Code360 powered by Coding Ninjas X Naukri.com. Code360 powered by Coding Ninjas X Naukri.com

Last updated: Dec 14, 2021

Deep Dive into Machine Learning

Learn about each technique or algorithm used in the Machine Learning domain for building models, solving real-world problems, and delving into one of the most exciting technological domains in the twenty-first century.

Supervised Learning

Learn about Supervised Learning, one of the most widely used Machine Learning techniques. This technique requires trained data and input-output pairs, so are familiar with all aspects and master the technique.

Introduction to Supervised Learning

This article explores the ins and outs of Supervised Learning.

Author Pratyksh_7855

0 upvotes

What is-Regression EASY

Regression is a popular statistical method used in statistical analysis. It is used to find the relationship between a variable unknown to us and a variable whose values are known to us.

1 upvote

Drawbacks of Supervised Learning EASY

In this blog, we’ll discuss the drawbacks of the supervised machine learning technique.

Author Chaturvedi

0 upvotes

Linear Regression Learn about Linear Regression, a simple yet powerful algorithm for determining variable relationships. It should be noted that Linear Regression is limited to only one variable.

Locally Weighted Regression Learn about Locally Weighted Regression, which differs from Linear Regression in its non parametric approach. Learn about the mathematics that is involved as well.

Logistic Regression Understand Logistic Regression with its mathematical concepts, such as the Lost Function and decision surfacing.

K-Nearest Neighbors (KNN) Learn about K Nearest Neighbor, a popular non-parametric Machine Learning algorithm with applications.

Naive Bayes Understand the concept of Naive Bayes classification, as well as its implementation, types, and real-world applications.

Decision Trees Learn about decision trees, which are an important component of machine learning and prediction algorithms. Flowchart-like structures with nodes and leaves. Discover how to use it, as well as its visualisations and applications.

Random Forest Random Forest methods were a huge breakthrough in regression and classification difficulties. For complex machine learning issues, a combination of decision trees is highly recommended. Learn about the many types and how they're used.

Support Vector Machine (SVM) Support Vector Machines are supervised machine learning algorithms that are particularly efficient for large numbers of samples. Learn how to put it into practice through applications.

Boosting Algorithm A critical supervised learning algorithm that, as the name implies, boosts the accuracy and overall performance of the models by reducing variance and bias. One of the most widely used algorithms for improving model predictions.

Unsupervised Learning

This is in contrast to Supervised Learning, which does not use trained data but has a long list of real-world applications and is widely used by many tech companies.

Clustering Clustering is a prominent unsupervised learning approach. It allows users to classify and group certain data points, resulting in more distinct and accurate findings.

Evaluating Classification Models Performance Creating and deploying models isn't the only job of a data scientist or machine learning engineer. It is also necessary to analyse the performance of any algorithm or model. Finding the best performance can assist the engineer in deciding whether to predict values on any dataset. Evaluating the performance of a model can also help it improve in the future.

Reinforcement Learning

Another machine learning technique that aids in achieving a better and more accurate output is reinforcement learning. This strategy improves a model's decision-making and decision-making path.

Introduction to Reinforcement Learning

This article is an overview of Reinforcement learning.

Author Pratyksh_7855

0 upvotes

Markov Decision Process

In this blog, we will learn about the Markov Decision Process.

Author Anant Dhakad

1 upvote

The primary objective of this article is to understand Q-learning.

0 upvotes

Epsilon Greedy Algorithm

This article aims to throw some light on the epsilon greedy algorithm.

1 upvote

What is a Genetic Algorithm?

Understand genetic algorithms: their foundations, steps, operations, and applications. Discover the pros, cons, and examples of genetic algorithms in AI with FAQs.

Author Chaturvedi

1 upvote

TradeOffs like Exploration vs. Exploitation

This blog aims to understand tradeoffs like exploration and exploitation.

0 upvotes

Introduction to the Actor-Critic Model MEDIUM

In this article, we will discuss the actor-critic method, model-free and policy-based reinforcement learning, pseudo-code to the actor-critic method, and implementation of the Cartpole game.

Author Shubham Agarwal

0 upvotes

Real-life Applications of Reinforcement Learning

This blog walks you through on real-life applications of RL agents to give you an insight into just how powerful it is.

Author Arun Nawani

1 upvote

Drawbacks of Unsupervised Learning EASY

This article will take you through Unsupervised Learning and its drawbacks.

Author Prakriti

0 upvotes

Important Differences

Machine learning has many subcategories and techniques, each with some similarities and differences. It is critical to understand how these algorithms differ in order to make better decisions when dealing with classification or regression problems. One of the most significant distinctions is between supervised and unsupervised learning, technique, application, limitations, and data.

Difference Between Classification and Clustering

In this article, we will be discussing the difference between classification and clustering and the different techniques of both.

Author pranjal pratyush

2 upvotes

Linear vs. Non-Linear Classification

In this blog post, we'll learn about Linear Classification and Non-Linear Classification and then compare and contrast the two.

Author Shabeg Singh Gill

0 upvotes

Applications of Machine Learning

Discover how Machine Learning and its algorithms are used to solve real-world problems. Machine Learning has enabled everything from weather prediction to recommendation systems to self-driving cars.

Weather Forecasting - Application of ML

This blog will explain how Machine Learning helps in weather forecasting. In the implementation part, I will be implementing the small model, which shows the algorithms required for weather forecasting.

Author soham Medewar

0 upvotes

Case-Based Reasoning in Machine Learning

In this article, we will learn about Case-Based Reasoning in Machine Learning, its advantages and disadvantages, and the CBR cycle.

Author shruti_f97c

0 upvotes

Visualizing and Predicting Analysis of Cricket Match - Part 1

In this article, we will be visualizing and predicting the analysis of cricket matches. We will see some interesting analyses of the IPL as well.

Author Shiva_09_Sinha

0 upvotes

Visualizing and Predicting Analysis of Cricket Match - Part 2

This is the second part of Visualizing and Predicting Analysis of Cricket Match

Author Shiva_09_Sinha

0 upvotes

Top 10 Data Science Projects EASY

In this blog, we will discuss the top six data science projects.

Author Juhi Sinha

1 upvote