The Ultimate Learning Path for Machine Learning Enthusiasts

The fastest way to keep up to the rapid development of AI if you have just started today

Introduction

Is AI developing way too fast for us? Pathways to becoming a machine learning working professional is long in 2023, and it will only keep on getting longer and longer.

Machine learning, the foundational subdomain of AI, has become an integral part of various industries, from healthcare to finance and beyond. As the rapid development, it’s essential for aspiring machine learning enthusiasts to follow a structured learning path to most efficiently reach their goals. In this article, I’ll outline a recommended learning path that covers the fundamental concepts and practical skills necessary to embark on a successful machine learning journey, all summarized from my past 5 years of interacting with this field. This will just be an introductory post, more details will be discussed in a subsequent series.

Foundations of Machine Learning

To begin your journey, it’s crucial to establish a strong foundation in the principles and algorithms of machine learning. Start by understanding linear algebra, probability theory, and calculus, as they form the backbone of many machine learning algorithms. Online courses and textbooks are excellent resources for gaining a solid understanding of these concepts. Keep in mind you won’t need to get to the level of exams, just focusing on understanding matrix operations. You will need that to understand gradient descent, and beyond.

Recommended Resource: This Udemy course from John Krohn is great, Mathematical Foundations of Machine Learning, I had the fortune to listen to one of his talks at a conference, he is great at making difficult concepts simple.

Programming and Data Manipulation

Equip yourself with programming skills to implement machine learning algorithms. Python is widely adopted in the machine learning community due to its simplicity and powerful libraries like NumPy, Pandas, and Scikit-learn. Dedicate time to learn Python and practice manipulating datasets, performing data cleaning, and preprocessing tasks. This will allow you to perform well in the process called Exploratory Data Analysis (EDA).

What is Exploratory Data Analysis (EDA)? Before diving into modeling, explore and analyze datasets to gain insights. EDA involves visualizing data, identifying patterns, and understanding the relationships between variables. Tools like Matplotlib, Seaborn, and Plotly can help you create informative visualizations.

Recommended Resource: Another Udemy course, this time from Jose Portilla, Python for Data Science and Machine Learning Bootcamp, bought it in 2017 still very useful today

Three Families of Machine Learning You Should Know

Yes, reinforcement learning is not on the list in this recommendation.

Supervised Learning Algorithms: Supervised learning is the most common type of machine learning, where models learn from labeled data to make predictions or classifications. Start by understanding linear regression and logistic regression, then move on to decision trees, random forests, and support vector machines. Implementing these algorithms in Python and applying them to real-world datasets will deepen your understanding.

Unsupervised Learning Algorithms: Clustering techniques like K-means and hierarchical clustering can help identify patterns and group data points without labeled information. Additionally, dimensionality reduction methods such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) can be valuable tools in preprocessing and visualizing high-dimensional data. In fact, do you know that the infamously powerful GPTs are trained in an unsupervised manner?

Deep Learning: As you progress, delve into the fascinating world of deep learning. Neural networks and deep learning algorithms have revolutionized the field of artificial intelligence. Start by understanding the basics of neural networks, backpropagation, and activation functions. Then, explore popular deep learning frameworks such as TensorFlow or PyTorch and gain hands-on experience by implementing deep learning models for tasks like image classification and natural language processing.

Model Evaluation and Optimization

Don’t overlook this part. Learn how to assess the performance of your machine learning models will allow you to optimize them for better results. Understand metrics like accuracy, precision, recall, and F1-score for classification problems. For regression tasks, metrics like mean squared error (MSE) and R-squared are essential. Techniques like cross-validation and hyperparameter tuning will help you improve your models’ performance. There are more complicated evaluation metrics, but all use them as foundations.

Recommended Resource: The classic Andrew Ng’s Machine Learning is still the best to get started, crazy to think that was first released 10 years ago.

Deployment and Production

The most important part. I found schools often overlook this aspect. To be able to turn models in to products, you have to familiarize yourself with the process of deploying machine learning models in production. Learn how to package and serve models using frameworks like Flask or Django. Explore cloud platforms like Amazon Web Services (AWS) or Google Cloud Platform (GCP) for scalable and reliable model deployment. A lot of my work professionally eventually ended up being a REST API. From that point on the software engineering team would often take over.

Recommended Resource: Udemy better start paying me for promoting these courses XD. This Deployment of Machine Learning Models was my first encounter for the software engineering side of machine learning.

Conclusion

Yes, the path is long. Embarking on a machine learning journey requires dedication, patience, and continuous learning. By following this recommended learning path, you’ll develop a strong foundation in the core concepts of machine learning, gain hands-on experience with various algorithms, and acquire the necessary skills to deploy models in real-world scenarios. Remember, practice and experimentation are key to becoming a proficient machine learning practitioner. So, start today!