
2. General AI (Strong AI):General AI, also known as Strong AI or
Artificial General Intelligence (AGI), refers to AI systems that
possess human-like intelligence and can understand, learn, and
apply knowledge in a manner similar to humans.
These systems have the ability to generalize across different
tasks, learn from experience, and adapt to new situations
autonomously.General AI remains largely theoretical and is yet
to be achieved. It would be capable of performing any
intellectual task that a human being can.

6. How AI Works
AI works through a combination of algorithms, data, and
computing power to simulate human-like intelligence and
perform tasks that would typically require human
intervention. Here's a simplified overview of how AI works:
1. Data Collection: AI systems rely on vast amounts of data to
learn and make decisions. This data can come from various
sources, including sensors, databases, text documents,
images, videos, and more.
2. Data Preprocessing: Before feeding the data into AI
algorithms, it often needs to be cleaned, organized, and
preprocessed to remove noise, handle missing values, and
standardize formats. This step ensures that the data is
suitable for analysis.
3. Algorithm Selection: Depending on the task at hand,
different AI algorithms may be employed. For example:

7. Machine Learning algorithms, such as neural networks, decision trees, support
vector machines, and random forests, are used for tasks like classification,
regression, clustering, and pattern recognition.
Deep Learning algorithms, a subset of machine learning, are particularly effective for
tasks involving unstructured data like images, audio, and text. They often involve
complex neural network architectures inspired by the human brain.
Rule-based systems use predefined rules and logical reasoning to make decisions or
perform tasks. These are commonly used in expert systems and knowledge-based
AI.

8. 4. Model Training: In supervised learning, the AI model is trained on
labeled data, where the input and the corresponding output are provided.
During training, the model adjusts its parameters to minimize the difference
between its predictions and the actual outputs. In unsupervised learning,
the model learns patterns and structures from unlabeled data.
Reinforcement learning involves learning through trial and error based on
feedback from the environment.
5. Model Evaluation: After training, the AI model is evaluated using
separate test data to assess its performance. Metrics such as accuracy,
precision, recall, and F1 score are often used to measure the model's
effectiveness.
6. Deployment: Once the AI model has been trained and evaluated, it can
be deployed to perform real-world tasks. Deployment may involve
integrating the AI system into existing software applications, devices, or
platforms.
7. Continuous Learning and Improvement: AI systems can be designed to
learn continuously from new data and experiences, allowing them to adapt
and improve over time. This may involve periodically retraining the model
with updated data or implementing online learning algorithms that can
incorporate new information in real-time.

9. Machine Learning
Machine learning is a subset of artificial intelligence (AI)
that focuses on developing algorithms and techniques
that enable computers to learn from data and make
predictions or decisions without being explicitly
programmed to perform specific tasks. In other words,
machine learning algorithms learn patterns and
relationships from data and use that knowledge to
make informed decisions or predictions.
Machine learning plays a crucial role in various fields,
including healthcare, finance, e-commerce,
cybersecurity, and more, by enabling computers to
learn from data and make intelligent decisions
autonomously.

10. Key Types of
Machine Learning
• Supervised Learning: Learning from labeled data.
• Unsupervised Learning: Finding patterns in
unlabeled data.
• Reinforcement Learning: Learning through trial
and error, maximizing rewards
Machine learning allows systems to improve their
performance over time by learning patterns from
data. Supervised learning works with input-output
pairs, unsupervised finds hidden structure in data,
and reinforcement learning uses a system of
rewards to optimize behavior.

11. Machine Learning
Process
• Data Collection and Preparation: Gathering and
cleaning relevant data.
• Feature Engineering: Selecting and transforming
the most significant data attributes.
• Model Selection: Choosing an appropriate ML
algorithm.
• Training: Feeding data into the model to adjust its
parameters.
• Evaluation: Measuring model performance on
unseen data.
• Deployment: Integrating the model into a real-
world application

12. Applications of
Machine
Learning
• Computer Vision: Analyzing and
Understanding images and videos.
• Natural Language Processing (NLP):
Interactions with human language
(translation, chatbots)
• Robotics: Designing and controlling
robots for automation.
• Healthcare: Diagnostics, drug
discovery, personalized treatment

13. How AI and
ML are
different ?
AI (Artificial Intelligence) is a broad field of computer
science focused on creating machines that can perform
tasks that would typically require human intelligence.
AI encompasses various approaches, including symbolic
reasoning, expert systems, natural language processing,
computer vision, and robotics, among others.
ML (Machine Learning) is a subset of AI that specifically
deals with algorithms and techniques that enable
computers to learn from data and make predictions or
decisions without being explicitly programmed.
ML primarily relies on statistical techniques to analyze
data, identify patterns, and make predictions or decisions.
Common ML techniques include supervised learning,
unsupervised learning, semi-supervised learning,
reinforcement learning, and deep learning.

14. Artificial intelligence course in Chandigarh
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