Post archives
- python 17
- tutorial 11
- programming 6
- exercises 6
- machine learning 5
- numpy 4
- pandas 3
- linear regression 2
- artificial intelligence 1
- deep learning 1
- jupyter 1
- data analysis 1
- seaborn 1
- matplotlib 1
- classification 1
- project 1
- deployment 1
- sse 1
- mse 1
- r² 1
- p-value 1
- gradient descent 1
python
Credit Card Approval Prediction (End-To-End Machine Learning Project)
Welcome back, forks! After a long period of not posting here, I am happy to share that I am back again on MIB. In this post, we will work on an end-to-end machine learning project. I firmly believe this is one of the most detailed and comprehensive end-to-end ML project blog post on the internet. This project is perfect for the beginner in Machine Learning and seasoned ML engineers who could still learn one or two things from this post. This project was featured on Luke Barousse Youtube channel, click here to watch the video.
Demystify Machine Learning
Welcome back! I am very excited about this post as we are introducing machine learning and its commonly used jargon. You will have a broad overview of machine learning, how it works, and even write our first machine learning code at the end of the post. To understand advanced machine learning, we first need to have a good grasp of the fundamentals. That is why I think this is the most important post on this blog so far.
Pandas Exercises Part 3
Pandas Exercises Part 2
Welcome back, guys! We will continue with part 2 in this series of Pandas exercise. I am very excited about this post because we will introducing DataFrame, the most used Pandas data structure. I hope you guys will enjoy this post.
Pandas Exercises Part 1
Welcome back, folks! In this series of 3 blog post, we will be discussing pandas which one of my favorite python libraries. We will go through 74 exercises to solidify your skills with pandas and as usual, I will explain the WHY behind every single exercise.
NumPy Exercises Part 3
Welcome back, folks! This post is the last in the series of NumPy exercises. In this post, we will see intermediate and advanced level exercises. Remember, the more you practice, the more you will understand NumPy and will use it with ease in your ML projects.
NumPy Exercises Part 2
For this second post of NumPy exercises series, we will be doing intermediate level exercises in NumPy and will go through the solution together as we did in the first part. Try to solve the exercises on your own then compare your answer with mine. Let’s get started.
NumPy Exercises Part 1
In this post, we will be solving 20 exercises in NumPy to sharpen what you have learnt from the NumPy introduction post. If you have not read the NumPy post, I highly encourage to go first through that post on this link and then come back to try out the exercises.
NumPy Crash Course
One of the most used scientific computing library for python is without a doubt NumPy, Numpy, which is an abbreviation of Numerical Python, is very fast at computing arrays since it is mostly written in C programming. NumPy adds support for large, multi-dimensional arrays and matrices, along with an extensive collection of high-level mathematical functions (for linear algebra) to operate on these arrays.
A step-by-step introduction to exploratory data analysis
There is this concept called exploratory data analysis (EDA), which is essential in machine learning. In this beginner-friendly blog, we will solely discuss EDA. So what is exploratory data analysis or EDA? Well, it is the process of analyzing data to make sense of it and come up with a summary that describes the data using different methods like data manipulation and visualization.
Iterators and range in Python
In this post, we will mainly discuss iterator and range function. We have been using iterator and range function; however, this time, we’ll go in-depth. Let’s first talk about iterator and iterable object in Python.
List in Python
So far, we have seen one sequence data type, which is the string data type. Python has 6 additional built-in sequence data type. In this and the upcoming posts, we will be discussing 3 of them which are the lists, tuple and range and see their functions. In this post, we will focus on the list only
Control flow part 2 - loop statement
In this second part of the control flow series, we will discuss the for and while loop statement, which is one of the most important concepts in programming. This allows repetition of a particular block of code without rewriting the code multiple times; this is the reasons why computer are so efficient. They can repeat an operation so many times, very quickly by removing the need for a human to code the same repetitive task.
Control flow part 1 - conditional statement
In this series of posts, we are going to talk about the control flow in Python programming. A program’s control flow is the order in which the program’s code executes. In this first post of the series, we’ll talk about the conditional and pass statement. But before we start, let’s discuss on the indentation in python and the difference between a statement and an expression. Again, this post is written entirely in Jupyter notebook and exported as a markdown file to Jekyll.
Data types, variables and operators in Python
In this post, we are going to discover all you need to know about data types, operators, and variables in python. I’ll be using jupyter notebook, feel free to use any text editor or IDE of your choice but I do highly recommend to use jupyter especially if you are interested in data science. I also suggest to type the codes and run it on your system and see your results. This tutorial post is entirely written in Jupyter and exported as a markdown file to Jekyll.
Introduction to Jupyter notebook
Jupyter notebook formally called ipython, is a web application that runs code in the browser with addition of comments, visualizations, paragraph, equations, title, links, figures, and LaTex by using the markdown language which is very useful while writing code that requires explanation.
Python, The programming language of Machine Learning
When I say python folk will think that I am talking about the snake. No, ain’t talking about the snake, I am talking about Python the programming language. Python is an open-source high-level programming language(a programming language is a language computers can understand) created by Guido van Rossum and was first released to the public in 1991. Its main features are code readability by using whitespace instead of the curly braces, dynamic typing, and automatic memory management.
tutorial
Credit Card Approval Prediction (End-To-End Machine Learning Project)
Welcome back, forks! After a long period of not posting here, I am happy to share that I am back again on MIB. In this post, we will work on an end-to-end machine learning project. I firmly believe this is one of the most detailed and comprehensive end-to-end ML project blog post on the internet. This project is perfect for the beginner in Machine Learning and seasoned ML engineers who could still learn one or two things from this post. This project was featured on Luke Barousse Youtube channel, click here to watch the video.
Demystify Machine Learning
Welcome back! I am very excited about this post as we are introducing machine learning and its commonly used jargon. You will have a broad overview of machine learning, how it works, and even write our first machine learning code at the end of the post. To understand advanced machine learning, we first need to have a good grasp of the fundamentals. That is why I think this is the most important post on this blog so far.
NumPy Crash Course
One of the most used scientific computing library for python is without a doubt NumPy, Numpy, which is an abbreviation of Numerical Python, is very fast at computing arrays since it is mostly written in C programming. NumPy adds support for large, multi-dimensional arrays and matrices, along with an extensive collection of high-level mathematical functions (for linear algebra) to operate on these arrays.
A step-by-step introduction to exploratory data analysis
There is this concept called exploratory data analysis (EDA), which is essential in machine learning. In this beginner-friendly blog, we will solely discuss EDA. So what is exploratory data analysis or EDA? Well, it is the process of analyzing data to make sense of it and come up with a summary that describes the data using different methods like data manipulation and visualization.
Iterators and range in Python
In this post, we will mainly discuss iterator and range function. We have been using iterator and range function; however, this time, we’ll go in-depth. Let’s first talk about iterator and iterable object in Python.
List in Python
So far, we have seen one sequence data type, which is the string data type. Python has 6 additional built-in sequence data type. In this and the upcoming posts, we will be discussing 3 of them which are the lists, tuple and range and see their functions. In this post, we will focus on the list only
Control flow part 2 - loop statement
In this second part of the control flow series, we will discuss the for and while loop statement, which is one of the most important concepts in programming. This allows repetition of a particular block of code without rewriting the code multiple times; this is the reasons why computer are so efficient. They can repeat an operation so many times, very quickly by removing the need for a human to code the same repetitive task.
Control flow part 1 - conditional statement
In this series of posts, we are going to talk about the control flow in Python programming. A program’s control flow is the order in which the program’s code executes. In this first post of the series, we’ll talk about the conditional and pass statement. But before we start, let’s discuss on the indentation in python and the difference between a statement and an expression. Again, this post is written entirely in Jupyter notebook and exported as a markdown file to Jekyll.
Data types, variables and operators in Python
In this post, we are going to discover all you need to know about data types, operators, and variables in python. I’ll be using jupyter notebook, feel free to use any text editor or IDE of your choice but I do highly recommend to use jupyter especially if you are interested in data science. I also suggest to type the codes and run it on your system and see your results. This tutorial post is entirely written in Jupyter and exported as a markdown file to Jekyll.
Introduction to Jupyter notebook
Jupyter notebook formally called ipython, is a web application that runs code in the browser with addition of comments, visualizations, paragraph, equations, title, links, figures, and LaTex by using the markdown language which is very useful while writing code that requires explanation.
Python, The programming language of Machine Learning
When I say python folk will think that I am talking about the snake. No, ain’t talking about the snake, I am talking about Python the programming language. Python is an open-source high-level programming language(a programming language is a language computers can understand) created by Guido van Rossum and was first released to the public in 1991. Its main features are code readability by using whitespace instead of the curly braces, dynamic typing, and automatic memory management.
programming
Iterators and range in Python
In this post, we will mainly discuss iterator and range function. We have been using iterator and range function; however, this time, we’ll go in-depth. Let’s first talk about iterator and iterable object in Python.
List in Python
So far, we have seen one sequence data type, which is the string data type. Python has 6 additional built-in sequence data type. In this and the upcoming posts, we will be discussing 3 of them which are the lists, tuple and range and see their functions. In this post, we will focus on the list only
Control flow part 2 - loop statement
In this second part of the control flow series, we will discuss the for and while loop statement, which is one of the most important concepts in programming. This allows repetition of a particular block of code without rewriting the code multiple times; this is the reasons why computer are so efficient. They can repeat an operation so many times, very quickly by removing the need for a human to code the same repetitive task.
Control flow part 1 - conditional statement
In this series of posts, we are going to talk about the control flow in Python programming. A program’s control flow is the order in which the program’s code executes. In this first post of the series, we’ll talk about the conditional and pass statement. But before we start, let’s discuss on the indentation in python and the difference between a statement and an expression. Again, this post is written entirely in Jupyter notebook and exported as a markdown file to Jekyll.
Data types, variables and operators in Python
In this post, we are going to discover all you need to know about data types, operators, and variables in python. I’ll be using jupyter notebook, feel free to use any text editor or IDE of your choice but I do highly recommend to use jupyter especially if you are interested in data science. I also suggest to type the codes and run it on your system and see your results. This tutorial post is entirely written in Jupyter and exported as a markdown file to Jekyll.
Python, The programming language of Machine Learning
When I say python folk will think that I am talking about the snake. No, ain’t talking about the snake, I am talking about Python the programming language. Python is an open-source high-level programming language(a programming language is a language computers can understand) created by Guido van Rossum and was first released to the public in 1991. Its main features are code readability by using whitespace instead of the curly braces, dynamic typing, and automatic memory management.
exercises
Pandas Exercises Part 3
Pandas Exercises Part 2
Welcome back, guys! We will continue with part 2 in this series of Pandas exercise. I am very excited about this post because we will introducing DataFrame, the most used Pandas data structure. I hope you guys will enjoy this post.
Pandas Exercises Part 1
Welcome back, folks! In this series of 3 blog post, we will be discussing pandas which one of my favorite python libraries. We will go through 74 exercises to solidify your skills with pandas and as usual, I will explain the WHY behind every single exercise.
NumPy Exercises Part 3
Welcome back, folks! This post is the last in the series of NumPy exercises. In this post, we will see intermediate and advanced level exercises. Remember, the more you practice, the more you will understand NumPy and will use it with ease in your ML projects.
NumPy Exercises Part 2
For this second post of NumPy exercises series, we will be doing intermediate level exercises in NumPy and will go through the solution together as we did in the first part. Try to solve the exercises on your own then compare your answer with mine. Let’s get started.
NumPy Exercises Part 1
In this post, we will be solving 20 exercises in NumPy to sharpen what you have learnt from the NumPy introduction post. If you have not read the NumPy post, I highly encourage to go first through that post on this link and then come back to try out the exercises.
machine learning
Linear Regression, the essential theory
Hello Folks! Welcome back. In this post, I will discuss the theory behind linear regression models, one of the wildly used machine learning models to predict continuous variables (fancy terms to say that we are predicting a number, also referred to as numerical target or label). The model is quite simple to understand yet powerful. We use it when model interpretability (when we want to know which dependent variables, aka features, are the most predictive) is required, like in the consumer lending or medical fields where transparency is at its core. In the next post, we will dive deeper into the coding part of a Linear Regression.
Credit Card Approval Prediction (End-To-End Machine Learning Project)
Welcome back, forks! After a long period of not posting here, I am happy to share that I am back again on MIB. In this post, we will work on an end-to-end machine learning project. I firmly believe this is one of the most detailed and comprehensive end-to-end ML project blog post on the internet. This project is perfect for the beginner in Machine Learning and seasoned ML engineers who could still learn one or two things from this post. This project was featured on Luke Barousse Youtube channel, click here to watch the video.
Demystify Machine Learning
Welcome back! I am very excited about this post as we are introducing machine learning and its commonly used jargon. You will have a broad overview of machine learning, how it works, and even write our first machine learning code at the end of the post. To understand advanced machine learning, we first need to have a good grasp of the fundamentals. That is why I think this is the most important post on this blog so far.
The requirements to get started in Machine Intelligence
Machine learning is a cross-disciplinary field that includes computer science, mathematics, and sometimes domain knowledge. We can’t just focus on one part and expect to be great at it, and no you don’t have to be a master at everything to get started as it is said jack of all trades, master of none. You need to have a basic understanding of these topics
What is Machine Learning? does it matter?
Many may ask, what is machine learning (ML)? We have heard this buzzword very often in the media or by big-name tech companies and lately, it has been used interchangeably with the word “artificial intelligence” (AI).
numpy
NumPy Exercises Part 3
Welcome back, folks! This post is the last in the series of NumPy exercises. In this post, we will see intermediate and advanced level exercises. Remember, the more you practice, the more you will understand NumPy and will use it with ease in your ML projects.
NumPy Exercises Part 2
For this second post of NumPy exercises series, we will be doing intermediate level exercises in NumPy and will go through the solution together as we did in the first part. Try to solve the exercises on your own then compare your answer with mine. Let’s get started.
NumPy Exercises Part 1
In this post, we will be solving 20 exercises in NumPy to sharpen what you have learnt from the NumPy introduction post. If you have not read the NumPy post, I highly encourage to go first through that post on this link and then come back to try out the exercises.
NumPy Crash Course
One of the most used scientific computing library for python is without a doubt NumPy, Numpy, which is an abbreviation of Numerical Python, is very fast at computing arrays since it is mostly written in C programming. NumPy adds support for large, multi-dimensional arrays and matrices, along with an extensive collection of high-level mathematical functions (for linear algebra) to operate on these arrays.
pandas
Pandas Exercises Part 3
Pandas Exercises Part 2
Welcome back, guys! We will continue with part 2 in this series of Pandas exercise. I am very excited about this post because we will introducing DataFrame, the most used Pandas data structure. I hope you guys will enjoy this post.
Pandas Exercises Part 1
Welcome back, folks! In this series of 3 blog post, we will be discussing pandas which one of my favorite python libraries. We will go through 74 exercises to solidify your skills with pandas and as usual, I will explain the WHY behind every single exercise.
linear regression
Linear Regression, the essential theory
Hello Folks! Welcome back. In this post, I will discuss the theory behind linear regression models, one of the wildly used machine learning models to predict continuous variables (fancy terms to say that we are predicting a number, also referred to as numerical target or label). The model is quite simple to understand yet powerful. We use it when model interpretability (when we want to know which dependent variables, aka features, are the most predictive) is required, like in the consumer lending or medical fields where transparency is at its core. In the next post, we will dive deeper into the coding part of a Linear Regression.
Demystify Machine Learning
Welcome back! I am very excited about this post as we are introducing machine learning and its commonly used jargon. You will have a broad overview of machine learning, how it works, and even write our first machine learning code at the end of the post. To understand advanced machine learning, we first need to have a good grasp of the fundamentals. That is why I think this is the most important post on this blog so far.
artificial intelligence
What is Machine Learning? does it matter?
Many may ask, what is machine learning (ML)? We have heard this buzzword very often in the media or by big-name tech companies and lately, it has been used interchangeably with the word “artificial intelligence” (AI).
deep learning
Why deep learning now?
As a refresh from my previous blog, deep learning is a subset of machine learning which learns by using layers of neurons like the ones found in our brain to output an expected result. The layers are organized in a way that can break down the input into different layers of abstraction. The more the layers we have, the deeper the neural network.
jupyter
Introduction to Jupyter notebook
Jupyter notebook formally called ipython, is a web application that runs code in the browser with addition of comments, visualizations, paragraph, equations, title, links, figures, and LaTex by using the markdown language which is very useful while writing code that requires explanation.
data analysis
A step-by-step introduction to exploratory data analysis
There is this concept called exploratory data analysis (EDA), which is essential in machine learning. In this beginner-friendly blog, we will solely discuss EDA. So what is exploratory data analysis or EDA? Well, it is the process of analyzing data to make sense of it and come up with a summary that describes the data using different methods like data manipulation and visualization.
seaborn
A step-by-step introduction to exploratory data analysis
There is this concept called exploratory data analysis (EDA), which is essential in machine learning. In this beginner-friendly blog, we will solely discuss EDA. So what is exploratory data analysis or EDA? Well, it is the process of analyzing data to make sense of it and come up with a summary that describes the data using different methods like data manipulation and visualization.
matplotlib
A step-by-step introduction to exploratory data analysis
There is this concept called exploratory data analysis (EDA), which is essential in machine learning. In this beginner-friendly blog, we will solely discuss EDA. So what is exploratory data analysis or EDA? Well, it is the process of analyzing data to make sense of it and come up with a summary that describes the data using different methods like data manipulation and visualization.
classification
Credit Card Approval Prediction (End-To-End Machine Learning Project)
Welcome back, forks! After a long period of not posting here, I am happy to share that I am back again on MIB. In this post, we will work on an end-to-end machine learning project. I firmly believe this is one of the most detailed and comprehensive end-to-end ML project blog post on the internet. This project is perfect for the beginner in Machine Learning and seasoned ML engineers who could still learn one or two things from this post. This project was featured on Luke Barousse Youtube channel, click here to watch the video.
project
Credit Card Approval Prediction (End-To-End Machine Learning Project)
Welcome back, forks! After a long period of not posting here, I am happy to share that I am back again on MIB. In this post, we will work on an end-to-end machine learning project. I firmly believe this is one of the most detailed and comprehensive end-to-end ML project blog post on the internet. This project is perfect for the beginner in Machine Learning and seasoned ML engineers who could still learn one or two things from this post. This project was featured on Luke Barousse Youtube channel, click here to watch the video.
deployment
Credit Card Approval Prediction (End-To-End Machine Learning Project)
Welcome back, forks! After a long period of not posting here, I am happy to share that I am back again on MIB. In this post, we will work on an end-to-end machine learning project. I firmly believe this is one of the most detailed and comprehensive end-to-end ML project blog post on the internet. This project is perfect for the beginner in Machine Learning and seasoned ML engineers who could still learn one or two things from this post. This project was featured on Luke Barousse Youtube channel, click here to watch the video.
sse
Linear Regression, the essential theory
Hello Folks! Welcome back. In this post, I will discuss the theory behind linear regression models, one of the wildly used machine learning models to predict continuous variables (fancy terms to say that we are predicting a number, also referred to as numerical target or label). The model is quite simple to understand yet powerful. We use it when model interpretability (when we want to know which dependent variables, aka features, are the most predictive) is required, like in the consumer lending or medical fields where transparency is at its core. In the next post, we will dive deeper into the coding part of a Linear Regression.
mse
Linear Regression, the essential theory
Hello Folks! Welcome back. In this post, I will discuss the theory behind linear regression models, one of the wildly used machine learning models to predict continuous variables (fancy terms to say that we are predicting a number, also referred to as numerical target or label). The model is quite simple to understand yet powerful. We use it when model interpretability (when we want to know which dependent variables, aka features, are the most predictive) is required, like in the consumer lending or medical fields where transparency is at its core. In the next post, we will dive deeper into the coding part of a Linear Regression.
r²
Linear Regression, the essential theory
Hello Folks! Welcome back. In this post, I will discuss the theory behind linear regression models, one of the wildly used machine learning models to predict continuous variables (fancy terms to say that we are predicting a number, also referred to as numerical target or label). The model is quite simple to understand yet powerful. We use it when model interpretability (when we want to know which dependent variables, aka features, are the most predictive) is required, like in the consumer lending or medical fields where transparency is at its core. In the next post, we will dive deeper into the coding part of a Linear Regression.
p-value
Linear Regression, the essential theory
Hello Folks! Welcome back. In this post, I will discuss the theory behind linear regression models, one of the wildly used machine learning models to predict continuous variables (fancy terms to say that we are predicting a number, also referred to as numerical target or label). The model is quite simple to understand yet powerful. We use it when model interpretability (when we want to know which dependent variables, aka features, are the most predictive) is required, like in the consumer lending or medical fields where transparency is at its core. In the next post, we will dive deeper into the coding part of a Linear Regression.
gradient descent
Linear Regression, the essential theory
Hello Folks! Welcome back. In this post, I will discuss the theory behind linear regression models, one of the wildly used machine learning models to predict continuous variables (fancy terms to say that we are predicting a number, also referred to as numerical target or label). The model is quite simple to understand yet powerful. We use it when model interpretability (when we want to know which dependent variables, aka features, are the most predictive) is required, like in the consumer lending or medical fields where transparency is at its core. In the next post, we will dive deeper into the coding part of a Linear Regression.