Develop Python-based machine learning models and AI applications while exploring automation and data prediction techniques.
Master Python for data analysis, machine learning, and automation. Build predictive models, create dynamic dashboards, and unleash the power of data visualization. Launch your career in data science and Python engineering, equipped with Python, NumPy, Pandas, and Matplotlib.
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Learn the fundamentals of machine learning, including regression analysis and classification algorithms, in this practical, hands-on course. Gain the skills needed to solve real-world problems using machine learning, with a focus on Python programming and data science libraries.
Get the skills you need for a career in finance technology with the FinTech Bootcamp. Learn Python programming, data science, financial analysis, data visualization, and machine learning to become a Financial Analyst, Data Scientist, or Data Analyst.
Machine learning (ML) algorithms are programs that learn from massive amounts of past data and experiences to optimize themselves in finding the best solution. It is a subset of artificial intelligence because programmers do not need to explicitly tell ML algorithms how to deal with every possible scenario they could encounter. They can “think” for themselves by relying on past experiences and data to generate new solutions. ML programs are becoming increasingly common and present in all aspects of our lives. It is highly probable you interacted with ML programs without even thinking about it.
There are many different types of machine learning algorithms that you use daily. Streaming services like Spotify and Netflix use recommendation engines, search engines power Google, and voice assistants like Siri and Alexa use natural language processing. When you use these services, they are trying to collect as much data from you as possible to feed their ML algorithms. The data points they collect will help ML programs make better predictions on what you are most likely to watch, search, or listen to in the future.
ML algorithms are trained in three different ways: supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is the most common way to train a model, and it uses labeled data to tell algorithms which patterns to look out for. Even though it sounds simple, it can still be a powerful predictor under certain circumstances, and programmers can see the model’s logic clearly. In unsupervised learning, the data has no labels, and the models are free to find patterns as they see fit. The drawback to unsupervised learning is that programmers often cannot see the model’s logic. It is akin to a black box that you can feed data in and see the output but not the transformation in between. Finally, reinforcement learning is a method in which the model learns through trial and error. It will try various methods to classify or predict outcomes but reward the methods it took to achieve desirable results while penalizing steps that performed worse. Google’s AlphaGo is based on reinforcement learning, and it arguably became the best Go player in the world by playing itself.
Within the machine learning umbrella, there is another subset called deep learning (DL) that was invented in 1986 that is making a fashionable comeback. Although it is superb at finding patterns in unstructured and unlabeled data, it recently became practical to use due to advances in computing. DL’s design was inspired by the human brain and relies on layered nodes (which could be considered neurons in a human brain) to find patterns in an iterative process. DL models learn distinct combinations of features that are associated together in each node and use this knowledge to test against data they haven’t seen before. The programs measure how well they did in identifying new objects and adjust accordingly to improve themselves. DL excels at finding patterns in unstructured data, which makes it very useful in real-world applications.
Machine learning programs affect our daily lives from detecting fraudulent financial transactions, recommending which movies and songs we’ll like next, to interpreting our voices into commands computers will understand. Fortunately, it’s never been easier to learn how these programs work and build them ourselves. It’s just a matter of trial and error, right?