My motivations, experiences and future plans for a machine learning education
At the end of my 2018 sabbatical, I committed to continue my exploration of new technologies, and I began studying machine learning.
Looking back, I never imagined where that journey would take me. Fast forward to today, and I have completed courses in deep learning, got into the top 10% of a simple machine learning competition and obtained the AWS Certified Machine Learning Specialty. This experience has been both challenging and rewarding, and I am eager to share my insights and growth with others who are just starting their machine learning journey. Here is my original article with updates for 2022. This post is also a resourceful guide to machine learning for beginners as I walk through my own learning journey.
Keen to understand more about this field and it’s potential I was quickly overwhelmed in detail. Machine learning is a vast topic, and it was quite daunting to know where to begin. Do you start with the maths or the code? Do I need a PhD?
After a false start, there were three things I learned: Don’t try and learn this fast, start with some good learning material and practice, practice, practice. These are relatively obvious, but in my eagerness, to progress, I lost sight of these study principles.
Artificial Intelligence is everywhere, and companies are betting their operating model on it. AI will enrich our lives while taking our jobs. Machine learning for business is rapidly becoming indispensable; it's the hottest topic, and there is already a shortage of skills. I felt it was time to get a better technical understanding of this field, starting with Machine Learning.
Kai-Fu Lee author of AI Superpowers describes the current moment as “the age of implementation”, where the technology starts “spilling out of the lab and into the world.” Finding the right level of Machine Learning information was difficult. There were either the high-level documents which overlay generalised Machine Learning or detailed explanations. It resulted in much mathematics, long equations and Machine Learning concepts that are hard to understand in isolation.
Why did I want to learn about Machine Learning?
I have always had a strong desire for continuous learning, and I saw Machine Learning as a new challenge and opportunity for growth.
To use Machine Learning as a tool that can augment human capabilities, translating theory into practice to solve real problems.
I started with this course because I’ve had taken Frank Kane’s courses before, and I liked the practical approach. His experience with Amazon and IMDB provides real-world examples where Machine Learning can be applied. The course doesn’t have academic, deeply mathematical coverage of Machine Learning algorithms, the focus is on a broad practical understanding, and it’s the application. Since I wrote this blog, the course has been updated in 2022.
This course will teach you techniques to select and clean your data, supervised and unsupervised Machine Learning algorithms, how to evaluate metrics, deep learning and neural networks. The course does provide an overview of Python, which was enough, but non-coders should consider a Python course before this. Frank covers almost all topics of which you need to be aware of before diving deep into this field.
As I was new to Python, I took this course to tailor my Python skills towards data science. There are a few Python specialisations. As I didn’t want to focus on a particular platform, this one drew my attention because it wasn’t (IBM/Google/AWS). It’s a five-course specialisation that focuses on the programming rather than the theory or mathematics.
The course taught me data manipulation and data cleaning techniques, plotting and data representation. IT covers Machine Learning, Natural Language processing and social network, graph theory with Python libraries scikit-learn, Natural Language Toolkit (NLTK) and NetworkX.
Each course builds on the other, and you will be building on the techniques as you progress through the series. The assignments are peer-graded, giving you different perspectives on how people approach the same problem.
My motivation for Andrew NG’s course was to understand what’s under the hood of Machine Learning and the intuition behind the algorithms. This course is cited as one of the best introductions on the theory and concepts behind Machine Learning. It covers supervised learning, unsupervised learning, deep networks and the best practice without overwhelming you with the underlying mathematics.
You don’t need in-depth knowledge of linear algebra or calculus to complete the course. Still, if you want to study mathematics, this course will provide the foundations for further training.
Since I wrote this blog, the course has been updated in 2022. The programming assignments are now in Python using TensorFlow or PyTorch, providing a shift from Octave or Matlab. This detail level helps you better understand Python Machine Learning libraries like scikit-learn.
There is an outstanding community around this course. The forums provide detailed explanations for many of the problems you will encounter, and it was the perfect complement to the more practical courses I took.
Is a strong background in mathematics necessary for learning machine learning?
As I was starting out in Machine Learning, my goal was to learn enough mathematics to understand and code the algorithms. I took the approach to learn mathematics on demand, first familiarising myself with the algorithms, then studying the mathematics behind them and finally translating the algorithms to code. I ended up with an understanding of basic linear algebra and essential calculus and a right balance of theory and practice.
If mathematics is a challenge, start by learning Linear Regression and Logistic Regression algorithms. These will introduce you to the cost/loss function and gradient descent and provide a foundation for other algorithms. One good tip was to work out the algorithms in Excel, this helped me get over some of the more challenging concepts.
That said, mathematics is only one part of the overall application of Machine Learning. Having been in the industry for many years, I tend to agree that for practitioners, the main prerequisite for Machine Learning is data analysis. That’s where domain experience domain expertise trumps the mathematics. Being able to identify a problem, selecting objectives and metrics, gathering and cleaning the data forms the greater part of a Machine Learning application.
What is the importance of programming skills in machine learning?
Initially coding the algorithms is frustrating but keep at it because it helps reinforce the understanding. If you’re unfamiliar with Python, I would recommend starting with additional courses. Ones that cover pandas for data manipulation and analysis, numpy for multi-dimensional arrays and matrices and matplotlib for data visualisation. That will give you the foundations to complete the courses mentioned above and start your Python Machine Learning journey.
How does design thinking apply to AI and machine learning?
Machine learning is being adopted in various industries, such as financial services, where Artificial intelligence has the potential to improve our lives substantially. However, it is essential to remember that AI is designed and created by humans for humans. Design thinking is an approach that puts people at the forefront of the design process. When applied to AI, this approach ensures that AI products and services focus on human connection, experience, and needs, fostering innovative design and creativity.
The Design Thinking process involves empathy, definition, ideation, rapid prototyping, and testing. This process encourages collaboration, experimentation, and iteration to arrive at human-centred and innovative solutions that are desirable, feasible, and viable. By using design Thinking to develop AI, we can ensure that the technology is helpful but also intuitive and enjoyable to use, ultimately enhancing the lives of those who interact with it.
Deep Dive into Design Thinking's Integration with Machine Learning
Discover how design thinking can enhance the machine learning pipeline in this blog post Integrating Design Thinking into Machine Learning for Innovative Solutions. Learn about problem framing, empathy, ideation, prototyping, testing, iteration, implementation, monitoring, explainability, trust, and ethics. This resource offers valuable insights for developing human-centric machine learning models.
How does LLM AI relate to Machine Learning?
Large Language Models (LLMs) are a testament to the advancements in machine learning. At their core, LLMs utilise deep learning, a subset of machine learning, leveraging intricate artificial neural networks reminiscent of the human brain's structure. These networks enable LLMs, especially transformer models, to learn from vast datasets of text and code. As a result, LLMs excel in tasks such as machine translation, text summarisation, and question answering. Furthermore, their prowess in generative AI empowers them to create coherent and contextually relevant content. In essence, LLMs epitomise the union of machine learning's predictive power with deep learning's ability to decipher and generate human language.
What’s Next ?
You can follow the progress and dive into deep learning in the next blog post. Starting out in Deep Learning including deep learning ai and AWS sagemaker.
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