As artificial intelligence continues to progress rapidly in 2021, achieving mastery of Machine Learning becomes increasingly important to all players in this field. This is because both artificial intelligence and machine learning complement each other. Therefore, if you are a beginner, the best thing to do is to work on some Machine Learning projects.
Creating real-world projects is one of the best ways to hone your expertise and translate your theoretical knowledge into a sensible experience. The more you experiment with very different machine learning projects, the more insights you will gain.
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These machine learning project ideas will help you move forward with all the practicalities you could accomplish in your profession as a machine learning expert. The focus of these machine learning projects is machine learning algorithms for beginners – that is, algorithms that don’t require you to have a deep understanding of machine learning and are therefore great for beginners.
To learn about machine learning, read our blog – What is machine learning?
Stock Price Prediction
The front-line concepts to start experiencing your hands-on machine learning projects are engaged with the Stock Price Predictor. Employers lookout for software programs that can monitor and analyze corporate performance and predict future prices of various stocks. With a wealth of data accessible in the stock market, it’s abuzz with opportunities for finance-minded data scientists.
However, before you start, you will need to have good knowledge of the following areas:
Predictive analytics – Leveraging various AI methods for various data processes equivalent to data mining, data exploration, etc. to “predict” the behavior of the feasible results.
Regression analysis – Regressive analysis is a type of predictive approach based primarily on the interaction between a dependent variable (target) and an unbiased variable (predictor).
Action analysis – In this technique, all the actions carried out by the 2 techniques mentioned above are analyzed, after which the result is entered into the machine learning memory.
Statistical modeling: It involves building a mathematical description of a real-world course and working out the uncertainties, if any, within that process.
Read our blog – Statistical Methods Every Data Scientist Should Know
Create ML Algorithms – From Scratch!
Creating machine learning algorithms from scratch can be one of the most interesting ideas for ML projects. This way, you can learn the practical details of their mechanics and understand how to transform mathematical instructions into functional codes. This skill will be useful in your future career in Machine Learning.
You can start by choosing an algorithm that is straightforward and not too complex. Behind the creation of every algorithm, even the simplest, are several carefully calculated decisions. Once you have reached a certain level of proficiency in creating simple ML algorithms, try modifying and extending their functionality. Below steps can help to create ML algorithms
- Understand the basics of the algorithm
- Explore different learning sources
- Break the algorithm into chunks
- Start with a simple example
- Validate with a trusted implementation
- Write up your process
- Basic programming experience in Python
- Introductory knowledge of linear algebra
- Basic probability theory
- Basic multivariate calculus
Explore ML courses
Iris Flower Classification ML Project
One of the best ideas to start experiencing hands-on machine learning projects is to work on the Iris Flowers Classification ML project. The iris flower data set is one of the best datasets for classification tasks. Since Iris flowers are of varied species, they can be distinguished based on sepal width, sepal length, petal width, and petal length. This ML project aims to classify the flowers between the three species: Versicolor, Setosa, and Virginica.
This particular Machine Learning project is generally referred to as the ML “Hello World”. The iris flower dataset contains numerical attributes and is perfect for beginners to learn about supervised Machine Learning algorithms, mainly how to load and handle data. Also, since it is a small dataset, it can easily fit in memory without requiring special transformations or scaling capabilities.
Source – Medium
Object Detection with Deep Learning
This is one of the coolest ML projects for beginners. When it comes to image classification, deep neural networks (DNN) should be your go-to choice. While DNNs are already used in many real-world image classification applications, this ML project aims to improve it.
In this ML project, you will solve the problem of object detection by leveraging DNNs. You will have to develop a model that can classify objects and also precisely locate objects of different classes. Here, you will treat the object detection task as a regression problem to object bounding box masks. In addition, you will define a multi-scale inference procedure that can generate high-resolution object detections at a minimal cost.
Image – Network architecture to detect an object in the image (Source – SAS)
- Basic knowledge of deep learning models
- Image classification
- Convolutional Neural Networks
- Computer vision
Enron Email Dataset
Enron Dataset is famous in natural language processing. Enron’s email dataset contains nearly 500,000 emails from more than 150 users. It is an extremely valuable data set for natural language processing. This project involves building an ML model that uses the k-means clustering algorithm to detect fraudulent actions. The model will separate the observations into ‘k’ group numbers according to similar patterns in the data set.
The aggregated Enron email and the financial dataset are stored in a dictionary, where each key in the dictionary is the name of a person and the value is a dictionary containing all characteristics of that person.
- Knowledge of statistical methods
- Unsupervised Machine learning algorithms especially k-means clustering
- Data mining
ML is still in its early stages and there are many projects to do and much to improve. With smart minds and sharp ideas, business-supported systems get better, faster, and more profitable. If you want to excel in Machine Learning, you need to gain hands-on experience with such ML projects. Only by working with ML tools and ML algorithms can you understand how ML infrastructures actually work. So, go ahead and put all the knowledge you have gathered through our Machine Learning project ideas guide to the test to create your own ML projects!
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