The AI Practitioner: Optimizing AI Solutions

The AI Practitioner: Optimizing AI Solutions

• discover the key concepts covered in this course
• define AI optimization and its importance in relation to the AI Practitioner role
• specify the types of AI optimization and describe key differences in the approaches
• identify key benefits of and improvements that can be achieved by AI optimization
• describe the principle of Gradient Descent optimization in AI and cases in which it is used
• describe the principle of Stochastic Gradient Descent optimization in AI and specify cases in which it is used
• describe the principle of Momentum optimization in AI and specify cases in which it is used
• describe the principle of AdaGrad optimization in AI and specify cases in which it is used
• describe the principle of RMSprop optimization in AI and specify cases in which it is used
• describe the principle of Adam optimization in AI and specify cases in which it is used
• describe the principle of AdaMax optimization in AI and specify cases in which it is used
• implement Gradient Descent Optimization algorithm from scratch using Python libraries and describe how algorithm convergence achieves loss minimization goal
• implement AdaGrad Optimization algorithm from scratch using Python libraries and specify formatting for inputs and outputs of the computation
• summarize the key concepts covered in this course

Optimization is required for any AI model to deliver reliable outcomes in most of the use cases. AI Practitioners use their knowledge of optimization techniques to choose and apply various solutions and improve accuracy of existing models. In this course, you'll learn about advanced optimization techniques for AI Development, including multiple optimization approaches like Gradient Descent, Momentum, Adam, AdaGrad and RMSprop optimization. You'll examine how to determine the preferred optimization technique to use and the overall benefits of optimization in AI. Lastly, you'll have a chance to practice implementing optimization techniques from scratch and applying them to real AI models.

The AI Practitioner: Role & Responsibilities

The AI Practitioner: Role & Responsibilities

• discover the key concepts covered in this course
• describe the role of an AI Practitioner in a company and identify key responsibilities
• specify the skillset needed to become an AI Practitioner and name commonly used tools
• recognize the role of an AI Practitioner when developing AI products and their relationship with other developers
• compare AI Practitioner to AI Developer and list fundamental differences in their workflows
• compare AI Practitioner to Data Scientist/AI Scientist and list fundamental differences in their workflows
• compare AI Practitioner to ML Engineer and list fundamental differences in their workflows
• compare AI Practitioner to AI Engineer and list fundamental differences in their workflows
• describe the mindset that needs to be adopted by an AI Practitioner to implement AI
• identify the importance of research work for an AI Practitioner
• describe the responsibilities of an AI Practitioner working in the data science domain and list the most important success factors
• describe the responsibilities of an AI Practitioner working in the AI Industry and list the most important success factors to developing AI products
• specify career paths and opportunities for an AI Practitioner, including multiple roles and responsibilities that lead to professional growth
• summarize the key concepts covered in this course

AI Practitioner is a cross-industry advanced AI Developer position that has a growing demand in the modern world. Candidates for this role need to demonstrate proficiency in optimizing and tuning AI solutions to deliver the best possible performance in the real world. AI Practitioners require more advanced knowledge of algorithm implementations and should have a firm knowledge of latest toolsets available. In this course, you'll be introduced to the AI Practitioner role in the industry. You'll examine an AI Practitioner's skillset and responsibilities in relation to AI Developers, Data Scientists, and ML and AI Engineers. Finally, you'll learn about the scope of work for AI Practitioners, including their career opportunities and pathways.

The AI Practitioner: Tuning AI Solutions

The AI Practitioner: Tuning AI Solutions

• discover the key concepts covered in this course
• describe the role and importance of hyper parameters in AI development
• describe the process of hyper parameter tuning and list multiple approaches to the process
• describe the role of hyper parameters in common machine learning models and approaches
• describe the role of hyper parameters in deep learning neural network models
• specify how to tune hyper parameters using a Grid Search approach
• specify how to tune hyper parameters using a Random Search approach
• specify how to tune hyper parameters using Bayesian method
• specify how to tune hyper parameters based on gradient
• specify how to utilize evolutionary hyper parameter tuning
• name multiple libraries that allow for hyper parameter tuning and describe how to use these libraries
• work with the Python Grid Search algorithm for hyper parameter tuning of a machine learning model to configure optimal parameters and recognize an increase in accuracy
• work with the Python Random Search algorithm for hyper parameter tuning of a machine learning model to configure optimal parameters and describe the advantages of using the Random Search algorithm
• summarize the key concepts covered in this course

Tuning hyper parameters when developing AI solutions is essential since the same models might behave quite differently with different parameters set. AI Practitioners recognize multiple hyper parameter tuning approaches and are able to quickly determine best set of hyper parameters for particular models using AI toolbox. In this course, you'll learn advanced techniques for hyper parameter tuning for AI development. You'll examine how to recognize the hyper parameters in ML and DL models. You'll learn about multiple hyper parameter tuning approaches and when to use each approach. Finally, you'll have a chance to tune hyper parameters for a real AI project using multiple techniques.