How to Build an MCP Server in Python (Beginner Tutorial)
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Category: Machine Learning
What is the Difference Between Machine Learning and Deep Learning?
Hell guys, as you know, I’m Kindson the Genius and good to see you again! In this short lesson, I would explain to you the …
20 Cool Machine Learning and Data Science Concepts (Simple Definitions)
Hello everyone, as you know, I’m Kindson The Genius. I would like to share with you these 20 cool Machine Learning and Data Science Concept …
ML.Net Tutorial 2: Building a Machine Learning Model for Classification
You could also learn Difference Between Machine Learning and Deep Learning. This tutorial follows from Tutorial 1 where you downloaded your dataset, setup the Visual studio solution …
Machine Learning Questions and Answers (Questions 31 to 40)
Welcome back! So we’ll continue with Questions 31 to 40 of our Machine Learning Q&A. You can find Question 1 to 20 below Questions 1 …
Machine Learning Questions and Answers (Questions 21 to 30)
Welcome back! So we’ll continue with Questions 21 to 30 of our Machine Learning Q&A. You can find Question 1 to 20 below Questions 1 …
Machine Learning Questions and Answers (Questions 11 to 20)
This is Machine Learning Questions and Answer (11 to 20) Find Question 1 to 10 here. So let’s get started! 11. Explain Clustering in …
Machine Learning Questions and Answers – (Question 1 to 10)
I’m happy to the making this lesson. I would give you brief answers to several Machine Learning questions. But if you would like to go …
What is Likelihood Function in Data Science and Machine Learning?
I will try to explain Likelihood Function in very clear and simple terms. Likelihood Function in Machine Learning and Data Science is the joint probability …
Machine Learning 101 – Multiple Linear Regression
You already know of Simple Linear Regression. You also know of Logistic Regression. Now we would discuss Multiple Linear Regression. This is a case where …
Machine Learning 101 – Minimizing Misclassification Rate in Bayes’ Classifier
In Lecture 4, we learnt about the Bayes’ classifier. Here we would see how to minimize misclassfication rate in Bayes classifier. Again, we would review …
Machine Learning 101 – Basics of Logistic Regression
First I would like clarify that the Logistic Regression model is a model for classification. Also note that Machine Learning 101 focuses on Supervised Learning. …
Machine Learning 101 – K-Nearest Neighbors Classifier
In the last lecture, we discussed Bayes’ Classifier. Now, we are going to discuss K-Nearest Neighbors Classifier. Remember that Bayes Classifier tries to classify X …
Machine Learning 101 – The Bayes’ Classfier
This is the second lecture on classification. It follow the first one: Introduction to Classification. Bayes’ Classifier is a classifier that works based on Bayes’ …
Machine Learning 101 – Introduction to Classification
In subsequent lectures, we have discussed regression problems. Now we would apply the same analysis to classification but with little adjustment. In case of classification, …
Machine Learning 101 – Bias-Variance Trade-off
This Lecture follows from Lecture 7 on Underfitting and Overfitting. Here we would discuss Bias-Variance Trade-off. I will try to make this lesson very clear. …
Machine Learning 101 – Application of Bayes’ Theorem
In the previous lesson (Lesson 9), we derived Bayes theorem. So let’s write it out: Also recall that Bayes’ theorem helps us find conditional probabilities …
Machine Learning 101 – What is Probability Density?
By now, you probably understand probability as well as probability theory. You also know about the Sum Rule and Product Rule. Then you also understand …
Machine Learning 101 – Rules of Probability & Bayes’ Theorem
We will now consider some of the important rules of probability. Meanwhile we would also understand the meaning of terms along the line. They include: …
Machine Learning 101 – Overfitting and Underfitting
Remember that in the previous lecture (Lecture 6), we discuss polynomial curve fitting. We kind of saw that the relationship in a dataset can be …
Machine Learning 101 – Introduction to Probability Theory
As you already know, one of the four basic theories of Machine Learning is the Probability Theory. Or simply, Probability. And this is one challenge …
Machine Learning 101 – Equation for a Line and Regression Line
Let’s go back to the regression problem we solved in Lecture 4. We are given a dataset. You need to find the relationship between the …
Machine Learning 101 – Polynomial Curve Fitting
This is Lecture 6 of Machine Learning 101. We would discuss Polynomial Curve Fitting. Now don’t bother if the name makes it appear tough. This …
Machine Learning 101 – Classes of Machine Learning Problems
This is lesson 3 of Machine Learning 101. We are going to examine classes of machine learning problems. In Lecture 2 we already mentioned a …
Machine Learning 101 – Simple Regression Problem
This is Lecture 4 of the Machine Learning 101. It follows from Lecture 3. In this lecture, we would solve some regression problems. So brace …
Machine Learning 101 – Overview of Machine Learning and Some Basic Terms
The is lesson 2 of our Machine Learning 101 course. This follows from Lesson 1. We would cover the following: The Goal of Machine Learning …
Machine Learning 101 – Introduction to Machine Learning Course
This is the very first of a complete Machine Learning course . So if you intend to learn Machine Learning, then you are in the right place. …
Basics of Genetic Algorithm – GA (Explained in Simple Terms)
We would examine the basics of Genetic Algorithm and dive a little deeper into the actual steps in genetics algorithm. I will try to be …
PCA Tutorial 2 – How to Perform Principal Components Analysis (PCA)
Now you know some theories about Principal Components Analysis (PCA) and now we are going to go through how to actually perform it. Next we …
20 Cool Machine Learning and Data Science Concepts (Simple Definitions)
Hello everyone, as you know, I’m Kindson The Genius. I would like to share with you these 20 cool Machine Learning and Data Science Concept …
PCA Tutorial 1 – Introduction to PCA and Dimensionality Reduction
PCA is one of the concepts that many find a bit tough to grasp. I had the same issue, but now I figure out a …
Simple Linear Regression in Machine Learning (A Simple Tutorial)
In this tutorial we would cover Simple Linear Regression in a very easy-to-understand way. We are assuming you don’t have much knowledge of Machine Learning …
Support Vector Machines in Machine Learning ( A Simple Tutorial)
Just as you know, I would try to explain Support Vector Machines (SVM) in a vary simple and clear way. I know many find it …
10 Machine Learning Project (Thesis) Topics for 2020
Are you looking for some interesting project ideas for your thesis, project or dissertation? Then be sure that a machine learning topic would be a …
What is Principal Component Analysis (PCA) – A Simple Tutorial
by In this simple tutorial, I would explain the concept of Principal Components Analysis (PCA) in Machine Learning. I would try to be as simple and …
Difference Between Prediction and Inference in Machine Learning
Hello friend, I’ll like to share with you this brief explanation of the difference between Prediction and Inference. They appear similar, to us researchers and …
Top 10 Free Machine Learning Papers Every Student Researcher Must Read
I have made a list of this 10 research paper I believe very student and researcher in area of Artificial Intelligence and Machine learning must …
How I Lost My Programming and Machine Learning Research Studio in Nigeria
I still cry when I remember what I lost in the in Nigeria couple of months before I relocated to Budapest. It’s a very long …
What is the Difference Between Machine Learning and Deep Learning?
Hell guys, as you know, I’m Kindson the Genius and good to see you again! In this short lesson, I would explain to you the …
5 Most Popular AI Programming Languages to Learn by 2019
So much changes is taking place in the field of programming. Easy to Learn and Free 1. Python 2. R Programming 3. MatLab Script 4. …
ML.Net Tutorial 1: Introduction to Machine Learning in .Net (Beginner to Expert)
My name is Kindson The Genius and today, I would introduce you to Machine Learning using .Net. Yes, .net C# programmer can now develop Machine …
5 Schools to Earn Masters Degree in Machine Learning (Part-time and Online Learning) 2018/2019
Hello, my name is Kindson. One fact we must all appreciated is that in the next few years, Machine Learning or related courses would gradually …
Brief Explanation of Top 5 Technology Trends You Should Know in 2019
Hello good to see you! My name is Kindson The Genius and I would give you a brief explanation of these top 10 technology trends …
What is a Linear Separator? What is a Hyperplane? (Simple Explanation)
Today, I would give a very simple explanation of the concept of linear separator and hyperplane. This is would be a very basic and simple, …
Classification in Machine Learning
In this lesson, we are going to examine classification in machine learning. Below are the topics we are going to cover in this lesson Formulation …
Understanding Clustering (k-Means) in Machine Learning (Unsupervised Learning)
In this lesson we are going discuss clustering under the following topic: Introduction to Clustering Formalized Definition of k-Means Clustering 1-of-K Coding Scheme The Expectation …
How to Reduce Expected Loss in Classification (Machine Learning)
This lessons explains in simple terms how to minimize expected loss during classification.Remember that when an input variable is classified wrongly, a loss is incurred. …
How to Minimize Misclassifcation Rate in Classification (Machine Learning)
Remember that classification is a supervised learning concept that has to do with determining the the class a new input variable belongs.In trying to assign …
3 Approaches to Classification in Machine Learning
In this lesson, we would examine 3 approaches to classification. The first 2 would be based on the a priori knowledge of the probabilities. The …
Best 23 Easy Tutorials on Artificial Intelligence, Pattern Recognition and Machine Learning
I have made a list of the best 20 easy lessons of various topic of Machine Learning, Pattern Recognition and Artificial Intelligence. Watch the Machine …
What is the Difference Between Classification and Clustering in Machine Learning
Today we will discuss the difference between two important topic that appear similar in machine learning. Classification and Clustering I have decided to create this …
Maximum Likelihood Estimation (MLE) in Machine Learning
What is Maximum Likelihood(ML)? and What is Maximum Likelihood Estimation (MLE)?These is a very important concept in Machine Learning and that is what we are …
Overview of Outlier Detection Techniques in Statistics and Machine Learning
Today we will discuss the concept of Outlier Detection in Statistics and Machine Learning and we would focus on the techniques used. We would cover …
Introduction of Recommender Systems
We would try to clearly explain the concept of a recommender system. What is a Recommender System A recommender system is a system that is …
Bias/Variance Trade-off in Classification(Machine Learning)
In this lesson, you will learn about Bias/Variance Trade-off in Machine Learning. This is a concept in machine learning which refers to the problem of …
Introduction to Support Vector Machine (SVM)
Today we would give a clear and simple explanation of Support Vector Machines. We would discuss the basics of support vector machines in very clear …
What is Over-fitting in Linear Regression and Machine Learning
In this short lesson, we will discuss the concept of over-fitting in Linear Regression. For now I would assume you have a basic knowledge of …
What is an Activation Function in Neural Networks
Activation Functions play a very important role in Neural Network so understanding them is key to getting a clearer understanding on how neural networks work. …
Basics of Backpropagation in Neural Networks(Machine Learning)
Backpropagation is the learning algorithm used in neural networks and is a generalization of the least mean squares algorithm used in linear perceptron. Backpropagation requires …
Introduction to Gradient Decent in Neural Networks – Learn Machine Learning
In this lesson, we would examine the learning process in Neural Networks. Remember that a neural network is a classifier that could learn from a …
What is Perceptron? How the Perceptron Works
Today we will understand the concept of Perceptron. Basics of The PerceptronThe perceptron(or single-layer perceptron) is the simplest model of a neuron that illustrates how …
What is K-Means in Clustering in Machine Learning?
We are going to explain the basic concept of k-means clustering and the k-means clustering algorithm. Table of Content What is K-Means Clustering How it …
Introduction to Machine Learning (ML)
Updated August 16, 2026: Clarified the main types of machine learning (including reinforcement learning), fixed heading structure for SEO, added a table of contents and …
Dimensionality Reduction and Principal Component Analysis (PCA)
More Detailed Video Explanation Here Video on How to Perform PCA in R here We would explain the concept of dimensionality reduction in a very …
Basics of Decision Theory – How Medical Diagnosis Apps Work
Watch the video here. In this lesson we would examine the following topics What is Decision Theory Application of Decision Theory in Cancer Diagnosis The …
Introduction to Artificial Intelligence – AI
You need to have basic knowledge of AI. You don’t have to be a Tech Pro like me to understand the principles of AI. So, …
Big Data and Big Data Analytics Explained!
The term “Big Data” have become quite common in the field of modern Relational Database Management and Data Analysis and today there are so many …