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The Artificial Intelligence course offered by Skillfloor provides a comprehensive online learning experience tailored for individuals interested in mastering the fundamentals of artificial intelligence (AI) and machine learning (ML). Through engaging modules and interactive content, participants delve into key concepts such as AI algorithms, neural networks, and data analysis techniques. The course equips learners with practical skills in implementing AI solutions, enabling them to leverage the power of AI to solve real-world problems effectively. With a focus on deep learning AI methodologies and advanced ML techniques, this course caters to both beginners and professionals seeking to expand their knowledge in the field of artificial intelligence.

Designed to be accessible and flexible, this AI course online offers participants the opportunity to learn at their own pace and from anywhere with an internet connection. By combining theoretical knowledge with hands-on exercises and case studies, learners gain a solid understanding of AI principles and applications. Whether aspiring to become AI specialists or looking to enhance their current skill set, individuals enrolled in this course will emerge with a comprehensive understanding of artificial intelligence and machine learning, ready to tackle the challenges of tomorrow's technology-driven world.




Skill Level



1-month Unpaid

Live Project




Live Training


Career Assistance


Expiry Period

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Course Highlights

  • Introduction to AI: The course starts with a basic overview of what artificial intelligence is. It discusses AI's purpose, how it evolved, and its impact on various industries.

  • History of AI: This section covers the development of AI, from its inception to the present day. It includes key milestones and figures who have contributed significantly to the field.

  • Types of AI: Students learn about different AI types, such as narrow (or weak) AI, which is designed to perform specific tasks, and general (or strong) AI, which has the potential to understand or learn any intellectual task that a human being can.

  • Machine Learning: This part introduces machine learning, a core component of AI, explaining how computers use data to improve tasks without being explicitly programmed for those tasks.

  • Deep Learning: An extension of machine learning, deep learning is discussed, highlighting its role in enabling computers to learn from vast amounts of data through layers of artificial neural networks.

  • Natural Language Processing (NLP): The course explains how AI technologies interpret and manipulate human language, allowing machines to understand how humans speak and write.

  • Robotics: This section touches on the integration of AI into robotics, enabling robots to perform tasks autonomously, from simple to complex.

  • Ethical Considerations: An essential part of the curriculum covers the ethical implications of AI, including privacy concerns, bias in AI systems, and the future impact on employment.

  • AI Applications: Students will learn about real-world applications of AI across different sectors such as healthcare, finance, and transportation, illustrating AI's versatility.

  • Future of AI: The course concludes with a forward-looking discussion on the potential future developments in AI, including emerging technologies and the challenges they may pose.



The Artificial Intelligence Foundation Certificate is designed to equip learners with fundamental AI knowledge. Covering key concepts such as machine learning and neural networks, this course provides a solid understanding of AI principles. Ideal for beginners, it lays the groundwork for further exploration in the dynamic field of artificial intelligence. Perfect for those seeking a comprehensive introduction to AI's basics and applications.

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Top 10 reasons

1. Introduction to AI Concepts: Explore the fundamental concepts that form the backbone of artificial intelligence, laying the groundwork for deeper understanding.

2. Machine Learning Essentials: Delve into the basics of machine learning, understanding algorithms and models that drive AI applications.

3. Data and AI: Learn how data fuels AI, covering data collection, preprocessing, and its crucial role in training AI models.

4. Neural Networks Overview: Gain insights into the neural networks that power AI systems, understanding their structure and functionalities.

5. AI in Real-world Applications: Explore practical applications of AI across various industries, from healthcare to finance, showcasing its real-world impact.

6. Ethical Considerations in AI: Discuss the ethical implications of AI, addressing issues like bias, transparency, and responsible AI development.

7. Natural Language Processing (NLP): Understand how AI processes and interprets human language, a key component in chatbots, language translation, and more.

8. Computer Vision Basics: Explore the world of computer vision, where AI systems interpret and make decisions based on visual data.

9. AI Tools and Frameworks: Familiarize yourself with popular AI tools and frameworks, enabling hands-on experience in building AI applications.

10. Future Trends in AI: Look ahead to emerging trends and advancements in AI, and prepare for the artificial intelligence world.


  •  Overview of Artificial Intelligence

  •  History and Evolution of AI

  •  Types of AI: Narrow vs. General Intelligence

  •  Applications of AI in Various Industries

  •  Introduction to Machine Learning

  •  Supervised, Unsupervised, and Reinforcement Learning

  •  Regression and Classification Algorithms

  •  Evaluation Metrics in Machine Learning

  •  Neural Networks and Perceptrons

  •  Convolutional Neural Networks (CNNs)

  •  Recurrent Neural Networks (RNNs)

  •  Training Deep Learning Models


  •  Introduction to NLP

  •  Text Preprocessing and Tokenization

  •  Word Embeddings: Word2Vec, GloVe

  •  Sentiment Analysis and Text Classification


  •  Basics of Computer Vision

  •  Image Processing Techniques

  •  Object Detection and Image Classification

  •  Convolutional Neural Networks for Image Recognition


  •  Introduction to Robotics

  •  Robot Perception and Sensing

  •  Motion Planning and Control

  •  Reinforcement Learning for Robotics


  •  Ethical Considerations in AI Development

  •  Bias and Fairness in AI Systems

  •  AI and Privacy Concerns

  •  Future Implications of AI on Society


  •  Real-world Applications of AI

  •  Case Studies in Healthcare, Finance, and Transportation

  •  Building AI Systems: Challenges and Best Practices

  •  The Future of Artificial Intelligence



AI refers to the development of computer systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.

AI can automate repetitive tasks, make predictions based on data, improve efficiency, and help solve complex problems in various fields like healthcare, finance, and transportation.

AI systems use algorithms to process data, identify patterns, and make decisions or predictions. They learn from experience and improve over time.

AI can be categorized into three types: narrow or weak AI, general AI, and artificial superintelligence, each with varying levels of intelligence and capabilities.


Examples include virtual assistants like Siri and Alexa, recommendation systems like those used by Netflix and Amazon, and autonomous vehicles.


AI can increase productivity, improve accuracy, enhance decision-making, enable automation, and help tackle complex problems more efficiently.


Risks include job displacement due to automation, biases in AI algorithms, privacy concerns, and the potential for misuse, such as in surveillance or warfare.

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