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Overview

The Certified Artificial Intelligence course by Skillfloor is a comprehensive offering designed to equip individuals with foundational knowledge and practical skills in the field of artificial intelligence. As an artificial intelligence course online, it caters to learners seeking a flexible learning format while delving into essential concepts such as machine learning, deep learning AI, and various applications of AI technologies. Through a structured curriculum, participants can expect to gain insights into the fundamentals of AI, including its algorithms, data processing techniques, and real-world implementations. Moreover, the program covers key aspects of machine learning, an integral component of AI, providing learners with a solid understanding of algorithms and models used to enable machines to learn from data and make predictions.


This AI course goes beyond theoretical understanding by incorporating practical exercises and case studies, allowing participants to apply their knowledge in simulated environments and real-world scenarios. By exploring topics such as deep learning AI, learners gain insight into advanced neural network architectures and techniques for training models on large datasets. With a focus on hands-on learning and industry-relevant skills, the Artificial Intelligence course by Skillfloor prepares individuals to embark on a career in the quick-changing field of AI, providing them with the expertise needed to contribute effectively to the development and deployment of AI solutions across various domains.

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₹20,000

₹9,900

Skill Level

Beginner

Internship

1-month Unpaid

Live Project

NA

Certificate

Yes

Live Training

Yes

Career Assistance

Yes

Expiry Period

Lifetime
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Course Highlights

  • Introduction to AI: An overview of AI concepts, history, and its impact on industries, laying the groundwork for understanding AI's role in modern technology.

  • Machine Learning Basics: Fundamental principles of machine learning, including how computers use data to make decisions. This section covers algorithms and models that form the backbone of AI.

  • Data Handling and Manipulation: Techniques for processing and manipulating data, crucial for feeding accurate information into AI models to generate reliable outputs.

  • Neural Networks and Deep Learning: Insight into the architecture of neural networks and deep learning, the advanced subsets of machine learning that mimic the human brain's functioning.

  • Natural Language Processing (NLP): Exploration of how AI understands and generates human language, enabling applications like chatbots and virtual assistants.

  • Computer Vision: An introduction to how AI interprets and understands visual information from the world, including image and video analysis.

  • AI in Robotics: Understanding the application of AI in developing autonomous machines that can perform tasks requiring human intelligence.

  • Ethical Considerations in AI: Discussion on the ethical implications of AI, including privacy, security, and the social impact of deploying AI technologies.

  • AI Project Management: Guidelines on managing AI projects, focusing on planning, executing, and deploying AI solutions effectively.

  • Real-World AI Applications: Case studies and examples of AI applications across various sectors, provide insights into how AI solves real-world problems.

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Certification

The Certified Artificial Intelligence Associate certificate equips learners with foundational AI knowledge. Covering key concepts like machine learning and neural networks, it fosters a solid understanding of AI's practical applications. Suitable for beginners, this certification validates proficiency in AI fundamentals, making it an essential credential for those entering the dynamic field of artificial intelligence.

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Top 10 reasons

  1. Certification: Gain a reputable AI associate certification to attest to your understanding of fundamental AI principles and applications.

  2. Fundamentals: Learn the fundamentals of machine learning, data analysis, and AI programming to lay the foundation for a successful career in the AI sector.

  3. Practical experience: Acquire practical experience through interactive projects and simulations, preparing you for AI possibilities and challenges in the real world.

  4. Insights: Get insights into the newest developments and best practices in artificial intelligence by learning from professionals at the forefront of the field.

  5. Employment: In today's competitive employment market, make a lasting impression on employers by adding AI knowledge to your CV.

  6. Collaboration: Become a member of a peer- and mentor-led learning community that supports collaboration as well as sharing information in the field of artificial intelligence.

  7. skill development: Get individualised feedback and direction from knowledgeable teachers to expedite your learning and skill development.

  8. Tools and materials: access to tools and materials that are customized to meet your learning needs, guaranteeing a thorough and productive learning process.

  9. Network: Build your network by interacting with other AI experts and enthusiasts to increase your contacts and future employment prospects.

  10. AI technology: As AI technology and innovation continue to grow, get ready for them and position yourself for success in this quickly changing industry.

Syllabus

  •  Understanding the fundamentals of artificial intelligence

  •  History and evolution of AI

  •  Types of artificial intelligence: Narrow vs. General AI

  •  Applications and real-world examples of AI

  •  Introduction to machine learning

  •  Supervised, unsupervised, and reinforcement learning

  •  Key concepts: algorithms, models, features, labels

  •  Hands-on exercises with popular machine-learning libraries

 

  •  Data cleaning and preprocessing techniques

  •  Handling missing data and outliers

  •  Feature selection and extraction methods

  •  Data transformation and normalization

 

  • Linear regression

  •  Logistic regression

  •  Decision trees and random forests

  •  Support Vector Machines (SVM)

  •  Evaluation metrics for classification and regression tasks

 

  •  K-means clustering

  •  Hierarchical clustering

  •  Principal Component Analysis (PCA)

  •  Association rule learning

  •  Evaluation metrics for clustering and dimensionality reduction

  • Introduction to neural networks

  •  Building blocks of neural networks: neurons, layers, activation functions

  •  Feedforward neural networks

  •  Convolutional Neural Networks (CNNs) for image recognition

  •  Recurrent Neural Networks (RNNs) for sequential data

 

  •  Basics of natural language processing

  •  Tokenization, stemming, and lemmatization

  •  Named Entity Recognition (NER)

  •  Sentiment analysis

  •  Text classification and language modeling

  •  Introduction to reinforcement learning

  •  Markov Decision Processes (MDPs)

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

  •  Policy gradients and actor-critic methods

  •  Applications in robotics, gaming, and optimization

 

  •  Bias and fairness in AI algorithms

  •  Privacy and security concerns

  •  Impact of AI on employment and society

  •  Ethical guidelines and regulations in AI development and deployment

  •  Case studies and discussions on ethical dilemmas

 

  • Project management methodologies for AI projects

  •  Model deployment techniques

  •  Monitoring and maintenance of AI systems

  •  Collaboration and communication in cross-functional AI teams

  •  Final project presentations and assessments

 

FAQ

A certified artificial intelligence (AI) associate is someone who has completed a recognized certification program in AI, demonstrating their proficiency in understanding and applying 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 tools and techniques, and pass an exam that tests your knowledge and understanding of AI concepts.

Skills such as programming, machine learning, data analysis, problem-solving, and critical thinking are important for artificial intelligence certification.

 

With artificial intelligence 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 artificial intelligence 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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