Top 5 IEEE Projects on Machine Learning(ML)

Summary:

Mobile phone has become the best friend to everyone and can even state that it has become a third hand to us. This could not be an overstatement with the advent of the internet and the unique pattern by which it works to keep us constantly engaged with our interests. Ever thought how could it be possible to watch any advertisement on YouTube which is related to what you had just viewed a video in the same? If you have wondered someone has hacked your mobile, don't worry, we were on the same page. I thought the same before I got to know about Machine Learning and Artificial Intelligence. It is the era of Machine Learning where it dominates over every other technology. Machine Learning helps to build some really cool real-world projects. If you have zero knowledge of what is Machine Learning and Artificial Intelligence, fear not. You've landed in the right place to educate yourself about the same. Skyfi Labs provides you the required knowledge on Machine Learning through the exquisite Machine Learning projects. Now, let’s get to know about the basics first.

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1. Machine Learning (Career Building Course)

2. Fraud Detection using Machine Learning

3. Machine Learning using Python

4. Movie Recommendation using ML

5. Handwritten Digits Recognition using ML

6. Machine Learning Training & Internship

7. Brain Tumor Detection using Deep Learning

Difference between Artificial Intelligence and Machine Learning

To develop intelligent systems, Machine Learning and Artificial Intelligence are the most important technologies. They are part of computer science and are moreover related to each other. Since they are correlated, people often have the misconception that both are the same. However, in various cases, both technologies vary. Here are a few insights on both that could make you understand the differences between them.

1. Definition: Artificial Intelligence (AI) is a technology that enables the machines to think and stimulate behaviors like a human. Whereas, Machine Learning (ML) is a subcategory of AI. This does not need explicit programming and used to make the machine learn by itself with the help of past data.
2. Types: AI can be divided into three types, on the basis of its capabilities, which are, General AI, Strong AI, and Weak AI. ML can also be divided into three types, Reinforcement learning, Supervised Learning, and Unsupervised learning.
3. Applications: The main applications of AI are an intelligent humanoid robot, Siri, Online game playing, customer support using chatbots, Expert System, etc. The main applications of machine learning are Google search algorithms, Facebook auto friend tagging suggestions, Online recommender systems, etc.
4. Tasks: AI works towards creating a separate intelligent system on the whole to perform the complex tasks. Wherein, ML allows machines to perform certain tasks for which they are trained specifically.
5. Subsets: Deep learning and Machine Learning are the subsets of AI. Deep learning is the only subset of ML.
6. Main Concerns: AI is always concerned about increasing the success rate. But ML is concerned about certain patterns and accuracy.
7. Additional work: AI also does reasoning, learning, and self-correction. Whereas, ML does the learning and self-correction only when new data is added.
8. Deals: AL deals with unstructured, semi-structured and structured data. But ML deals only with semi-structured and structured data.
9. Scope: When comparing both the technology, AI has a wide range of scope, ML has only a limited range of scope.
10. Goals: AI aims to solve complex problems by creating a smart computer system just like a human being. ML allows the machines to provide accurate output by learning specific data.

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IEEE projects list for Machine Learning

1. Machine Learning using Python

To learn this, you need not possess prior knowledge of Python. With this IEEE machine learning project, you can predict the price of a house by collecting data from other houses in a particular area by putting ML into action. You will get to learn the various ML algorithms, importing and processing of dataset and evaluation and visualization of the model. As ML is the new future, this machine learning project would definitely give an insight into the usage of ML.

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2. Fraud Detection using Machine Learning

As ML has become the main source for the current technology for development, it is used to even prevent the ambiguous and illegal bank transactions. This machine learning IEEE project will be useful to detect the bank fraudulent transactions by analyzing the previous banking data. This IEEE project provides thorough learning of Linear Regression & its types, Mean Squared Error Function, Confusion Matrix and so on.

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3. Movie recommendation using Machine learning project

As part of this machine learning project, you will learn to develop a model that recommends the movie based on watching history. Also, you will learn about Linear Regressing and its types, Mean squared error function, update function, etc. while developing this project.

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4. Handwritten digits recognition using Machine Learning

In this machine learning IEEE project, you will learn to develop a machine learning model that recognizes the handwritten digits like vehicle number plates, cheque amounts, forms filled by hand, etc. using MNIST data. By developing this project you will learn about neural networks, tensorflow & keras libraries, deep learning, etc.

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5. Machine Learning (Career Building Course)

As the modern world runs mostly with the techies which use predominantly machine learning, this course would make you ready to get a current pace with the modern development in the technology. This course offers you to learn the basic concepts of machine learning to the advanced level. Also, it helps to get a better understanding of ML concepts practically and thereby learning to develop multiple machine learning projects.

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You can also check out the following list for more Machine Learning IEEE projects:

• Sentiment analysis using twitter data set
• Building an intrusion detection system using a filter-based feature selection algorithm
• A novel recommendation model using machine learning
• Detecting suicidal tendency using Machine learning
• Stock Price Prediction using machine learning
• Wine Quality Prediction using Linear Regression
• Iris Flower Classification using Machine Learning
• Bigmart sales prediction using Machine Learning
• Sales Forecasting using Machine Learning
• Health improvement using Machine Learning project
• Moneyball sports analyzer using machine learning project
• Students performance prediction using Machine Learning

What do you use machine learning for?

Have you ever thought about how Alexa is playing your favourite song with the expected volume when you ask it to play your favourite music station on the Amazon Echo? Well, it is all done only with Machine Learning and with the advent of Artificial Intelligence. Especially, Machine Learning does the work by feeding the machine with data and allowing it to learn. Eventually, it provides accurate results out of the learning process and the data that are fed to it. Machine Learning does not need much programming to make machines learn from experiences like a human. The machines learn, grow, and correct by themselves. The iterative process lets the machine learn by itself by leveraging the algorithms to find insightful information without being told to look for it. Now that's a lot. For a better understanding, consider some applications of ML in cyber fraud detection, self-driving Google car, and online recommendation engines from Facebook, Amazon, and Netflix. Machines can do this job by filtering the pieces of information based on certain patterns and give out accurate outputs.

The modern buzz word "Big data" has been used often in the years. ML is used to analyze the large chunks of Big data. ML replaces the traditional statistical techniques by allowing the algorithms or automating generic methods to extract and interpret the data. The results of Machine Learning applications are network intrusion detection, email spam filtering, web search results, real-time ads on web pages and mobile devices, and pattern and image recognition. All these are the by-products of ML to analyze a large amount of data.

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What is the best programming language for machine learning?

To respond to specific requirements like a man, the right programming language, appropriate algorithm, and relevant data are needed. When considering the popularity of programming languages in machine learning, Python comes as a king in the usage apart from other languages like R, Java, Lisp, Javascript and C++. About 57% of machine learning developers and data scientists use it and about 33% of the people prioritizing it for the development. Python is not only considered as a widely used language but also remains as a primary choice for the users.

Machine Learning is the predominant future of all the technologies. The expertise IEEE projects for machine learning at Skyfi Labs provide you the required knowledge to make you develop multiple projects. Do give a quick read on above mentioned projects to know the importance of ML and venture your career as a Machine Learning Engineer. To know more about the ML projects, please drop your request in the comment section below.

PS: Here is the detailed explanation of the project topics

Top 5 IEEE Projects on Machine Learning(ML)