7 Major projects in Machine Learning

While hiring machine learning engineers and data scientists, the companies look for experience related to machine learning projects. A complete machine learning project involves preparing a problem statement, designing a specific solution to the problem, gathering and processing the data, and evaluating the quality of your model.

The machine learning projects you pick up say a lot about your maturity and creativity as a data scientist. If you have the right skillset in your resume, you can pursue a great machine learning career with a good pay scale.

In this guide, we have mentioned some of the best machine learning projects you must have on your portfolio to showcase your machine learning skills to the companies.

Recommender systems
Recommender systems are used everywhere, ranging from movies to products and books. All the medium and large-sized businesses are creating their in-house recommender systems. The most common recommender system is the YouTube Video Recommendation System.

Tools Required – Python and sklearn (scikit-learn)

Algorithms – Deep neural networks and classification algorithms 

Dataset – This project will need a statistical and social network of YouTube data, i.e. videos. It will consist of IDs, and other information such as uploader, length, ratings, category, age, and a list of up to 20 IDs of related videos.   

2. Time Series Analysis

Time series analysis includes developing mathematical models that best describe an observed time series and identify the causes behind it. It basically tries to seek the ‘why’ behind a time series dataset. The four main components of a time series data are:

Level – the baseline value for the series

Trend – the optional and linear behaviour of the series

Seasonality – the cycles of behaviour over time

Noise – the optional value that cannot be described by the machine learning model.

Tools Required – Python and NumPy

Algorithms – Linear regression, Recurrent Neural Networks (RNN), Long short-term memory (LSTM), ARIMA-based techniques. 

Dataset – It will include the data related to the stock market such as year and stock value.

The quality of your model will primarily depend on how well it describes the given data and the interpretation it provides to better inform the problem domain. 

3. Chatbot using Deep learning 

Another interesting and important project is to create a chatbot using deep learning methods. You can implement the chatbots in various ways and a good chatbot accurately identifies the context the user is asking and then provides it with the relevant answer.

Tools Required – Deep learning methods 

Algorithms – Natural language processing and neural networks 

Dataset – the dataset may contain a record of past conversations to train the machine model.

4. SMS Spam detection

With the increase in the number of mobile users, the number of SMS spam messages has also increased. Though people think mobile messaging is ‘clean’ and trusted, the volume of mobile phone spam is dramatically increasing year by year. 

SMS spam detection is a solution to deal with the problem. It also includes certain concerns and quick fixes from Email spam filtering while confronting its issues. 

Tools – Python and matplotlib

Algorithm – NLTK

Dataset – It includes SMS tagged messages, that is, a set of SMS messages in English.

5. Face detection

Facial recognition is the first step for whole face biometrics, and its accuracy highly impacts the performance of sequential operations. The most common example can be seen with the face detection algorithm available on the modern digital cameras where it detects faces and adjusts the focus accordingly.

Tools – Open CV, Python, Seaborn

Algorithms – Convolution Neural Network and face detection algorithms

Dataset – A dataset having images marked by bounding boxes. 

6. Twitter sentiment analysis 

Solving Twitter data has become an inevitable part of the sentiment analysis problem. You will classify a tweet as a good or bad, or positive or negative in the project. Working on Twitter data is both fun and challenging at the same time.

Tools – Python, Seaborn

Algorithms – Natural language processing (NLP)

Dataset – Twitter data containing more than 30,000 tweets

7. Age detection

Age detection can come under the category of face detection. It could be a fascinating challenge for any deep learning enthusiast. The images in the dataset will vary in terms of scale, pose, expression, illumination, age, resolution, occlusion, and makeup.

Tools – Python, OpenCV, Seaborn

Algorithms – Convolution Neural Network

Dataset – It will contain thousands of images and you have to detect the age 

Bottom line

These machine learning projects will give you an upper-hand over your competitors. You must try to finish a problem before moving on to the next.  

Along with adding these projects to your resume, you must also put them on your GitHub profile. Recruiters, these days, also check the GitHub profile of a candidate before hiring them.