Python for Machine Learning

Machine learning is transforming the overall workplace. Top Companies like Facebook, Google and Amazon are looking for machine learning engineers. The average salary for a machine learning engineer is $150,000 per annum in the United States.

Some people call it business intelligence. Some call it business analytics. So intelligence and learning are interconnected. Through learning, you get intelligence. We have data in our hands, our job is to find out every valuable insight that can be extracted from that data to solve various business problems. So when humans do that, it is called human learning. When a machine does that, it is called machine learning, knowing everything from data. We don’t program or give instructions. We just have data and a variety of algorithms. The machine has to discover patterns, so that method of discovering relevant significant patterns out of data is defined as Machine Learning through which strategic business decision making is done.

For example, Airlines incur huge revenue losses in case they have vacant seats, so with the help of python for machine learning, we can discover those patterns and how we can reschedule those flights to save airlines from incurring heavy losses. Understanding the pattern of why people cancel flights from past historical data.

Another example is a person suffering from diabetes, which is a combination of genetics and lifestyles. How do we know what causes diabetes? Data shows that a sedentary lifestyle causes diabetes. From the above two examples, it is clear discovering patterns from data is machine learning.

For those patterns which are undiscoverable from the human brain, machine learning is a game-changer. Attrition does not happen overnight. What causes people to leave a particular company there is a hidden pattern. Consumers are buying certain products in a supermarket. Consumer buying decisions are dependent on consumer behaviour. There are great differences in consumer behaviour while buying a car versus buying biscuits. Market researchers try to understand consumer behaviour. The first step in python for machine learning is data visualisation, where we understand through visual inspection what this data is trying to convey.

Various tools in visualisation apart from Python for machine learning are Tableau, Power BI and Qlik etc. These three tools are mentioned in the Gartner report, known as the Gartner magic quadrant. Various types of machine learning are Supervised Machine Learning and Unsupervised Machine Learning. The dependent variable is the variable about which we want to discover a pattern. Rest are independent variables, so in this case, we are talking about supervised machine learning.

To quote an example for the same to calculate attrition pattern, we have a target column as a dependent variable, and the rest causes are independent variables. Where we don’t have the dependent variable, we adopt an unsupervised machine learning route. When there is no apparent variable which we can call the dependent variable, we adopt an unsupervised machine learning route. Now let’s talk about supervised machine learning. If your dependent variable should be categorical in nature, we use a classification algorithm. For example, when the dependent variable is when a person will cancel the flight or not cancel the flight. Independent variables in a classification problem can be numerical or categorical. If your dependent variable is numerical, then we use the Regression algorithm. For example, to calculate the GDP of the nation. Here your dependent variable could be numeric, independent variable is time, for example, rainfall over the hundred year’s pattern.

Now, let’s talk about unsupervised machine learning, here we use an algorithm like Clustering.

Clustering is an unsupervised machine learning method of identifying and grouping similar data points in larger datasets without concern for the specific outcome. Another algorithm used in unsupervised machine learning is Association Rule Discovery; Market Basket Analysis is the subset of ARD. Machine learning is also useful in analysing various other data formats like text data, images, and audio data.

In short, python for machine learning simply means visualising the relevant data, applying the right classification of algorithms, identifying numeric or non-numeric data and ultimately discovering the patterns Benefits that make Python the best robust for machine learning and AI-based projects consist of ease and consistency, access to great libraries and frameworks for AI and machine learning, flexibility, platform independence, and a wide community. These add to the overall popularity of the language.

Python for machine learning provides many such libraries. So if you want to do numerical computing, you have a package named numpy. Numpy stands for Numerical Python, and it is the core library for numeric and scientific computing. So you can manipulate them, slice them and extract information from these multidimensional arrays. To import any library in Python, we use the import keyword, then we name the library and then we have to provide an alias, so by convention, numpy is written as np, so the actual syntax for the same is import numpy as np to invoke numpy library. So you can create a single-dimensional numpy array and multidimensional numpy array (a list within a list). If you want to do visualisation, you have many libraries like matplotlib, seaborn, plotly and so on. If you want to do data manipulation, you get pandas.

If you want to implement machine learning algorithms, you got Scikit-learn. Scikit-learn was built on top of two Python libraries – NumPy and SciPy and has become the trendiest Python for machine learning library for developing machine learning algorithms. If you want to go deep learning, you have TensorFlow, Keras. The purpose of these libraries is that the user does not have to write the entire code from the beginning. It provides inbuilt functions to make your work easier.

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