The AI Fusion

This is my first small Python project where I built a tac-tac-toe game in Python, we have played a lot in small classes while sitting at the last bench some of us have played at the first bench too. It is a very famous game that we are building today after the completion of this project we can play with our friends with the project we have made.

Here's a complete guide to the Tic-tac-toe game in Python!

Most of the students and working professional who wants to switch their career to data science think a data scientist has to work on building algorithms or architectures. They are not completely wrong but they also have to hover in between the data, and a data scientist needs to know how to clean and process the data so that insights and knowledge can be extracted from data that we can use later for optimizing the performance of an algorithm.

Machine learning is a dynamic technology that hovers around the concept of optimization where our main goal is to maximize the accuracy of a model. We have so many machine learning algorithms for solving a business problem with many optimizers. We should know about the types of Loss functions used in machine learning algorithms or architectures.

Here's a complete guide to Types of Loss functions in machine learning in Python!

When I was thinking about writing a blog related to Python I was confused about which topic I should start with, one thought from somewhere hit my mind that students face a real-time problem that how we learn Python. Some students come from a technical background and some do not but want to switch their career to data science so which path they should follow to learn Python efficiently? How should they start, and from where they should learn?

Here's a complete guide to How to learn Python in 2 months!

Profit in business is very important, most companies have hired employees to analyze their business and make a decision to enhance the efficiency of a company so that the revenue of the company increases. Companies are using artificial intelligence to make a profit, if they get any business problem in a company they make their employees work on it.

Here's a complete guide to Types of optimizers in machine learning in Python!

Machine learning algorithms heavily depend on the data that we get for training a model, In the process of model training, many steps are involved including feature engineering, EDA, feature scaling in machine learning, feature selection, and training a model.

Machine learning is becoming very popular in today’s world as we use artificial intelligence in business to increase the profit and revenue of a company. When we get a large set of records that can not be handled by machine learning algorithms that are based on mathematical and statistical equations then we look at machine learning architectures like ANN, CNN, and RNN.

Here's a complete guide to Activation functions in deep Learning in Python!

Here's a complete guide to Bias and Variance In Machine Learning in Python!

The performance of a machine learning model in the dynamic world of artificial intelligence is crucial, we have various algorithms for finding a solution to a business problem. Some algorithms like linear regression , logistic regression have parameters whose values are fixed so we have to use those models without any modifications for training a model but there are some algorithms out there where the values of parameters are not fixed.

Here's a complete guide to Hyperparameter tuning in machine learning in Python!

Feature selection in machine learning is gaining so much popularity because it makes the data more organized by reducing the number of features and keeping only relevant features, It removes irrelevant features by using techniques of feature selection. There are generally three types of feature selection techniques which are feature selection using the filter method, feature selection using the wrapper method, and feature selection using the embedded method.

Machine learning is becoming very popular in today’s world, for every business problem we use artificial intelligence techniques. There is a problem that occurs when we train a model and that is overfitting and underfitting, because of this problem, we are not able to find the accurate accuracy.

Here's a complete guide to Overfitting and Underfitting in Machine Learning using Python!

Feature engineering is essential while building a machine-learning model, we analyze the distribution of features, correlation outliers, etc. After analyzing the data we clean it with different methods. One of the problems in feature engineering is an imbalanced dataset

In today’s world, for increasing the revenue of any business we simply use artificial intelligence to make any task in a business efficient. We get the data through the client or get the resources from where we have to extract data using web scrapping, Database, or third-party API by using this data we make a machine learning model that predicts unseen data.

Here's a complete guide to Types of Matrices in Machine Learning using Python!

There are so many metrics for calculating the accuracy of an algorithm that we build for a business problem whether it is a regression problem or a classification problem but the problem with the clustering technique is how to measure an accuracy that comes under unsupervised learning. To solve this problem silhouette score in clustering comes into the picture which measures how good clusters are formed on data.