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products:ict:ai:ai_course_1 [2023/07/26 14:43] – wikiadmin | products:ict:ai:ai_course_1 [2023/07/26 18:59] (current) – wikiadmin |
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Course outlines for each of the three roles, AI Expert, AI Data Analyst, and AI Research Scientist, will vary depending on the specific institution or program offering the courses. However, I can provide you with a general overview of the key topics and skills that are typically covered in each role's course curriculum: | ====== AI Course 1 ====== |
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| Topics for the three roles in AI. |
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| AI Expert, AI Data Analyst, and AI Research Scientist. |
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1. AI Expert: | 1. AI Expert: |
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The AI Expert course focuses on providing a comprehensive understanding of artificial intelligence, including theoretical concepts, algorithms, and practical applications. It is designed for individuals interested in developing AI systems and solutions for real-world problems. The course may include the following topics: | The AI Expert role focuses on providing a comprehensive understanding of artificial intelligence, including theoretical concepts, algorithms, and practical applications. It is for individuals interested in developing AI systems and solutions for real-world problems. The role includes the following topics: |
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- [[products:ict:ai:introduction_to_artificial_intelligence|Introduction to Artificial Intelligence: History, goals, and key concepts.]] | - [[products:ict:ai:introduction_to_artificial_intelligence|Introduction to Artificial Intelligence: History, goals, and key concepts.]] |
- [[products:ict:ai:machine_learning_intro|Machine Learning: Supervised, unsupervised, and reinforcement learning algorithms. | - [[products:ict:ai:machine_learning_intro|Machine Learning: Supervised, unsupervised, and reinforcement learning algorithms. |
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- Deep Learning: Neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). | |
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- Natural Language Processing (NLP): Understanding and processing human language. | - [[products:ict:ai:deep_learning|Deep Learning]]: Neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). |
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| - [[products:ict:ai:nlp|Natural Language Processing (NLP)]]: Understanding and processing human language. |
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- Computer Vision: Image and video analysis using AI techniques. | - [[products:ict:ai:computer_vision|Computer Vision]]: Image and video analysis using AI techniques. |
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- AI Ethics and Responsible AI: Ethical considerations in AI development and deployment. | - [[products:ict:ai:ai_ethics|AI Ethics and Responsible AI]]: Ethical considerations in AI development and deployment. |
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- AI Tools and Libraries: Working with popular AI frameworks like TensorFlow and PyTorch. | - [[products:ict:ai:ai_tools_and_libraries|AI Tools and Libraries]]: Working with popular AI frameworks like TensorFlow and PyTorch. |
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- AI Applications: Implementing AI in various domains such as healthcare, finance, and robotics. | - [[products:ict:ai:ai_applications|AI Applications]]: Implementing AI in various domains such as healthcare, finance, and robotics. |
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2. AI Data Analyst: | 2. AI Data Analyst: |
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The AI Data Analyst course is tailored for individuals interested in working with data and using AI techniques to extract insights and make data-driven decisions. The curriculum may include the following topics: | The AI Data Analyst part is tailored for individuals interested in working with data and using AI techniques to extract insights and make data-driven decisions. This part includes the following topics: |
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- Data Analysis Fundamentals: Data cleaning, exploration, and visualization. | - [[products:ict:ai:data_analysis_fundamentals|Data Analysis Fundamentals]]: Data cleaning, exploration, and visualization. |
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- Statistics and Probability: Key concepts for data analysis and machine learning. | - [[products:ict:ai:statistics_and_probability|Statistics and Probability]]: Key concepts for data analysis and machine learning. |
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- Machine Learning for Data Analysis: Applying supervised and unsupervised learning algorithms. | - [[products:ict:ai:machine_learning_for_data_analysis|Machine Learning for Data Analysis]]: Applying supervised and unsupervised learning algorithms. |
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- Data Preprocessing and Feature Engineering: Preparing data for AI models. | - [[products:ict:ai:data_preprocessing|Data Preprocessing and Feature Engineering]]: Preparing data for AI models. |
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- Data Mining and Pattern Recognition: Identifying patterns and trends in data. | - [[products:ict:ai:data_mining|Data Mining and Pattern Recognition]]: Identifying patterns and trends in data. |
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- Big Data and Cloud Computing: Handling and analyzing large datasets using cloud-based tools. | - [[products:ict:ai:big_data_and_cloud_computing|Big Data and Cloud Computing]]: Handling and analyzing large datasets using cloud-based tools. |
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- Database Management: SQL and NoSQL databases for data storage and retrieval. | - [[products:ict:ai:database_management|Database Management]]: SQL and NoSQL databases for data storage and retrieval. |
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- AI in Business: Using AI to solve business problems and optimize processes. | - [[products:ict:ai:ai_in_business|AI in Business]]: Using AI to solve business problems and optimize processes. |
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3. AI Research Scientist: | 3. AI Research Scientist: |
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The AI Research Scientist course is designed for individuals who want to pursue cutting-edge research in artificial intelligence and contribute to advancing the field. It typically covers the following topics: | The AI Research Scientist part is designed for individuals who want to pursue cutting-edge research in artificial intelligence and contribute to advancing the field. It covers the following topics: |
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- Advanced Machine Learning: Deep dive into advanced ML algorithms, optimization techniques, and model evaluation. | - [[products:ict:ai:advanced_machine_learning|Advanced Machine Learning]]: Deep dive into advanced ML algorithms, optimization techniques, and model evaluation. |
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- Reinforcement Learning: In-depth study of RL algorithms and their applications. | - [[products:ict:ai:reinforcement_learning|Reinforcement Learning]]: In-depth study of RL algorithms and their applications. |
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- Probabilistic Graphical Models: Learning and reasoning with probabilistic models. | - [[products:ict:ai:probabilistic_graphical_models|Probabilistic Graphical Models]]: Learning and reasoning with probabilistic models. |
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- Deep Reinforcement Learning: Merging deep learning with RL for complex tasks. | - [[products:ict:ai:deep_reinforcement_learning|Deep Reinforcement Learning]]: Merging deep learning with RL for complex tasks. |
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- Natural Language Processing Research: Advanced NLP techniques, sentiment analysis, text generation. | - [[products:ict:ai:natural_language_processing_research|Natural Language Processing Research]]: Advanced NLP techniques, sentiment analysis, text generation. |
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- Computer Vision Research: Advanced topics in image and video analysis, object detection, and segmentation. | - [[products:ict:ai:computer_vision_research|Computer Vision Research]]: Advanced topics in image and video analysis, object detection, and segmentation. |
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- AI Ethics and Bias: Addressing ethical challenges and biases in AI research. | - [[products:ict:ai:ai_ethics_and_bias|AI Ethics and Bias]]: Addressing ethical challenges and biases in AI research. |
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- Research Methodology: Techniques for conducting AI research, writing research papers, and presenting findings. | - [[products:ict:ai:research_methodology|Research Methodology]]: Techniques for conducting AI research, writing research papers, and presenting findings. |
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- Specialization and Thesis: Focusing on a specific research area and completing an original research project. | - [[products:ict:ai:specialization_and_thesis|Specialization and Thesis]]: Focusing on a specific research area and completing an original research project. |
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