Machine Learning Basics: Components, Application, Resources and More – Part II

Articles From: QuantInsti
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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

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

Data modelling enables identifying the underlying data structures, finding out the patterns and filling the gaps between the places where data is nonexistent.

Programming Skills

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.

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