Machine Learning & Artificial Intelligence Models

About The Diploma

  • This diploma is the ideal scientific and practical choice for your excellence in Artificial Intelligence, The Diploma provides students with a comprehensive understanding of AI concepts, technologies, and applications. The course covers topics such as machine learning, deep learning, natural language processing, robotics, and computer vision.
  • The Artificial Intelligence diploma program is designed for individuals who want to develop technical skills in AI and pursue a career in the field. It is also suitable for professionals who want to expand their knowledge and advance their careers in industries such as healthcare, finance, marketing, and manufacturing.
  • The Artificial Intelligence diploma program usually consists of a combination of Live Interactive lectures, practical assignments and hands-on projects. Students learn about Python, AI algorithms, ML algorithms, Deep Learning algorithms, and software tools used in the field. They also work on real-world projects that allow them to apply their knowledge and skills in practical scenarios
  • Upon completion of the Artificial Intelligence diploma, students are equipped with the skills and knowledge needed to pursue a career in the field of AI. They can work as AI engineers, data scientists, machine learning engineers, robotics engineers, and more. Overall, an Artificial Intelligence diploma is a valuable certification for individuals who want to develop technical skills in AI and pursue a career in this rapidly growing field

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350$500$

Course Description

Why Study Artificial Intelligence

There are several reasons why studying Artificial Intelligence (AI) is important:

  • Career Opportunities: AI is one of the fastest-growing fields, and there is a high demand for skilled AI professionals. Pursuing a career in AI can lead to a lucrative and fulfilling profession.
  • Problem Solving: AI is being used to solve some of the world's most complex problems, such as climate change, disease detection, and traffic congestion. Studying AI can help you understand how to develop solutions to these and other pressing issues.
  • Innovation: AI is driving innovation in many industries, including healthcare, finance, and transportation. By studying AI, you can learn how to develop new technologies that can revolutionize these industries.
  • Better Decision Making: AI can help organizations make better decisions by analysing large amounts of data and providing insights that humans may not be able to detect. Studying AI can help you understand how to apply these techniques to make better decisions
  • Understanding the Future: AI is expected to have a significant impact on society in the coming years. By studying AI, you can gain a better understanding of how this technology will shape the future and be better prepared for the changes it may bring.
  • Overall, studying AI is important because it can lead to exciting career opportunities, help solve complex problems, drive innovation, improve decision making, and provide insights into the

Who should attend

An Artificial Intelligence (AI) course is suitable for a wide range of individuals who are interested in learning about AI and developing technical skills in the field. Some examples of people who may benefit from attending an AI course include:

  • Students: AI courses are valuable for students who want to pursue a career in the field of AI. The courses provide a strong foundation in AI concepts, technologies, and techniques, which can help students develop the skills and knowledge needed to succeed in the industry.
  • Professionals: AI courses are also beneficial for professionals who want to expand their knowledge and apply AI techniques to their work. For example, professionals in the fields of healthcare, finance, marketing, and manufacturing can use AI to improve their operations and make better decisions.
  • Entrepreneurs: AI courses can be valuable for entrepreneurs who want to develop new AI-based technologies and products. The courses provide a strong understanding of AI concepts and techniques, which can help entrepreneurs create innovative solutions that leverage AI technologies.
  • Researchers: AI courses can also be beneficial for researchers who are interested in exploring the potential of AI. The courses provide a strong foundation in AI concepts and techniques, which can help researchers develop new AI-based applications and technologies.
  • Overall, an AI course is suitable for anyone who wants to learn about AI and develop technical skills in the field. Whether you are a student, professional, entrepreneur, or researcher, attending an AI course can provide you with the knowledge and skills needed to succeed in the industry.

Diploma Objectives

The objectives of an Artificial Intelligence (AI) diploma program are to provide students with a comprehensive understanding of AI concepts, technologies, and applications. The program is designed to help students develop advanced technical skills and knowledge in the field of AI, and to prepare them for a variety of roles in the industry. Some of the key objectives of an AI diploma program include:

  • Introducing AI Concepts: The course aims to introduce students to the foundational concepts of AI, including machine learning, deep learning, natural language processing, robotics, and computer vision.
  • Developing Technical Skills: The course aims to help students develop technical skills in AI concepts, algorithms, and tools. Students learn about programming languages, data analysis techniques, and software tools used in the field.
  • Applying AI Techniques: The course aims to help students apply AI techniques to real-world scenarios. Students work on practical projects and assignments that allow them to apply their knowledge and skills in real-world scenarios.
  • Understanding AI Applications: The course aims to help students understand the applications of AI in various industries, such as healthcare, finance, marketing, and manufacturing.
  • Advancing Career Opportunities: The course aims to help students advance their career opportunities in the field of AI. Students learn about job opportunities in the industry, and develop the skills and knowledge needed to succeed in these roles.
  • Overall, the objectives of an AI diploma program are to provide students with a comprehensive understanding of AI concepts, technologies, and applications, and to help them develop advanced technical skills and knowledge in the field. The program aims to prepare students for a variety of roles in the industry, including AI engineers, data scientists, machine learning engineers, robotics engineers, and more.

Terms & Conditions

Certificate of Completion

To have successfully completed; a trainee should:

-have an attendance rate of not less than 80% (or such higher attendance  requirement as prescribed for the course);

-Pass the course practical assessments and projects in at least 80% of the total number of assignments as required in each training course.

Curriculum

  • Introduction to Machine Learning
  • Supervised Learning
  • Regression and Classification
  • Unsupervised Learning
  • Bias and Variance
  • Regularization
  • Hyperparameter Tuning

1)Linear regression 2)Logistic regression 3)Decision trees 4)Random forests 5)K-Nearest Neighbors 6)Support vector machines

1)Clustering 2)KMeans 3)Hierarchical 4)DBSCAN

  • Introduction to deep learning
  • Neural networks
  • Backpropagation
  • Convolutional neural networks
  • Recurrent neural networks
  • Natural language processing
  • Deep learning frameworks

  • Introduction to NLP
  • Text classification
  • Sentiment analysis
  • Machine translation
  • Tokenization
  • Stemming
  • Lemmatization
  • Part-of-speech tagging
  • Named entity recognition
  • Coreference resolution
  • Semantic parsing
  • Question answering
  • Dialogue systems

  • Introduction to Computer Vision
  • Generative Adversarial Networks (GANs)
  • Image Processing
  • Object Detection
  • Image Classification
  • Adversarial Examples

  • Introduction to reinforcement learning
  • Dynamic programming
  • Monte Carlo methods
  • Temporal-difference learning
  • Function approximation
  • Deep reinforcement learning
  • Applications of reinforcement learning
  • Multi-agent reinforcement learning
  • Proximal policy optimization
  • Imitation learning
  • Safe reinforcement learning

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