Here's a list of the 25 most important AI skills and their subsections:
Machine learning (ML): Proficiency in ML algorithms and techniques for training and deploying models.
Deep Learning: Expertise in deep neural networks and frameworks like TensorFlow and PyTorch.
Natural language processing (NLP): Understand language models and text analytics to accomplish tasks such as sentiment analysis and language generation.
Computer Vision: Knowledge of image and analysis techniques for applications such as image recognition and object detection.
Reinforcement Learning: Master reinforcement learning algorithms for autonomous decision-making in dynamic environments.
Data science: data preprocessing, feature engineering, and data visualization skills for AI model development.
Statistics: A strong statistical foundation to interpret AI model results and validate hypotheses.
Big data technologies: Familiarity with tools such as Hadoop and Spark for working with large data sets.
Cloud computing: Cloud platforms such as Master, Huawei, AWS, Azure, or Google Cloud for scalable AI deployments.
Natural Language Generation (NLG): The ability to generate human-like text content using artificial intelligence.
Speech recognition: Expertise in voice-based AI applications and technologies.
Time series analysis: Skills for ** and anomaly detection using time-related data.
Recommender system: Knowledge of personalized recommendation algorithms.
AI model optimization: Techniques to improve AI model performance, efficiency, and resource utilization.
AI ethics: Understand ethical considerations in AI, including bias reduction and fairness.
Python: Proficiency in Python, the main language for AI development.
R: Familiarity with the R programming language used for statistical analysis.
J**A C++: Proficiency in these languages for AI applications in areas such as robotics and game development.
SQL and NoSQL: Database management skills for storing and retrieving AI-related data.
Data wrangling: Ability to clean, preprocess, and transform data for AI modeling.
Data visualization: Proficiency in data exploration using tools such as Mattplotlib, Seaborn, or Tableau.
Docker and Kubernetes: Knowledge of containerization and orchestration for AI model deployments.
CI CD: Learn about continuous integration and continuous deployment pipelines for AI systems.
Model deployment: The skills to deploy AI models into production.
Communication, collaboration, and problem-solving: Strong soft skills to effectively communicate AI findings, collaborate with cross-functional teams, and solve complex AI challenges.