A cheat sheet of tasks and things to take care of for every end-to-end ML projects. In this, I write down a check list of items and tasks to check whenever you start with a new Data Science / ML project. Once you start off with the project there will be so many things going […]
Task Checklist for Almost Any Machine Learning Project Read More »
Today, I discuss the Data Science Roadmap, the missing guide to self study machine learning. I’ll discuss what exactly you need to know and do in order to self study Data science / ML / AI / Stats. I will provide you with some of the best resources for each topic, why you need to
Data Science Roadmap – How to become a Data Scientist? (6 month self study plan) Read More »
Why learn the math behind machine learning algorithms when you can readily implement it using the python libraries like scikit-learn, h2o, statsmodels etc? This is a fair question especially coming from beginners when it is easy to implement ML with few lines of code and get the results fast. Now, you must understand that learning
Why learn the math behind Machine Learning and AI? Read More »
Today, I want to discuss some of the common mistakes that programmers make when starting to learn machine learning. But first, let me speak about why software engineers should start looking into ML. First, let’s see why programmers should start ML? Today, from what I’ve seen, people coming with a strong software engineering background, good
Mistakes programmers make when starting machine learning Read More »
The use cases of machine learning to real world problems keeps growing as ML/AI sees increased adoption across industries. However, there are certain core use cases that add lot of value for organizations and you’ll often find them being implemented in banks, healthcare, manufacturing, product companies or by consulting organizations as well. Let’s tour of
The size of the data you can load and train in your local computer is typically limited by the memory in your computer. Let’s explore various options of how to deal with big data in python for your ML projects. In certain situations, you might have a hard time working on large data, that is
How to deal with Big Data in Python for ML Projects (100+ GB)? Read More »
Granger Causality test is a statistical test that is used to determine if a given time series and it’s lags is helpful in explaining the value of another series. You can implement this in Python using the statsmodels package. That is, the Granger Causality can be used to check if a given series is a
Granger Causality Test in Python Read More »
Introduced in 1969 by Clive Granger, Granger causality test is a statistical test that is used to determine if a particular time series is helpful in forecasting another series. What is Granger Causality? Let’s first understand when is the Granger causality test is useful. Let’s say you have two time series data X and Y,
Granger Causality Test Read More »
Not all Data Science projects that get kicked-off see the light of day. While this is true for any project, there are common reasons specific to Data Science projects, which can be avoided. These can either be prevented if you are aware and not commit these mistakes. Or if you see these problems, consider avoiding
Main Pitfalls in Machine Learning Projects Read More »
Population stability Index (PSI) is a model monitoring metric that is used to quantify how much the distribution of a continuous response variable has changed between two given samples, typically collected at different points in time. Originally, PSI was used to check if the distribution of the Y (aka the response variable or the scoring
Population Stability Index (PSI) Read More »
Let’s understand the formula for the linear regression coefficients. That is the formula for both alpha and the beta. Now, if you have simple linear regression that does, you have just 1x variable in your data, you will be able to compute the values of alpha and beta using this formula. Let’s suppose you
Regression Coefficient Formula Read More »
In this lesson, I introduce what Linear regression is all about. Linear Regression is a foundational algorithm for machine learning and statistical modeling. Traditionally, Linear Regression is the very first algorithm you’d learn when getting started with predictive modeling. While there are a lot more ML and Deep learning algorithm in use today,
Linear Regression in Machine Learning – Clearly Explained Read More »
Understanding linear regression. Let’s understand what linear regression is all about from a non-technical perspective, before we get into the details, we will first understand from a layman’s terms what linear regression is. Now, linear regression is a machine learning algorithm ml algorithm that uses data to predict a quantity of interest, typically, we call
Linear Regression in Machine Learning – Clearly Explained Read More »
In this guide, you will understand the steps to deploy your ML model app in Amazon EC2 service. Here the ML app is a flask based. However, the same steps and ideas can be followed to host apps built with other similar applications. Note that we will not be covering how to build an ML
Deploy ML model in AWS Ec2 – Complete no-step-missed guide Read More »
Decorators in python allow you to dynamically change the functionality of another function, without altering it’s code. What? Is that possible? Yes. This covers: 1. What is a decorator and how to create one? 2. Easier way to decorate functions 3. Class decorators 4. Problem with docstrings on decorated functions and how to solve. What
Decorators in Python – How to enhance functions without changing the code? Read More »
Generators in python provide an efficient way of generating numbers or objects as and when needed, without having to store all the values in memory beforehand. Introduction You can think of Generators as a simple way of creating iterators without having to create a class with __iter__() and __next__() methods. So how to create a
Generators in Python – How to lazily return values only when needed and save memory? Read More »
Iterable in Python is any object that can be looped over. In order for an iterable to be looped over, an iterable needs to be converted to an iterator using the __iter__() method. Once created, it is actually the iterator that gets iterated over. This is also what happens internally when you run a for-loop.
Iterators in Python – What are Iterators and Iterables? Read More »
Any Python file with a .py extension is a Module in Python. A python package is a collection of such modules along with a __init__.py file. Let’s understand how to work with modules and packages with clear examples. Introduction When you work on python projects, it’s not a good practice to have all you python
Python Module – What are modules and packages in python? Read More »
Object oriented programming is an effective way of writing code. You create classes which are python objects, that represented meaningful entities which defines its own behaviour (via methods) and attributes. Let’s understand what a class is and the concepts behind Object Oriented Programming in Python Everything you have encountered so far in Python, such as
Object Oriented Programming (OOPS) in Python Read More »
