Decoding AI: An iOS Dev’s Roadmap to Becoming an AI Engineer

In my last article, I described my upcoming summer focus on learning more about artificial intelligence (AI). It will be a summer that will unveil new possibilities for me as a programmer. Therefore, in this article, I will share my more detailed roadmap for this education within AI – a roadmap designed to help me transition my skills as a traditional programmer(IOS) into the skills required by an AI engineer.

1. Learn the Basics of AI and Machine Learning

AI is a complex and broad discipline, spanning several different subfields. One of these is machine learning, where computers learn from data to improve their performance. This is my first step – gaining a general understanding of AI and machine learning.

Here I will use the links provided here and continue reading “The Hundred-Page Machine Learning Book” by Andriy Burkov

2. Learn AI Programming Languages

As an iOS developer, I’m very familiar with Swift and Objective-C, but to dive deeper into the world of AI, it’s necessary to learn Python..or maybe Mojo. This language is prevalent within data analysis and machine learning due to its readability and wide range of scientific and mathematical libraries. My journey continues with building my Python competence and delving into libraries like NumPy, Pandas, and Matplotlib, using the

3. Use AI Libraries and Tools & Work with Datasets

Once comfortable with Python, I will begin exploring libraries such as Scikit-Learn, TensorFlow, and PyTorch, which are vital tools for any AI engineer. Parallel to this, I will start working with datasets and start building very simple machine learning models to gain practical experience.

4. Learn about Neural Networks

After building a base in machine learning, I will begin exploring topics like neural networks and deep learning. These complex machine learning models mimic how the human brain processes information and form the foundation of many advanced AI systems.

Here I intend to look at the “Deep Learning Specialization” on Coursera, created by Andrew Ng, this is a five-course sequence that includes courses on neural networks, deep learning, structuring machine learning projects, eg.

5. Take Advanced AI Courses

Once I have a basic understanding and practical experience, it’s time to take more advanced courses to delve deeper into specialized AI topics. These may include subjects like natural language processing, image recognition, and reinforcement learning.

Here I have looked at a Coursera online course: “Natural Language Processing Specialization” created by DeepLearning.AI. This course covers traditional NLP topics and deep learning techniques. It should provide a good mix of theory and application.

6. Build AI Projects

No skill is fully mastered without practical experience. Therefore, throughout this process, I will work on practical AI projects to cement my learning

7. Stay Updated & Network

AI is a rapidly changing field, and it’s important to stay abreast of the latest trends. I will also try to build my network with others in the industry through conferences, workshops, and online forums.

8. Become Certified

And finally, when I feel ready…and it might just be a while :-), I will consider obtaining certification in AI or machine learning. Certification can give a significant boost to my CV and help me stand out on the job market.

Empowering iOS Development with AI Expertise

Combining my skills as an iOS programmer with tasks as an AI engineer can open new doors for opportunities. I could develop iOS apps that use AI for things like user behavior analysis and personalization. I can incorporate AI-driven features like image recognition or natural language processing, use machine learning models directly in my mobile apps with tools like Core ML, or create interactive chatbots. In healthcare, I can use AI to analyze data and make personalized recommendations. I could also improve Siri integration using AI, or use AI to analyze user data for trends and insights.So this blend of skills can lead to opportunities in both existing and emerging fields.

This learning plan is the path I have chosen to further my education from a programmer to a AI engineer. It is undoubtedly a very challenging, but also an exciting development. I look forward to embarking on the next chapter of my technology career, as I know it will be a time filled with learning, discovery and undoubtedly a new understanding of the potential that AI holds.

So, that’s the plan! If you’ve got your own AI experiences or know some neat tricks, don’t be shy – put them down in the comments.