Top 10 Projects Of Machine Learning For Beginners

Hello guys, welcome back to my blog. In this article, I will discuss the top 10 projects of machine learning for beginners.

If you require an article on some other topics then comment us below in the comment box. You can also catch me on Instagram – Chetan Shidling.

Also read – The Future Scope Of VLSI Engineers.


What Is Machine Learning

As we know, we are living in a world of humans and machines, humans have been evolving and learning from past experience for millions of years. On the other hand, the era of machines & robots has just begun. In today’s world these machines are the rewards are like, they need to be a program before they actually follow our instructions. But what if machines started to learn on their own? So here machine learning comes into the picture.

“Machine learning is the core of much futuristic technology advancements in our world”

Today we can see various examples of machine learning around us, such as Tesla’s self-driving car, apple Siri, Sophia, and many more.

Before seeing the top 10 projects of machine learning for beginners, we will see,

What exactly is Machine learning?

“ Machine learning is a subfield of artificial intelligence, that focuses on the design of the system, that can be learned from & make decisions & predictions based on the experience, which is data in case of machines.”

It allows computers to act and create data-driven decisions rather than being explicitly programmed to carry out a certain task. These programs are designed to learn & improve over time when exposed to new data.

Let’s have a look at the top 10 projects of machine learning for beginners.

10. Sales forecasting.

09. Cancer tumour detection.

08. Human activity recognition with Smart phones.

07. Machine translation.

06. Recommender system.

05. Sentimental analysis.

04. Caption bot.

03. Music generation.

02. Image colouring.

01. Object detection.

Let’s discuss one by one the top 10 projects of machine learning for beginners.

10. Sales Forecasting

This is the common test that can be performed by various organizations, this can be used in retail and e-commerce sectors. Machine learning can help us to discover the factors that influence sales in an organization, helps us to estimate the number of sales, that it will be going to have in the future. We can also estimate the future sales volume.

Mainly it identifies how many products will sell in a certain period? In what market?? At which cost?. Apart from the prediction of sales, it’s having a high demand for deriving insights into how workforces? resources? & cash flow that has to be managed by an organization.

Retail joined world mart uses machine learning for predicting its sales, for different products across various regions.

09. Cancer Tumour Detection

Machine learning can be used in healthcare industries for predicting diseases before they occur. It adds analysts to process huge and complex medical datasets and helps for getting clinical insights.

Machine learning has been developed to detect cancer tumor cells in the human body and helps us to predict whether a tumor is malignant or benign. Machine learning can cure “DIABETIC RETINOPATHY “ disease, it plays a big role in discovering new drugs.

“Arima model”, “convolutional neural networks”, “ k-means cluster” are majorly used in the detection of diseases.

08. Human Activity Recognition With Smart Phones.

Nowadays mobiles are designed in such a way that, it has to detect automatically when we are doing certain activities such as running, cycling, etc. This is nothing but Machine learning at work.

To become familiar with these types of projects, the ‘novice machine learning engineers’ are making use of the dataset, which contains fitness activity records for a few people, which will be collected through mobile devices that are equipped with ‘ inertial sensors’. Then the learners can build classification models that are going to predict future activities accurately.

07. Machine translation

We are familiar with google translate and we love to use it. The technology following google translate is “ Machine translation “. Machine translate can instantly translate between 100 different human languages. It’s available in smartphones and smartwatches.

It uses the sequence to sequence learning. It is a subfield of computational linguistics that will be focused on translating techs from one language to another language. The input consists of a sequence of symbols in some particular language, then computer programmers convert this into another

The short and long memory networks are used for creating applications, which supports machine translation.

06. Recommender System

This is an information filtering system that will be used to predict rating & priority. Companies which are using a recommender system focus on
increasing sales & enhance customer experience.

Companies like Amazon, Netflix liberate the recommender system to help users discover new and relevant items like products and videos, jobs, movies, and music.

05. Sentimental Analysis.

It’s the process of using “Machine learning” & natural language processing technique to analyze customers’ sentiments based on their emotions. This allows businesses to identify customer’s emotions towards products, branch, or services in online conversation & in feedback.

It focuses not only on polarity such as positive and negative but also on feelings and emotions like happiness, anger, sadness, etc. And also focuses on intentions like, interested, and not interested.

This can also be carried out from tweets and Instagram posts to understand the mood of the public towards brands or events like elections.

04. Caption Bot

Machine learning helps us to generate a textual description for an image or videos. It’s an easy Problem for humans but very challenging for a machine, as it involves both understanding the content of an image, and how to translate this understanding into natural language.

Microsoft has created its own caption bot, where we can upload an image or URL of the image. Then it will give us a textual description. Caption AI is another application that helps us to add a perfect caption and best hashtags for a picture.

We can use recurrent neural networks and long, short memory networks to build automatic image caption generations.

03. Music Generation

Music is a language of composers, to communicate. But, it’s impossible for machines to understand structures, notes, patterns of music. The python tool kit used for computer-aided musicology is Music21. It allows us to teach the fundamental sub music theory, generates music example theory, and study music.

The tool kit is helpful in providing a simple interface to acquire the musical notation of MIDI files, which stands for “ Musical instrument digital interface”. Along with this, it allows us to create a note, chords of the object. So that we can make our own MIDI files easily.

To generate music automatically we can use wave net architecture, long-short term memory network.

02. Image Colouring.

Automated colourisation of black and white images has been subject to much research within the computer vision and machine learning communities. What is image colourisation exactly? It’s the process of taking an input greyscale of a black and white image and then producing an output as a colourised image. That represents the symmetric colours and different tones of input.

For example, an ocean, on a clear sunny day must be plausibly blue, and it can’t be colored pink or brown by the model colorization. Colourising black and white image with deep learning have become an impressive show key for a real-world application of neural networks in our life.

Autoencoders and convolutional neural networks are mostly used for automated image colorization.

01. Object Detection

Object detection in a computer vision technique that aims to detect objects like, cars, buildings, and human beings. The objects can be generally identified from either pictures or video feeds. Object detection has been applied broadly in video severance and self-driving cars. Object detection locates the presence of an object in an image and draws boundary box
around that object.

First, we should take an image as input, and then we have to divide the input into various regions, and then we have to consider each image as a separate image and pass all the regions to the convolutional neural network. We can classify them into various classes. Google stuns of flow to the library provides its own object detection EPI to identify various objects in an image detectorists facebooks, AI researches of a software system that implements state of the cart of the object detection algorithm.

Google uses object detection for building its self-driving vehicles. Regions with convolutional neural features, or RCNN and mass car CNN models are mainly used for object detection.

These are the top 01 machine leaning projects for beginners. I hope this article may help you all at lot. Thank you for reading.

Also read,

Chetan Shidling

Entrepreneur, Electrical Engineer, Blogger, YouTuber, Content Writer Google Adsense Expert, SEO Expert, Digital Marketer

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