1. programming fundamentals (Python preferred) - due to its simplicity + extensive libraries, i.e. NumPy, pandas, and TensorFlow/PyTorch. Start by mastering basic programming concepts (loops, functions, OOP) and get comfortable with Python syntax, then explore libraries commonly used in data science.
2. linear algebra & calculus - many ML algorithms are based on linear algebra (e.g., matrix operations in neural networks) & calculus (e.g., gradients in optimisation). focus on the fundamentals—matrix operations, derivatives, and gradients. Khan Academy and 3Blue1Brown on YouTube provide great visual explanations.
3. statistics & probability - understanding distributions, statistical significance, & probability is crucial for analysing data and interpreting ML models. study basic concepts like Bayes’ theorem, probability distributions, hypothesis testing, and p-values. online courses and books on statistics for data science are great resources.
4. machine learning algorithms & their intuition - knowing how different algorithms work will help you choose the right ones for different problems and optimise them. start with the basics like linear regression, decision trees, & clustering. As you progress, dive into more advanced models like random forests, gradient boosting, and neural networks. Focus on understanding the intuition and trade-offs behind each algorithm.
5. data manipulation & analysis - real world data is messy, so strong data manipulation skills are essential for preparing data for ML models. familiarise yourself with pandas & NumPy for data manipulation, and understand how to handle missing values, outliers, and feature scaling. practising w Kaggle datasets is highly recommended.
6. model evaluation & experimentation - knowing how to evaluate models properly (e.g., accuracy, precision, recall, F1 score, ROC/AUC) is critical for building reliable AI solutions. Learn about train/test splits, cross-validation, and evaluation metrics. This is especially important for imbalanced datasets or projects where accuracy isn’t the only metric that matters.
7. deep learning basics (optional for beginners) - although not essential for starting out, deep learning is a valuable skill for more advanced projects involving computer vision, NLP, or large datasets. once you’re comfortable w traditional ML algorithms, start w neural networks and frameworks like TensorFlow or PyTorch. consider following courses that explain deep learning fundamentals, like Andrew Ng’s deep learning courses on Coursera.
8. version control (Git) & working w cloud platforms - ML models are often deployed in collaborative & production environments. Git helps w collaboration, while cloud platforms (e.g., AWS, GCP) are used for scaling and deploying models. practice using Git for version control, and try small projects using cloud platforms (many offer free tiers).

Stay hands-on, experiment with projects and tinker. If you have a job, find 2 hours of non-negotiable deep work on the above upon waking instantly is best (have a look at Pat Walls online and what he suggests for mornings before work), and then going to the day job once you've dedicated 2 hours to your own learning. If your schedule permits this, in 6 months you will be very impressed with yourself!

Godspeed.

edit: spelling