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My 2023 Roadmap: Backend & AI

January:

• Start a Django project.

Key Goals: routers, templates, forms, db integration

• Further familiarize with microservices and containers.

Key Goals: Docker containerization, deploying microservices to Kubernetes

February:

• Continue Django, and start building a full-stack web application using Python.

• Learn about database design and optimization.

Key Goals: indexing, sharding, and replication

March:

• Continue working on the full-stack web application, and start learning about cloud computing and deployment.

Key Goals: cloud architecture, security, scaling in cloud

• Gain experience with a NoSQL database.

April:

• Deploy full-stack web application to a cloud platform.

• Begin learning about machine learning and data science.

Key Goals: supervised & unsupervised learning, data preprocessing, model evaluation

May:

• Build a machine learning project using a library.

• Learn about natural language processing.

Key Goals: text classification, tokenization, named entity recognition

June:

• Build a natural language processing project using a library.

• Learn about computer vision.

Key Goals: image processing, object detection, facial recognition

July:

• Build a computer vision project using a library.

• Consider contributing to an open-source project.

August:

• Participate in online coding challenges or hackathons to improve skills and gain exposure to new technologies and techniques.

September:

• Continue working machine learning, natural language processing, or computer vision project, or start a new one.

October:

• Continue learning about the back end and AI development in Python while staying up to date with the latest tools and technologies.

November:

• Continue learning and building projects in the areas of back-end and AI development in Python.

• Check the current progress and set goals for the next year.