Quant AI

Typically, industrial solutions in finance are simpler as compared to the cutting-edge research work going in the field of machine learning and AI. Overall, focus in the finance industry is more on the practical issues and customizing the tools and framework available to suit the requirement of the problem at hand, rather than coming up with cutting edge models. Hence, individuals with backgrounds in computer science, statistics, maths, financial engineering, econometrics and natural sciences should be able to reinvent themselves to work as machine learning experts in the finance industry.

All three kinds of machine learning algorithms including supervised, unsupervised and reinforcement learning are used in finance. Although most of the literature and discussion so far has been around supervised learning, unsupervised and reinforcement learning are also picking up pace in terms of use cases in finance. Additionally, NLP, which is a subset of AI and shares some common algorithms with machine learning, is currently used extensively in finance.

Deep learning is a subset of machine learning and machine learning is a subset of AI (Artificial Intelligence). Data science although is not a subset of machine learning but there are a lot of common elements between data science and machine learning. All these areas are extensively used in finance.

Many programming languages, especially Python, provide methods and ways to implement machine learning models in a few lines of code. Some of the libraries in Python, especially scikit-learn and keras provide easier methods to implement deep learning algorithms, perform data processing and visualization. The training of the deep learning models can easily be performed using GPU and cloud services. The machine learning concepts and the steps in the case studies throughout the book come with detailed python code and related explanation.

The reinforcement learning algorithms that empowered “AlphaGo” are also finding inroads into finance. Reinforcement learning’s main idea of “maximizing the rewards” aligns beautifully with the core motivation of several areas within finance including algorithms trading and portfolio management.

There is a saying “If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake” which summarizes the importance of unsupervised learning, which is applicable to finance as well. Unsupervised learning models are categorized as clustering or dimensionality reduction models and are used across many areas in finance.

We respect your time, and hence, we offer concise but effective short-term courses created under professional guidance. We try to offer the most value within the shortest time. Please check the price of the course before enrolling in it. Once a purchase is made, we offer complete course content. For more details on the refund policies see Click Here

Yes, you can ask your queries related to the course on the community. We try our best to reply to the questions asap. However, we might need 2-3 business days in answering the questions. It might take longer in case of complicated questions. Additionally, the python ecosystem, APIs and the functions keep on changing quite frequently. Although, we try to be up to date with the latest setup, but some issues due to the changing python ecosystem is expected.

Yes. We provide the certificate after completion of all the quizzes in the course.