Machine learning has revolutionized the way we approach problem-solving and decision-making. As a subset of artificial intelligence, it has enabled us to tackle complex challenges in fields as diverse as healthcare, finance, and entertainment. However, the vast array of mathematical & statistical theoretical backgrounds, algorithms, frameworks, and libraries can be overwhelming.
In this Series, we’ll spend time understanding the fundamentals of machine learning by exploring topics such as supervised learning, unsupervised learning, deep learning, neural networks, and natural language processing. We’ll cover popular programming languages for machine learning such as Python, R, and Julia, along with widely used machine learning frameworks such as TensorFlow, PyTorch, and Scikit-Learn. We’ll build multiple hands-on step-by-step Guided Projects & Portfolio Projects that will hopefully help us understand what ML is all about, and where it’s heading throughout the next few years.
Whether we’re just starting or already have some projects under our sleeve, this Series will provide us with the technical knowledge and skills to quickly build intelligent applications. By the end of this Series, we’ll be able to work with ML algorithms confidently, build neural networks from scratch, deploy pre-trained models, perform various predictive analyses on large datasets, and much more.