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The Certified AI Course by SkillFloor offers a demanding and in-depth exploration of artificial intelligence (AI) concepts and techniques. Through a carefully curated curriculum, participants delve into key areas such as machine learning, deep learning, natural language processing, and computer vision. With a focus on practical application, Whether you're a novice looking to enter the field or an experienced professional seeking to expand your expertise, this AI course provides the essential training needed to thrive in the current  AI-driven world.

By enrolling in this comprehensive AI training program, participants From understanding fundamental AI algorithms to implementing advanced neural networks, the course covers a wide range of topics essential for success in AI. Through online learning modules and interactive sessions, learners can conveniently access course materials and collaborate with peers from around the globe. Upon completion, participants emerge as certified AI professionals, equipped with the knowledge and skills needed to tackle complex AI challenges and drive innovation in diverse industries. Whether you're interested in AI courses online or seeking hands-on AI training, the Certified AI Course by SkillFloor offers a dynamic learning experience tailored to meet the demands of today's quick changing AI environment.




Skill Level



2-month Unpaid

Live Project




Live Training


Career Assistance


Expiry Period

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Course Highlights

  • Introduction to AI: Provides a foundational overview of artificial intelligence, including its history, development stages, and various branches like machine learning, deep learning, and neural networks.

  • Machine Learning Fundamentals: Covers the basics of machine learning, algorithms, how they are trained, and the difference between supervised, unsupervised, and reinforcement learning.

  • Deep Learning and Neural Networks: Delves into more complex AI systems, explaining how deep learning mimics the human brain to process data and patterns through neural networks.

  • Natural Language Processing (NLP): Introduces the techniques used for computers to understand, interpret, and produce human language, including text analysis and speech recognition.

  • AI in Practice: Explores real-world applications of AI across various industries such as healthcare, finance, and automotive, demonstrating how AI solutions are deployed to solve complex problems.

  • Ethics and AI: Addresses the ethical considerations and societal impacts of artificial intelligence, including privacy concerns, bias in AI systems, and the future of work.

  • Data Science Integration: Highlights the role of data science in AI, including data collection, preprocessing, and analysis, to improve the accuracy of AI models.

  • Robotics: Offers insight into how AI is used in robotics, covering topics from simple automated machines to advanced robots with AI capabilities for decision-making.

  • AI Project Lifecycle: Guides through the stages of an AI project from conception to deployment, including problem identification, algorithm selection, model training, and evaluation.

  • Capstone Project: Provides an opportunity to apply the learned concepts in a comprehensive project, allowing participants to develop, test, and present an AI solution to a real-world problem.



The Certified AI Expert certificate signifies a deep understanding of artificial intelligence principles and applications. This accreditation validates expertise in machine learning, neural networks, and data analysis. Holders have demonstrated proficiency in developing AI solutions, making data-driven decisions, and contributing to innovative projects. Ideal for professionals aiming to excel in the dynamic field of AI, this certificate showcases a commitment to staying at the forefront of technological advancements.

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Top 10 reasons

  1. Foundations of AI: Grasp the fundamental concepts and principles that underlie artificial intelligence, laying a solid groundwork for advanced learning.

  2. Machine Learning Mastery: Dive into the core of machine learning techniques, understanding algorithms and models crucial for AI applications.

  3. Data Science Integration: Learn how to extract meaningful insights from data, a vital skill for crafting intelligent systems.

  4. Deep Dive into Neural Networks: Explore the intricacies of neural networks, unravelling the mysteries behind deep learning and neural architecture.

  5. Natural Language Processing (NLP): Understand the nuances of human language and how AI interprets and generates text, enabling effective communication between man and machine.

  6. Computer Vision Techniques: Delve into the world of computer vision, learning to make machines interpret and understand visual information.

  7. AI Ethics and Bias Mitigation: Grasp the ethical considerations surrounding AI and discover strategies to mitigate bias in AI algorithms.

  8. Real-world AI Applications: Explore practical applications of AI in various industries, gaining insights into how AI can solve real-world problems.

  9. Hands-On Projects: Apply your knowledge through hands-on projects, enhancing your skills and building a portfolio that showcases your expertise.

  10. Certification Capstone: Culminate your learning journey with a comprehensive certification project, demonstrating your proficiency as a Certified AI Expert.


  • Overview of Artificial Intelligence

  • History and Evolution of AI

  • Types of Artificial Intelligence

  • Applications and Use Cases


  •  Introduction to Machine Learning

  •  Supervised Learning

  •  Unsupervised Learning

  •  Reinforcement Learning

  • -Model Evaluation and Validation

  •  Introduction to Neural Networks

  •  Convolutional Neural Networks (CNNs)

  •  Recurrent Neural Networks (RNNs)

  •  Generative Adversarial Networks (GANs)

  •  Transfer Learning

  •  Basics of Natural Language Processing

  •  Text Preprocessing Techniques

  •  Word Embeddings

  •  Named Entity Recognition (NER)

  •  Sentiment Analysis

  •  Introduction to Computer Vision

  •  Image Processing Techniques

  •  Object Detection and Recognition

  •  Image Classification

  •  Image Segmentation


  •  Introduction to Reinforcement Learning

  •  Markov Decision Processes (MDPs)

  •  Q-Learning and Deep Q-Networks (DQN)

  •  Policy Gradient Methods

  •  Applications of Reinforcement Learning

  • Ethical Considerations in AI Development

  • Bias and Fairness in AI
  • Transparency and Explainability

  • Accountability and Governance

  • Case Studies and Best Practices

  • AI Development Lifecycle

  • Model Deployment and Monitoring

  • Scalability and Performance Optimization

  • Case Studies and Real-world Applications

  • Emerging Trends in AI


A certified AI expert is someone who has completed a recognized certification program in artificial intelligence (AI), demonstrating their proficiency in understanding and applying advanced AI concepts and techniques.


Obtaining certification in AI can validate your skills, enhance your credibility, and increase your job prospects in fields where AI technology is used.


To become certified, you typically need to complete specific training courses, gain hands-on experience with AI algorithms and tools, and pass an exam that tests your knowledge and proficiency in AI concepts.

Skills such as programming, mathematics, data analysis, machine learning, deep learning, and problem-solving are important for AI certification.


With AI certification, you can pursue roles such as AI engineer, data scientist, machine learning engineer, AI researcher, or AI consultant in various industries.

The time it takes to become certified varies depending on the program and your prior knowledge, but it typically takes several months to a year of study and preparation.


Earning an AI certification can provide you with valuable skills and credentials that can help advance your career and open up new opportunities in the field of AI.

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