Python is world’s most cutting edge programming languages today. It is being used in areas like Machine Learning, Artificial Intelligence, Web Development and Data Visualization. It is an open-source, Procedure oriented, Object Oriented, programming tool and is known for its pseudo code nature which means once someone develops the right logic to code in python, it’s almost like writing code in English as there are a lot of pre-defined keywords which have some particular task and could be used while writing the code. Python is the top choice for ML/AI enthusiasts when compared to other programming languages. The latest trends reflect that over 70% of Data Scientists use Python as their go-to programming language.
What is Python best for Machine Learning?
Python could be used to perform Machine Learning operations easily as it contains a lot of modules and packages which contains ML Algorithms. We just have to call the module and fit to our data. Python will do the rest. These modules are specific, organized and generalized pieces of statements and mathematical equations which are used to work on the data.
It has an exceptional libraries and a great tool for developing prototypes. Unlike R, Python could be used to build web applications as it can be connected to Web-Development frameworks like Django. As Python is a free of cost, open source language, lot of developers try to constantly update and make python better by performing some modifications time and again. They have developed libraries that follow a particular area of data science application. For instance, there are libraries available for handling arrays, performing numerical computation with matrices, machine learning, data visualization and many more. These libraries are highly effective and make the coding much easier with less lines of codes. Let’s have a glance at some of the important Python libraries that are used in the machine learning space.
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NumPy: Numpy is the core library for scientific computing in Python. It provides a high-performance multi-dimensional array object, and tools for working with these arrays.
Pandas: Pandas is a library used in Python for Data Manipulation and analysis.
SciPy: SciPy is a Python library used to solve scientific and mathematical problems. It is built over NumPy and therefore if we import scipy there is no need to import NumPy. Scipy contains fully-features versions of mathematical and scientific functions.
Matplotlib: Matplotlib is a Python package used for 2D grapics. It provide means to plot the data and generate graphical insights with various kinds of plots like histogram, boxplots, scatter plots etc.
Seaborn: Seaborn is a library which is built on top of Matplotlib. It is used to plot more sophisticated statistical visualizations.
StatsModels: The StatsModels library provides functionalities for approximation of various statistical models.
Scikit-learn: Scikit-Learn is a Python Library used to solve scientific and mathematical problems. It does that by providing a huge range of ML algorithms. It’s built on top of NumPy, Scipy and Matplotlib.
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There are various IDEs(Integrated Development environment which uses Python as engine and provides great functionalities on top of that.
The Jupyter Notebook - an open-source web-based application that enables ML fanatics to create, share and envisage, their projects.
There are newer IDEs and more advanced IDEs like PyCharm, Visual Studio Code and spyder.
How to master Machine Learning with Python?
Learn Python: Learn Python from basic to advanced. Practice those features that are important for data analysis, statistical analysis and Machine Learning. Start from declaring variables, conditional statements, control flow statements, functions, collection objects, modules and packages.
Statistics for Data Science - Learn the concept of random variable and its importance in the field of analytics. Learn to understand the behavior of data from the measures of central tendencies. Understand the importance of other statistical measures like confidence interval and distribution functions. The next step is to understand probability & various probability distributions and their crucial role in analytics. Understand the concept of various hypothesis tests like t-tests, z-test, ANOVA (Analysis of Variance), ANCOVA (Analysis of Covariance), chi-square test.
How does Major Machine Learning Algorithms work
Different algorithms have different tasks. It is advisable to understand the context and select the right algorithm for the right task.
- Regression (Prediction): Regression basically means to regress or make something follow a linear path. These algorithms are used for predicting numeric or continuous values. For example, predicting wheather, vehicle mileage, stock prices and so on.
- Linear Regression – predicting a response variable, which is numeric in nature, using one or more features or variables. Linear regression model is mathematically represented as:
Various regression algorithms include:
Linear Regression
Polynomial Regression
Exponential Regression
Decision Tree
Random Forest
Neural Network
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As a note to new aspirants, it is suggested to understand the concepts of assumptions of regressions, Ordinary Least Square Method(OLS), Dummy Variables, one hot encoding and performance evaluation metrics (RMSE, MSE, MAPE, MAE).
Classification – We use classification algorithms to answer simple questions like yeas or no, present or absent. Basically, predicting a set of classes or a categorical variable. For example, predicting loan default (yes/no) or predicting cancer (yes/no) and so on.
Classification algorithms include:
Binomial Logistic Regression
Fractional Binomial Regression
Quasibinomial Logistic regression
Decision Tree
Random Forest
Neural Networks
K-Nearest Neighbor
Support Vector Machines
Some of the classification algorithms are explained here:
K-Nearest Neighbors – Most used classification algorithm.
It is a non-parametric algorithm (does not make any assumption on the underlying data distribution)
It chooses to memorize the learning instances
The output is a class membership
There are three key elements in this approach – a set of labelled objects, eg, a set of stored records, a distance between objects, and the value of k, the number of nearest neighbours
Distance measures that the K-NN algorithm uses – Euclidean distance (square root of the sum of the squared distance between a new point and the existing point across all the input attributes. Other distances include – Hamming distance, Manhattan distance, Minkowski distance
Example of K-NN classification. The test sample (green dot) should be classified either to blue squares or to red triangles. If k = 3 it is assigned to the orange triangles because there are 2 triangles and only 1 square inside the inner circle. In other words the number of triangles is more than the number of squares If k = 5 (dashed line circle) it is assigned to the blue squares (3 squares vs. 2 triangles inside the outer circle). It is to be noted that to avoid equal voting, the value of k should be odd and not even.
Logistic Regression – It is a supervised algorithm that is used for binary classification. The metric used is a sigmoid function which takes any real value and maps it between zero and 1. In other words, Logistic Regression returns a probability value for the class label.
If the output of the sigmoid function is more than the specified threshold i.e 0.5 here , we can classify the outcome as 1 or YES, and if it is less than 0.5, we can classify it as 0 or NO
Decision Tree – Decision tree is mostly used in classification problems. It is a supervised learning algorithm. Decision Tree algorithms can also be used for regression problems. In other words, Decision Tree works for both categorical and continuous input and output variables. Each branch node of the tree represents a choice between some alternatives and each leaf node represents a decision beyond that threshold. As an early learner, it is suggested to understand the concept of ID3 algorithm, Gini Index, Entropy, Information Gain, Standard Deviation and Standard Deviation Reduction.
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Random Forest – This algorithm could be understood as a collection of multiple decision trees. Similar to Decision tree, it could also be used for both classification & regression problems. While algorithms like Decision Tree can cause a problem of over fitting as it uses the concept of bagging wherein a model performs well in training data but does not perform well in testing or unseen data, but Random forest takes a random subset of features which solves the problem of overfitting to a great extent. By taking the random subset of featuresthe decision trees obtained in each samples are uncorrelated. This is concept is known as bootstrapping which means sampling with replacement.
Support Vector Machine – a supervised learning algorithm, used for classification problems. It could also be used for regression problems which is known as Support Vector Regressor. In this, we plot each data item as a point in n-dimensional space n here represents the number of features The features are plotted and a hyperplane is formed between the data points when a satisfactory distance is measured to safely differentiate the two classes. Once the hyperplane is formed, future Data points could be segregated into these two classes depending upon their similarity with either side of the hyperplane.
It is fundamental to understand the concept of margin, support vectors, hyperplanes and tuning hyper-parameters (kernel, regularization, gamma, margin). Also get to know various types of kernels like linear kernel, radial basis function kernel and polynomial kernel
- Clustering
Clustering algorithms are unsupervised learning type of algorithms that are used for dividing data points into groups such that the data points in each group are similar to each other and very different from other groups.
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Some of the clustering algorithms include:
K-means – An unsupervised learning algorithm in which the items are grouped into k-cluster
The elements of the cluster are similar or homogenous.
Euclidean distance is used to calculate the distance between two data points.
The data point which is equidistant from all the other data points is calculated by checking the Euclidean distance and is referred as a centroid. This centroid refers to the cluster.
Conclusion
Python has an widespread set of modules and frameworks. It is fast, less complex and thus it saves development time and cost. It makes the program completely readable, particularly for novice users. This particular feature makes Python an ideal recipe for Machine Learning. Moreover, Python has a plethora of modules which could be used for Machine Learning and Deep Learning. These Modules are constantly worked upon and updated by the people who use Python as it is an open-source community-driven programming language.
Author:
Md Anas Ansari
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