Artificial Intelligence Tutorial for Beginners in 2024 | Learn AI Tutorial from Experts

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In 2023, the global AI market was nearly $208 billion and is expected to exceed $1 trillion by 2028, growing 40% each year? 

By 2030, it could reach almost $2 trillion.

As a beginner, navigating this booming field can be challenging, but our AI tutorial for beginners will guide you step-by-step. In this blog, you’ll find everything from the basics of AI to the OpenAI software development and programming tutorial you need to start your AI journey in 2024.

• What is Artificial Intelligence?
• History of Artificial Intelligence
• Why is artificial intelligence (AI) being adopted by companies worldwide?
• Levels of Artificial Intelligence
• Best Applications of Artificial Intelligence in 2024
• Goals of Artificial Intelligence
• Jobs in Artificial Intelligence
• Advantages of Artificial Intelligence
• Disadvantages of Artificial Intelligence
• A simple Artificial Intelligence mini-project
• Future of Artificial Intelligence

What Is Artificial Intelligence?

Artificial Intelligence (AI) is a branch of computer science that focuses on creating systems capable of performing tasks that typically require human intelligence. These tasks include:

• Learning: Acquiring information and rules for using the information
• Reasoning: Using rules to reach approximate or definite conclusions
• Problem Solving: Finding solutions to complex issues
• Perception: Interpreting sensory data to understand the environment
• Language Understanding: Processing and responding to natural language

AI systems achieve these capabilities through various techniques, such as machine learning, deep learning, and neural networks.

These technologies enable machines to learn from experience, adapt to new inputs, and perform human-like tasks.

History Of Artificial Intelligence

The history of Artificial Intelligence (AI) stretches back to ancient myths and early automatons created by Greek, Chinese, and Egyptian cultures. 

Modern AI began taking shape in the 1940s and 50s with the concept of an artificial brain, leading to its establishment as an academic discipline at the 1956 Dartmouth Conference, where the term “Artificial Intelligence” was coined.

• Early Beginnings (1940s-1950s)- Alan Turing’s Bombe machine cracked the Enigma code in 1942, and he proposed the Turing Test in 1950 to assess machine intelligence.

• Industrial and Chatbot Developments (1960s)- General Motors used the first industrial robot, Unimate, in 1961, and Joseph Weizenbaum created the first chatbot, Eliza, in 1964.

• Advances and Challenges (1970s-1980s)-Japan developed the first anthropomorphic robot, and due to slow progress, the AI Winter of the 1980s saw reduced funding and interest.

• Resurgence and Breakthroughs (1990s-2000s)- IBM’s Deep Blue defeated chess champion Garry Kasparov in 1997, and IBM Watson excelled in natural language processing in the 2000s.

• Recent Developments (2010s-present)- Amazon’s Alexa revolutionized smart devices in 2014, the humanoid robot Sophia was granted citizenship in 2017, and GPT-3 advanced human-like text generation in 2020.

These milestones highlight AI’s evolution, marked by significant advancements in computing, machine learning, and robotics, shaping its integration into modern life.

Why Is Artificial Intelligence Adopted By Companies Worldwide?

Artificial Intelligence (AI) is rapidly adopted by companies worldwide due to its transformative potential in enhancing efficiency, productivity, and customer experience. 

AI technologies offer significant advantages that drive its growing demand and widespread adoption:

• Cost Reduction: IBM reports that AI-powered chatbots can reduce customer service costs by up to 30%. This cost efficiency is a major incentive for businesses to implement AI solutions.

• Enhanced Customer Experience: 51% of eCommerce companies use AI to provide seamless and personalized customer experiences, increasing satisfaction and loyalty.

• Economic Impact: AI has the potential to boost economies significantly. It is estimated that AI could contribute close to $500 billion to the GDP by 2025, showcasing its vast economic benefits.

• Productivity Gains: AI improves productivity by automating repetitive tasks. According to PWC, AI is expected to enhance productivity by 40% by 2035, allowing businesses to focus on more strategic activities.

• Data Accuracy: To ensure accuracy and minimize errors, 48% of businesses optimize machine learning (ML), data analysis, and other AI tools. This helps maintain high-quality data and informed decision-making.

These compelling benefits illustrate why AI is integral to business strategies across various industries.

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Levels of Artificial Intelligence

1. Artificial Narrow Intelligence (ANI)

Also known as weak AI, ANI is designed for tasks like chatbots and speech recognition (e.g., Siri, Alexa). It uses natural language processing and deep learning to enhance user experience but doesn’t mimic human intelligence.

• Siri – Apple’s voice-activated assistant for iOS devices
• Alexa – Amazon’s virtual assistant used in Echo devices
• Cortana – Microsoft’s virtual assistant for Windows

2. Artificial General Intelligence (AGI)

Also known as Strong AI, AGI can understand, learn, and apply intelligence across a broad range of tasks, similar to a human being. It can solve problems and adapt to new situations.

• Sophia the Robot: A humanoid robot by Hanson Robotics designed to mimic human expressions and conversations.
• GPT-3: OpenAI’s language model can generate human-like text and understand complex language prompts.

3. Artificial Super-intelligence

ASI surpasses human intelligence in all aspects, including creativity, problem-solving, and decision-making. This level of AI remains a concept in the future, representing a scenario where machines surpass human intellectual capabilities.

• Skynet – The self-aware AI from the “Terminator” series.
• The Matrix AI – The controlling intelligence in “The Matrix” film series.

Best Applications of Artificial Intelligence in 2024

Here are the top industries and application areas of artificial intelligence in 2024

1. Healthcare

• AI is revolutionizing healthcare with applications such as predictive analytics for disease diagnosis and prognosis, personalized treatment plans based on patient data, and medical imaging analysis for early detection of conditions like cancer.

• Virtual health assistants and AI-powered telemedicine platforms facilitate remote patient monitoring, virtual consultations, and efficient healthcare delivery, especially in underserved areas.

2. Autonomous Vehicles

• Self-driving cars equipped with AI-powered perception, decision-making, and navigation systems are becoming increasingly sophisticated, enhancing road safety, reducing accidents, and enabling more efficient transportation.

• Advanced driver-assistance systems (ADAS) utilize AI algorithms for features like adaptive cruise control, lane-keeping assistance, and collision avoidance, augmenting driver capabilities and reducing human error.

3. Finance

• AI algorithms are used in finance for fraud detection, risk assessment, and algorithmic trading. They leverage vast amounts of data to identify real-time patterns, anomalies, and market trends.

• Robo-advisors employ AI-based algorithms to provide personalized investment advice, portfolio management, and financial planning services tailored to individual preferences and goals.

4. Retail

• AI-powered recommendation engines analyze customer data, purchase history, and browsing behavior to provide personalized product recommendations, enhance cross-selling and upselling opportunities, and improve customer engagement.

• Predictive analytics algorithms forecast demand, optimize inventory management, and inform pricing strategies, reducing stockouts, minimizing wastage, and maximizing profitability.

5. Entertainment

• AI algorithms power content recommendation engines on streaming platforms, personalized playlists on music apps, and tailored content curation on social media enhancing user engagement and satisfaction.

• AI-driven algorithms enhance virtual reality (VR) and augmented reality (AR) experiences through immersive storytelling, interactive gaming, and realistic simulations, transforming entertainment experiences for users across various platforms.

6. Education

• AI-powered adaptive learning platforms personalize educational content and learning experiences based on individual student progress, preferences, and learning styles, improving learning outcomes and engagement.

• Virtual tutors and intelligent tutoring systems utilize AI algorithms to provide real-time feedback, guidance, and support to students. They facilitate self-paced learning and effectively address individual learning needs.

Goals of Artificial Intelligence

Artificial Intelligence (AI) aims to achieve several key goals, each contributing to its overall ability to mimic and enhance human intelligence:

• Problem-Solving
  AI systems are designed to tackle complex issues by efficiently identifying solutions. This involves algorithms that can analyze data, recognize patterns, and propose optimal solutions in various fields, such as healthcare, finance, and logistics.

• Knowledge Representation
  AI aims to represent information about the world in a form that a computer system can utilize to solve complex tasks. This includes databases, ontologies, and semantic networks that allow machines to understand and process human knowledge.

• Planning
  AI systems develop strategies to achieve specific objectives by anticipating and evaluating different scenarios. This includes scheduling tasks, managing resources, and predicting outcomes to ensure successful completion of goals.

• Learning
  AI strives to improve its performance over time through learning algorithms. This includes machine learning techniques where systems can learn from data, adapt to new information, and improve accuracy without being explicitly programmed for each task.

• Social Intelligence
  AI aims to interact effectively with humans by understanding and responding to social cues. This includes natural language processing, emotion recognition, and engaging in human-like conversations, enhancing user experience and collaboration.

• Perception
  AI systems aim to interpret and understand sensory data from the world around them. This involves image and speech recognition technologies that enable machines to comprehend and respond to visual and auditory information.

Together, these goals drive the development of AI technologies, enabling machines to perform tasks that enhance human capabilities and transform various industries.

Jobs in Artificial Intelligence

1. Machine Learning Engineer

Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models and systems. They work on algorithms and models that enable machines to learn from data and make predictions or decisions autonomously.

Key Responsibilities

• Designing and implementing machine learning algorithms and models.
• Developing data pipelines for training and testing machine learning models.
• Optimizing model performance and scalability.

Required Skills

• Proficiency in programming languages such as Python, R, or Java.
• Strong understanding of machine learning algorithms and techniques.
• Experience with machine learning frameworks like TensorFlow or PyTorch.
• Knowledge of data preprocessing, feature engineering, and model evaluation techniques.
• Understanding of software engineering principles for developing scalable and maintainable code.

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2. Data Scientist

Data Scientists analyze complex data sets to uncover insights and patterns that inform business decisions. They utilize statistical analysis, machine learning, and data visualization techniques to extract valuable information from large volumes of data.

Key Responsibilities

• Collecting, cleaning, and analyzing structured and unstructured data.
• Developing predictive models and algorithms to solve business problems.
• Communicating findings and insights to stakeholders through reports and presentations.

Required Skills

• Proficiency in programming languages like Python, R, or Java.
• Strong statistical analysis and mathematical skills.
• Experience with data manipulation and visualization libraries such as: Pandas, Numpy, Matplotlib and Seaborn
• Knowledge of machine learning algorithms and techniques.
• Domain expertise in the industry or sector they are working in.

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3. AI Research Scientist

AI Research Scientists are involved in fundamental research to advance the field of Artificial Intelligence. They explore new algorithms, models, and techniques to solve complex problems and push the boundaries of AI capabilities.

Key Responsibilities

• Researching to develop novel AI algorithms and methodologies.
• Publishing research findings in academic journals and conferences.
• Collaborating with cross-functional teams to apply research findings to real-world problems.

Required Skills

• Advanced knowledge of machine learning, deep learning, and other AI techniques.
• Strong mathematical and theoretical background in areas such as linear algebra, calculus, and probability theory.
• Experience with programming languages like Python, C++, or MATLAB.
• Excellent problem-solving and analytical skills.
• Ability to work independently and in a team-oriented environment.

4. Computer Vision Engineer

Computer Vision Engineers focus on developing algorithms and systems that enable computers to interpret and understand visual information from images or videos. They work on object detection, image classification, and facial recognition applications.

Key Responsibilities:

• Designing and implementing computer vision algorithms for image and video analysis.
• Developing and training deep learning models for object detection and segmentation tasks.
• Optimizing algorithms for real-time performance on various hardware platforms.

Required Skills

• Proficiency in programming languages such as Python, C++, or MATLAB.
• Strong understanding of computer vision concepts and techniques.
• Experience with deep learning frameworks like TensorFlow, Keras, or OpenCV.
• Knowledge of image processing techniques and libraries.
• Familiarity with hardware acceleration technologies like GPUs and TPUs.

5. Natural Language Processing (NLP) Engineer

NLP Engineers specialize in developing algorithms and systems that enable computers to understand and generate human language. They work on text classification, sentiment analysis, and language translation tasks.

Key Responsibilities

• Building and training NLP models for various language-related tasks.
• Preprocessing and cleaning text data for analysis and modeling.
• Developing tools and libraries for NLP applications.

Required Skills

• Proficiency in programming languages like Python, Java, or Scala.
• Strong understanding of NLP techniques and algorithms.
• Experience with NLP libraries and frameworks like NLTK, spaCy, or Transformers.
• Knowledge of linguistics and language theory.
• Familiarity with techniques such as word embeddings, sequence modeling, and attention mechanisms.

6. Robotics Engineer

Robotics Engineers focus on designing, building, and programming robots that can perform tasks autonomously or semi-autonomously. They work on various robotic systems, including industrial robots, autonomous vehicles, and humanoid robots.

Key Responsibilities

• Designing and prototyping robotic systems for specific applications.
• Developing algorithms for robot perception, motion planning, and control.
• Integrating sensors, actuators, and other hardware components into robotic platforms.

Required Skills

• Proficiency in programming languages such as C++, Python, or ROS (Robot Operating System).
• Strong understanding of robotics concepts and principles.
• Experience with robotic simulation tools such as Gazebo or V-REP.
• Knowledge of sensor fusion, localization, and mapping techniques.
• Ability to work with mechanical and electrical engineers in a multidisciplinary team environment.

Begin your path to becoming an AI expert with our blog on “How to Start a Career in Artificial Intelligence and Machine Learning in 2024.” 

Advantages of Artificial Intelligence

Artificial Intelligence (AI) offers numerous benefits that enhance efficiency, accuracy, and capabilities across various fields:

• Reduction In Human Error: AI systems can perform highly precise tasks, minimizing errors and improving accuracy in data entry and analysis processes.

• Available 24×7: Unlike humans, AI can operate continuously without fatigue, providing consistent performance and availability around the clock.

• Helps In Repetitive Work: AI excels in automating repetitive tasks, freeing up human workers to focus on more complex and creative activities.

• Digital Assistance: AI-powered virtual assistants and chatbots offer immediate support and streamline customer service, improving user experience.

• Faster Decisions: AI can quickly analyze vast amounts of data, enabling faster decision-making in critical situations like financial trading and medical diagnosis.

• Rational Decision Maker: AI systems make decisions based on data and algorithms, reducing the influence of emotions and biases in critical scenarios.

• Medical Applications: AI assists in diagnosing diseases, developing treatment plans, managing patient care, and enhancing the efficiency and accuracy of healthcare services.

• Improves Security: AI enhances security measures through advanced threat detection, fraud prevention, and surveillance systems, making environments safer.

• Efficient Communication: AI improves communication by enabling real-time language translation and facilitating seamless interactions across different languages and regions.

Disadvantages of Artificial Intelligence

Despite its advantages, AI also presents several challenges and drawbacks:

• Cost Overruns: Developing and implementing AI technologies can be expensive, with high initial investment and ongoing maintenance costs.

• Dearth Of Talent: There is a shortage of skilled professionals with expertise in AI, making it difficult for companies to find and hire the necessary talent.

• Lack Of Practical Products: Many AI solutions are still in the research or experimental phase, limiting their practical applications and widespread adoption.

• Lack Of Standards In Software Development: The absence of standardized protocols in AI software development can lead to inconsistencies and interoperability issues.

• Potential For Misuse: AI can be used maliciously to hack, spy, and spread misinformation, posing ethical and security risks.

• Highly Dependent On Machines: Overreliance on AI and automation can reduce human skills and critical thinking abilities.

• Requires Supervision: AI systems need continuous monitoring and supervision to ensure they operate correctly and ethically, requiring ongoing human oversight.

Understanding the advantages and disadvantages of AI helps make informed decisions about its implementation and managing its impact effectively.

A Simple Artificial Intelligence Mini-Project

A simple AI programming tutorial demonstrates a basic machine learning task: predicting housing prices using linear regression. For this project, we’ll use the sklearn library.

Project: Predicting Housing Prices

Step 1: Import Libraries

import numpy as np

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

from sklearn.metrics import mean_squared_error

Step 2: Create a Sample Dataset

For simplicity, we’ll create a synthetic dataset.

# Create a sample dataset

data = {

    'Size': [750, 800, 850, 900, 950, 1000, 1050, 1100, 1150, 1200],

    'Price': [150000, 160000, 165000, 175000, 180000, 190000, 200000, 210000, 215000, 225000]

}

df = pd.DataFrame(data)

print(df)

Step 3: Split the Data into Training and Testing Sets

# Split the dataset into features (X) and target (y)

X = df[['Size']]

y = df['Price']

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Step 4: Train the Model

# Initialize the linear regression model

model = LinearRegression()

# Train the model

model.fit(X_train, y_train)

Step 5: Make Predictions

# Make predictions on the test set

y_pred = model.predict(X_test)

# Output the predictions

print("Predicted Prices:", y_pred)

print("Actual Prices:", y_test.values)

Step 6: Evaluate the Model

# Calculate the mean squared error

mse = mean_squared_error(y_test, y_pred)

print("Mean Squared Error:", mse)

Full Code

import numpy as np

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LinearRegression

from sklearn.metrics import mean_squared_error

# Create a sample dataset

data = {

    'Size': [750, 800, 850, 900, 950, 1000, 1050, 1100, 1150, 1200],

    'Price': [150000, 160000, 165000, 175000, 180000, 190000, 200000, 210000, 215000, 225000]

}

df = pd.DataFrame(data)

print("Dataset:")

print(df)

# Split the dataset into features (X) and target (y)

X = df[['Size']]

y = df['Price']

# Split the data into training and testing sets

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize the linear regression model

model = LinearRegression()

# Train the model

model.fit(X_train, y_train)

# Make predictions on the test set

y_pred = model.predict(X_test)

# Output the predictions

print("\nPredicted Prices:", y_pred)

print("Actual Prices:", y_test.values)

# Calculate the mean squared error

mse = mean_squared_error(y_test, y_pred)

print("Mean Squared Error:", mse)

Output

Dataset:

Size   Price

750   150000

800   160000

850    165000

900    175000

950    180000

1000   190000

1050   200000

1100    210000

1150    215000

1200    225000

Predicted Prices: [195000. 215000.]

Actual Prices: [200000 215000]

Mean Squared Error: 12500000.0

This simple project demonstrates how to use linear regression to predict housing prices based on the size of the house. You can extend this project by using a real dataset and adding more features for better predictions.

Also, explore the ‘Top Machine Learning and Artificial Intelligence Projects For Beginners in 2024′ and discover the best starter projects to enhance your skills!

Future of Artificial Intelligence

The future of Artificial Intelligence (AI) holds immense promise and potential, shaping various industries and driving technological innovation. Here’s a concise breakdown of what lies ahead:

• Continued Advancements
  AI is experiencing unprecedented growth, impacting every sector and driving emerging technologies like big data, robotics, and the Internet of Things (IoT).

• Growing Demand
  As AI continues to evolve, there is an increasing demand for skilled professionals, making it a lucrative career choice.

• Emerging Applications
  AI is finding applications in diverse areas, such as facial recognition, healthcare, and chatbots, demonstrating significant growth potential.

• Integration in Daily Life
  Virtual assistants and AI-powered services are becoming ubiquitous daily, indicating a seamless integration of AI technologies into society.

• Technological Glimpse
  Innovations like self-driving cars from companies like Tesla offer a glimpse into the future, showcasing the transformative potential of AI in various domains.

As AI continues to change industries and create new opportunities, embracing its potential now is pivotal for a prosperous future driven by innovation and AI technological advancement.

Conclusion

The Artificial Intelligence Tutorial for Beginners in 2024 offers a solid foundation for grasping the AI basics and detailed concepts from experts. 

For those seeking to delve deeper into AI and Machine Learning, the Great Learning PG Program in AI and Machine Learning is the ideal next step.

With the flexibility to learn anytime, anywhere, and access to weekly online mentorship, participants gain exclusive insights and skills. 

Moreover, with live career mentorship and access to the Great Learning job board, this program opens doors to the hottest jobs and highest-paying packages in the AI industry. 

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