Lets Talk Machine Learning

Machine Learning (ML) is a subfield of artificial intelligence that enables computers to learn and make predictions or decisions without explicit programming. Lets explore the fundamentals of Machine Learning, and the steps to study and understand this exciting field.

ML focuses on the development of algorithms and models that learn patterns and relationships from data. By analysing large amounts of data, ML algorithms can improve their performance over time, allowing computers to make accurate predictions and decisions.

If  like many of us you stopped thinking about study the minute your school days were over,  you may need a  refresher course or three. There are a multitude of free ones available. I encourage you to search for the one that suits your learning style. Decide on a subject, decide your goal outcome. Free courses allow you to dip your toes into the water and see how far you can go before you decide to wade back onto the shore.

What is the worse case scenario in choosing to study?  It helps with memory, provides a positive outlet to our need to engage with technology, and  is a huge benefit to keep learning and studying as we age.

Remember, it takes work and consistency.  Studying a little each day is better than ignoring it for a month and then trying to overload the senses with a full weekend of cramming.

Make a conscious decision to choose learning over scrolling your favourite social media app or watching a rerun of "Friends".

The internet offers a wide selection of excellent courses, both free and paid, that cover various aspects of computer science and its sub-branches. Start with the basics and just keep diving. It is helpful to understand several key concepts.

Data: ML relies on vast amounts of data as input for training and evaluation.

Feature: Features represent the measurable properties or characteristics of data that ML algorithms analyse.

Label: Labels are the desired outputs or target values associated with the data, used for supervised learning.

Model: ML models are mathematical representations that learn patterns in the data and make predictions or decisions.

Training: Training involves providing labelled data to ML algorithms to optimize the model's parameters.

Testing and Evaluation: ML models are assessed using testing data to measure their performance and generalisation abilities.

Fundamentals of ML

Supervised Learning: Explore classification and regression algorithms such as linear regression, logistic regression, decision trees, and support vector machines.

Unsupervised Learning: Study clustering algorithms like K-means and dimensionality reduction techniques like principal component analysis (PCA).

Evaluation metrics:  Understand common metrics for assessing ML model performance, such as accuracy, precision, recall, and F1-score.

Mathematics and Statistics Foundations.

Linear algebra: Learn the principles of linear transformations, vector spaces, and matrix operations.

Calculus: Understand differential and integral calculus, particularly optimization techniques.

Probability and statistics: Study probability theory, random variables, distributions, and statistical inference.

Programming Skills

Python:  Master the Python programming language and ML libraries like NumPy, Pandas, and scikit-learn.

Data manipulation:  Gain proficiency in pre-processing, cleaning, and visualizing data using libraries like Pandas and Matplotlib.

Advanced ML Techniques

Deep Learning: Dive into neural networks, deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and frameworks like TensorFlow or PyTorch.

Reinforcement Learning: Learn about Markov decision processes, Q-learning, policy gradients, and explore RL frameworks.

Remember also, YouTube is your friend. It is overflowing with brilliant tutorials.

"Don't stop until you are insanely fucking proud of yourself"  

-  Gary Vee