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Start for freeEmbark on the AI Gold Rush: Your Path to Becoming an AI Engineer
The artificial intelligence (AI) industry is experiencing an unprecedented boom, with companies investing billions into AI projects. This surge has created a gold rush for a specific career role that stands to benefit immensely: the AI Engineer. Drawing from my tenure at LCK Technologies, Bloomberg, and Nvidia, I'll guide you through an actionable week-by-week study plan to start your journey as an AI Engineer, using entirely free learning resources.
AI engineers are among the highest-paid professionals in the tech industry. However, attaining such a position demands substantial dedication and effort, as employers have high expectations correlating with the salary range offered. Preparing for a career in AI is no small feat; it requires at least four hours of dedicated study daily for eight months, setting a solid foundation for what is a lifelong learning process.
Is AI Engineering the Right Path for You?
Before diving into the roadmap, it's crucial to assess whether AI engineering suits your interests and skills. Proficiency in coding and math is indispensable in this field. If these areas don't spark your interest or if you lack skills in them, there are alternative career paths within AI, such as AI sales representative, AI product manager, or AI ethics executive.
Starting Your Journey: A Week-by-Week Guide
Weeks 1-2: Begin with computer science fundamentals. If you lack a Computer Science background, Khan Academy offers a comprehensive course covering the essentials.
Weeks 3-4: Delve into Python, the most popular programming language in the AI sphere. Start with basic concepts and gradually move to advanced topics. For Python tutorials, channels like mine and Cory Schafer's on YouTube are excellent resources.
Weeks 5-6: Focus on data structures and algorithms, crucial for writing scalable programs. Again, YouTube provides valuable playlists for learning and practicing these concepts.
Weeks 7-8: Advance your Python skills by exploring inheritance, generators, iterators, list comprehensions, multi-threading, and multiprocessing. These concepts are vital for processing large volumes of data efficiently.
Building Your Professional Presence
While acquiring technical skills, concurrently start building a strong LinkedIn profile. Follow influential AI professionals, actively engage in meaningful discussions, and post valuable comments to establish a network within the AI community.
Venturing into Specializations
Weeks 10-11: Learn SQL and relational databases, foundational for handling the data required to train AI models.
Weeks 12-17: Dive into NumPy and Pandas for data cleaning and exploration, followed by a deep dive into math and statistics, the bedrock of AI.
Weeks 18-21: Focus on machine learning, covering pre-processing and model building techniques. Utilize YouTube playlists and engage in practical exercises.
Weeks 22-24: Explore MLOps, learning about APIs, FastAPI, Docker, Kubernetes, and cloud platforms like AWS or Azure. MLOps is crucial for automating machine learning project development.
Weeks 25-27: Engage in building machine learning projects, both regression and classification types, to apply your learning practically.
Weeks 28-30: Choose to specialize further in either Natural Language Processing (NLP) or Computer Vision, akin to choosing a medical specialty.
Continuous Learning and Networking
The journey doesn't end after eight months. Continuous learning and networking are crucial for securing a job in this competitive field. Build an ATS-compliant resume, create a project portfolio website, and continue to engage on LinkedIn and other platforms to showcase your work and connect with potential employers.
Effective Learning Strategies
Effective learning requires a balance between consuming tutorials and implementing knowledge. Spend less time watching videos and more on practicing, sharing, and group learning. Engaging with a community, such as through the Discord server, can provide valuable support and feedback.
Conclusion
Becoming an AI Engineer is a challenging yet rewarding path. This 8-month roadmap is designed to guide you through the necessary steps, leveraging free resources and a community of learners. Remember, the key to success in AI is continuous learning and practical application of skills.
For detailed resources and the complete roadmap, watch the original video here.
