Learn about Supervised Machine Learning Algorithms and Unsupervised Machine Learning Algorithms with Part I of this series.
Difference between machine learning and deep learning
Machine Learning models lack the mechanism to identify errors, in such cases the programmer needs to step in to tune the model for more accurate decisions, whereas deep learning models can identify the inaccurate decision and correct the model on its own without human intervention.
But for doing so, deep learning models require a huge amount of data and information, unlike Machine Learning models.
Prerequisites to learn machine learning
There are some prerequisites to learning machine learning without which one will be deprived of the important concepts needed to proceed with learning the same. These are:
Statistical concepts are essential in machine learning to create models from data. Statistics such as analysis of variance and hypothesis testing are crucial for building algorithms.
Probability helps in predicting future consequences, and the majority of the algorithms in machine learning are based on uncertain conditions where reliable decisions are needed.
Data modelling enables identifying the underlying data structures, finding out the patterns and filling the gaps between the places where data is nonexistent.
We are all aware that machine learning mostly depends on algorithms, which means one should possess sound knowledge of at least one of the programming languages. Python is considered an easy language to master, and also, is used by most of the quants.
Python libraries for machine learning
Python libraries help with eliminating the need to write code from scratch. They play a vital role in developing machine learning models as they need algorithms. Let us take a look at some of the most popular libraries below.
It is a Python Machine Learning library built upon the SciPy library and consists of various algorithms including classification, clustering and regression, and can be used along with other Python libraries like NumPy and SciPy for scientific and numerical computations.
Some of its classes and functions are sklearn.cluster, sklearn.datasets, sklearn.ensemble, sklearn.mixture etc.
TensorFlow is an open-source software library for high-performance numerical computations and machine learning applications such as neural networks. It allows easy deployment of computation across various platforms like CPUs, GPUs, TPUs etc. due to its flexible architecture. Learn how to install TensorFlow GPU here.
Keras is a deep learning library used to develop neural networks and other deep learning models. It can be built on top of TensorFlow, Microsoft Cognitive Toolkit or Theano and focuses on being modular and extensible.
Stay tuned for the next installment in this series to learn about the Common terms used in machine learning.
Visit QuantInsti for additional information on this topic: https://blog.quantinsti.com/machine-learning-basics/.
Disclosure: Interactive Brokers
Information posted on IBKR Traders’ Insight that is provided by third-parties and not by Interactive Brokers does NOT constitute a recommendation by Interactive Brokers that you should contract for the services of that third party. Third-party participants who contribute to IBKR Traders’ Insight are independent of Interactive Brokers and Interactive Brokers does not make any representations or warranties concerning the services offered, their past or future performance, or the accuracy of the information provided by the third party. Past performance is no guarantee of future results.
This material is from QuantInsti and is being posted with permission from QuantInsti. The views expressed in this material are solely those of the author and/or QuantInsti and IBKR is not endorsing or recommending any investment or trading discussed in the material. This material is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation to buy, sell or hold such security. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.
In accordance with EU regulation: The statements in this document shall not be considered as an objective or independent explanation of the matters. Please note that this document (a) has not been prepared in accordance with legal requirements designed to promote the independence of investment research, and (b) is not subject to any prohibition on dealing ahead of the dissemination or publication of investment research.
Any trading symbols displayed are for illustrative purposes only and are not intended to portray recommendations.