Machine Learning with Python Python is a popular platform used for research and development of production systems. It is a vast language with number of modules, packages and libraries that provides multiple ways of achieving a task. Python and its libraries like NumPy, SciPy, Scikit-Learn, Matplotlib are used in data science and data analysis. They are also extensively used for creating scalable machine learning algorithms. Python implements popular machine learning techniques such as Classification, Regression, Recommendation, and Clustering. Python offers ready-made framework for performing data mining tasks on large volumes of data effectively in lesser time. It includes several implementations achieved through algorithms such as linear regression, logistic regression, Naïve Bayes, k-means, K nearest neighbor, and Random Forest. Python in Machine Learning Python has libraries that enables developers to use optimized algorithms. It implements popular machine learning techniques such as recommendation, classification, and clustering. Therefore, it is necessary to have a brief introduction to machine learning before we move further. What is Machine Learning? Data science, machine learning and artificial intelligence are some of the top trending topics in the tech world today. Data mining and Bayesian analysis are trending and this is adding the demand for machine learning. This tutorial is your entry into the world of machine learning. Machine learning is a discipline that deals with programming the systems so as to make them automatically learn and improve with experience. Here, learning implies recognizing and understanding the input data and taking informed decisions based on the supplied data. It is very difficult to consider all the decisions based on all possible inputs. To solve this problem, algorithms are developed that build knowledge from a specific data and past experience by applying the principles of statistical science, probability, logic, mathematical optimization, reinforcement learning, and control theory.
Applications of Machine Learning Algorithms The developed machine learning algorithms are used in various applications such as − • Vision processing • Language processing • Forecasting things like stock market trends, weather • Pattern recognition • Games • Data mining • Expert systems • Robotics Steps Involved in Machine Learning A machine learning project involves the following steps − • Defining a Problem • Preparing Data • Evaluating Algorithms • Improving Results • Presenting Results The best way to get started using Python for machine learning is to work through a project end-to-end and cover the key steps like loading data, summarizing data, evaluating algorithms and making some predictions. This gives you a replicable method that can be used dataset after dataset. You can also add further data and improve the results. Libraries and Packages To understand machine learning, you need to have basic knowledge of Python programming. In addition, there are a number of libraries and packages generally used in performing various machine learning tasks as listed below −
• numpy − is used for its N-dimensional array objects • pandas − is a data analysis library that includes dataframes • matplotlib − is 2D plotting library for creating graphs and plots • scikit-learn − the algorithms used for data analysis and data mining tasks • seaborn − a data visualization library based on matplotlib Installation You can install software for machine learning in any of the two methods as discussed here − Method 1 Download and install Python separately from python.org on various operating systems as explained below − To install Python after downloading, double click the .exe (for Windows) or .pkg (for Mac) file and follow the instructions on the screen. For Linux OS, check if Python is already installed by using the following command at the prompt − $ python --version. ... If Python 2.7 or later is not installed, install Python with the distribution's package manager. Note that the command and package name varies. On Debian derivatives such as Ubuntu, you can use apt − $ sudo apt-get install python3 Now, open the command prompt and run the following command to verify that Python is installed correctly − $ python3 --version Python 3.6.2 Similarly, we can download and install necessary libraries like numpy, matplotlib etc. individually using installers like pip. For this purpose, you can use the commands shown here − $pip $pip $pip $pip install install install install numpy matplotlib pandas seaborn Method 2