Artificial Intelligence (AI) Engineer

Artificial intelligence engineers use AI and machine learning techniques to develop applications and systems that can help organizations increase efficiency, cut costs, increase profits, and make better business decisions. AI engineering focuses on developing the tools, systems, and processes that enable artificial intelligence to be applied in the real world. Any application where machines mimic human functions, such as solving problems and learning, can be considered artificial intelligence. Algorithms are “trained” by data, which helps them to learn and perform better.

Who is the Course for?

The course is for individuals aspiring to become Artificial Intelligence (AI) Engineers, whether they have a background in computer science, data science, or related fields, or are newcomers eager to explore AI technologies. It’s ideal for software developers, data analysts, and anyone interested in building AI applications, and machine learning models, and understanding the underlying algorithms that drive intelligent systems.

What you will learn: The fundamentals of artificial intelligence and its various branches (machine learning, deep learning, natural language processing), Key algorithms and models used in AI, including supervised and unsupervised learning, Programming languages and tools commonly used in AI development (e.g., Python, TensorFlow, PyTorch), Techniques for data preprocessing, feature selection, and data augmentation, Building, training, and evaluating machine learning models, Implementing deep learning architectures, including neural networks and convolutional networks, Understanding and applying natural language processing (NLP) techniques, Ethical considerations and biases in AI systems, Deploying AI models in production environments, Keeping up with the latest trends and advancements in AI technologies

Skills you will acquire

• Proficiency in programming languages relevant to AI, such as Python
• Understanding and applying machine learning algorithms and models
• Data preprocessing, feature engineering, and data augmentation techniques
• Building, training, and fine-tuning machine learning models
• Designing and implementing deep learning architectures (e.g., neural networks, CNNs)
• Utilizing natural language processing (NLP) techniques for text analysis
• Evaluating model performance and conducting error analysis
• Deploying AI models in production environments
• Identifying and addressing ethical considerations and biases in AI
• Staying updated with current trends and advancements in artificial intelligence technologies

Industry you can work in with the skills: Same as the above but not limited to that