Let’s understand how to define and formulate the machine learning problem (for predictive modeling) from a business problem. This structured approach should help you apply the process to most other types of predictive modeling problems at work. Introduction Often in ML teams, you will hear from the business/company departments about the problems and issues they …
Let’s build your first machine learning project with Python from scratch. “But I am a complete beginner, I am not ready yet!..” – Your mind voice. If you have been looking to get started in ML, but can’t really figure out how and where to start, then this one is for you. Just read on.. …
numpy.random.randint function is used to get random integers from low to high values. The low value is included while the high value is excluded in the calculations. The output values are taken from the discrete uniform distribution of the range values. random.randint(low, high=None, size=None, dtype=int) Purpose: The numpy random randint function used for creating a …
The np.random.uniform() function is used to create an array with random samples from a uniform probability distribution of given low and high values. random.uniform(low=0.0, high=1.0, size=None) Purpose: The numpy random uniform function used for creating a numpy array with random float values from low to high interval. Parameteres: Low: float or array-like of floats,optional: Lowest …
The np.sort() function is used to sort the array along a specified axis. Numpy.sort (a, axis=- 1, kind=None, order=None) Purpose: This function is used for sorting the array. Parameters: arr:a:array_like array to be sorted. axis: None or int,optional Axis on which we perform the arithmetic mean if specified. otherwise, the arr will be flattened. kind: …
numpy.median function is used to calculate the median of an array along a specific axis or multiple axes. Median is defined as the middle value separating the higher half from the lower half of a data sample in other words median is a value in the middle when you sort the values. In this post, …
numpy.median() – How to compute median in Python Read More »
The numpy.where() function is used to filter data based on the conditions provided. These conditions can vary from being as simple as value comparisons to nested bit-wise conditions. You can also use this function to perform conditional replacements in the input data array. In this article, you will learn about the possible use cases of …
Python is the most popular programming language used for AI and machine learning. Let’s see how to setup python environment for ML using anaconda. How to install Python? Simply visit Python.org, go to downloads section, download latest version that shows there and install it like you do for any other software. To do machine learning …
Managing packages apt update apt list apt install nano apt remove nano Navigating the file system pwd # to print the working directory ls # to list the files and directories ls -l # to print a long list cd / # to go to the root directory cd bin # to go to the …
The train-test split technique is a way of evaluating the performance of machine learning models. Whenever you build machine learning models, you will be training the model on a specific dataset (X and y). Once trained, you want to ensure the trained model is capable of performing well on the unseen test data as well. …
“How is machinelearningplus university different from other courses?”. Other courses include those you see on Coursera, Udemy, Udacity, Youtube and courses offered by universities. I think this is important to know, so let me try and answer this question. First of all, let me clarify that some of these courses are taught by great accomplished …
A Data scientist uses Data and AI to solve business problems, is skilled at working with data, extract meaningful insights, using ML to solve business problems, build applications that make predictions and recommendations, deploy and monitor the solutions. The perks of being a Data Scientist Data scientist is a relatively a new profession. By Data …
What is a Data Scientist? – Roles, Responsibilities, Skillsets, Career Path and Salary Read More »
Data Scientists turn data to information and information to useful insight. In this one, I look back and reflect on how the profession of Data Scientists came into being. All of this is based on happenings in companies that were early adopters of AI, esp analytics firms and product companies. Ok, let me get straight …
The story of how Data Scientists came into existence Read More »
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 …
