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These days, it’s easy to fit pretty much any model you can think of with one library or another, but how much do you really learn by calling .fit() and .predict()? While it’s certainly much more practical to use a framework like python’s statsmodels or scikit-learn for the normal use-case, it seems equally logical that when learning data science it makes a lot of sense to get a feel for how these models actually work. Below we show how to use numpy to implement a basic linear regression model from the ground up. Let’s get started!
#data-science #numpy #python #machine-learning #developer
1619660285
A geek in Machine Learning with a Master’s degree in…
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NumPy is an essential Python library to perform mathematical and scientific computations. NumPy offers Python’s array-like data structures with exclusive operations and methods. Many data science libraries and frameworks, including Pandas, Scikit-Learn, Statsmodels, Matplotlib and SciPy, are built on top of NumPy with Numpy arrays in their building blocks. Some frameworks, including TensorFlow and PyTorch, introduce NumPy arrays or NumPy-alike arrays as their fundamental data structure in the name of tensors.
How NumPy becomes the base of Data Science computing system (source)
Data Science relies heavily on Linear Algebra. NumPy is famous for its Linear Algebra operations. This article discusses methods available in the NumPy library to perform various Linear Algebra operations with examples. These examples assume that the readers have a basic understanding of NumPy arrays. Check out the following articles to have a better understanding of NumPy fundamentals:
#developers corner #linear algebra #matrices #numpy #numpy array #numpy dot product #numpy matrix multiplication #numpy tutorial #svd #vectors
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Static code analysis refers to the technique of approximating the runtime behavior of a program. In other words, it is the process of predicting the output of a program without actually executing it.
Lately, however, the term “Static Code Analysis” is more commonly used to refer to one of the applications of this technique rather than the technique itself — program comprehension — understanding the program and detecting issues in it (anything from syntax errors to type mismatches, performance hogs likely bugs, security loopholes, etc.). This is the usage we’d be referring to throughout this post.
“The refinement of techniques for the prompt discovery of error serves as well as any other as a hallmark of what we mean by science.”
We cover a lot of ground in this post. The aim is to build an understanding of static code analysis and to equip you with the basic theory, and the right tools so that you can write analyzers on your own.
We start our journey with laying down the essential parts of the pipeline which a compiler follows to understand what a piece of code does. We learn where to tap points in this pipeline to plug in our analyzers and extract meaningful information. In the latter half, we get our feet wet, and write four such static analyzers, completely from scratch, in Python.
Note that although the ideas here are discussed in light of Python, static code analyzers across all programming languages are carved out along similar lines. We chose Python because of the availability of an easy to use ast
module, and wide adoption of the language itself.
Before a computer can finally “understand” and execute a piece of code, it goes through a series of complicated transformations:
As you can see in the diagram (go ahead, zoom it!), the static analyzers feed on the output of these stages. To be able to better understand the static analysis techniques, let’s look at each of these steps in some more detail:
The first thing that a compiler does when trying to understand a piece of code is to break it down into smaller chunks, also known as tokens. Tokens are akin to what words are in a language.
A token might consist of either a single character, like (
, or literals (like integers, strings, e.g., 7
, Bob
, etc.), or reserved keywords of that language (e.g, def
in Python). Characters which do not contribute towards the semantics of a program, like trailing whitespace, comments, etc. are often discarded by the scanner.
Python provides the tokenize
module in its standard library to let you play around with tokens:
Python
1
import io
2
import tokenize
3
4
code = b"color = input('Enter your favourite color: ')"
5
6
for token in tokenize.tokenize(io.BytesIO(code).readline):
7
print(token)
Python
1
TokenInfo(type=62 (ENCODING), string='utf-8')
2
TokenInfo(type=1 (NAME), string='color')
3
TokenInfo(type=54 (OP), string='=')
4
TokenInfo(type=1 (NAME), string='input')
5
TokenInfo(type=54 (OP), string='(')
6
TokenInfo(type=3 (STRING), string="'Enter your favourite color: '")
7
TokenInfo(type=54 (OP), string=')')
8
TokenInfo(type=4 (NEWLINE), string='')
9
TokenInfo(type=0 (ENDMARKER), string='')
(Note that for the sake of readability, I’ve omitted a few columns from the result above — metadata like starting index, ending index, a copy of the line on which a token occurs, etc.)
#code quality #code review #static analysis #static code analysis #code analysis #static analysis tools #code review tips #static code analyzer #static code analysis tool #static analyzer
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Let’s begin our journey with the truth — machines never learn. What a typical machine learning algorithm does is find a mathematical equation that, when applied to a given set of training data, produces a prediction that is very close to the actual output.
Why is this not learning? Because if you change the training data or environment even slightly, the algorithm will go haywire! Not how learning works in humans. If you learned to play a video game by looking straight at the screen, you would still be a good player if the screen is slightly tilted by someone, which would not be the case in ML algorithms.
However, most of the algorithms are so complex and intimidating that it gives our mere human intelligence the feel of actual learning, effectively hiding the underlying math within. There goes a dictum that if you can implement the algorithm, you know the algorithm. This saying is lost in the dense jungle of libraries and inbuilt modules which programming languages provide, reducing us to regular programmers calling an API and strengthening further this notion of a black box. Our quest will be to unravel the mysteries of this so-called ‘black box’ which magically produces accurate predictions, detects objects, diagnoses diseases and claims to surpass human intelligence one day.
We will start with one of the not-so-complex and easy to visualize algorithm in the ML paradigm — Linear Regression. The article is divided into the following sections:
Need for Linear Regression
Visualizing Linear Regression
Deriving the formula for weight matrix W
Using the formula and performing linear regression on a real world data set
Note: Knowledge on Linear Algebra, a little bit of Calculus and Matrices are a prerequisite to understanding this article
Also, a basic understanding of python, NumPy, and Matplotlib are a must.
Regression means predicting a real valued number from a given set of input variables. Eg. Predicting temperature based on month of the year, humidity, altitude above sea level, etc. Linear Regression would therefore mean predicting a real valued number that follows a linear trend. Linear regression is the first line of attack to discover correlations in our data.
Now, the first thing that comes to our mind when we hear the word linear is, a line.
Yes! In linear regression, we try to fit a line that best generalizes all the data points in the data set. By generalizing, we mean we try to fit a line that passes very close to all the data points.
But how do we ensure that this happens? To understand this, let’s visualize a 1-D Linear Regression. This is also called as Simple Linear Regression
#calculus #machine-learning #linear-regression-math #linear-regression #linear-regression-python #python
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Take your current understanding and skills on machine learning algorithms to the next level with this article. What is regression analysis in simple words? How is it applied in practice for real-world problems? And what is the possible snippet of codes in Python you can use for implementation regression algorithms for various objectives? Let’s forget about boring learning stuff and talk about science and the way it works.
#linear-regression-python #linear-regression #multivariate-regression #regression #python-programming
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Machine learning algorithms are not your regular algorithms that we may be used to because they are often described by a combination of some complex statistics and mathematics. Since it is very important to understand the background of any algorithm you want to implement, this could pose a challenge to people with a non-mathematical background as the maths can sap your motivation by slowing you down.
In this article, we would be discussing linear and logistic regression and some regression techniques assuming we all have heard or even learnt about the Linear model in Mathematics class at high school. Hopefully, at the end of the article, the concept would be clearer.
**Regression Analysis **is a statistical process for estimating the relationships between the dependent variables (say Y) and one or more independent variables or predictors (X). It explains the changes in the dependent variables with respect to changes in select predictors. Some major uses for regression analysis are in determining the strength of predictors, forecasting an effect, and trend forecasting. It finds the significant relationship between variables and the impact of predictors on dependent variables. In regression, we fit a curve/line (regression/best fit line) to the data points, such that the differences between the distances of data points from the curve/line are minimized.
#regression #machine-learning #beginner #logistic-regression #linear-regression #deep learning