For your machine learning project, start off with algorithms that are relatively simple and move on to the more complex ones. Tutorials: R: Logistic Regression from Scratch; Python: Logistic Regression from Scratch; Python: k-Nearest Neighbors from Scratch; Develop A Neural Network That Can Read Handwriting.
Artificial intelligence (AI) and machine learning (ML) are impacting our everyday lives in ways hereto unimaginable. From intelligent games and apps to autonomous cars and healthcare, machine learning has brought about incredible transformation in several industries.
Particularly, IT engineering and development has witnessed some amazing transformations over the years—from generating data to harnessing it, the industry has come a long way. Incorporating some kind of intelligence into apps has become almost an essential aspect of development, be it a regular form-based application or an advanced application capable of aiding decision making.
As such, ML and AI have been generating renewed interest among academia and novices alike. If you’re new to machine learning and are looking for projects to complement your learning, your search ends right here. In this article, we’ll discuss 5 fun and insightful ML projects, which will give you a glimpse of various challenges that you may come across as an ML engineer. Let’s jump right in!
- Iris Flowers Classification –
Considered to be one of the best datasets in classification literature, the Iris flowers dataset is the first thing that a beginner must consider to get started with supervised machine learning. Often referred to as the “Hello World” of machine learning, the Iris dataset comprises several numerical attributes that beginners need to handle and classify accordingly. Being small and compact, the Iris dataset easily fits into memory and doesn’t require any scaling/transformations as such.
You can download the Iris dataset from the UCI ML repository here. - BigMart Sales Prediction –
The BigMart sales dataset features 2013 sales data of 1559 products across 10 different outlets in various cities, along with certain product and store attributes. The BigMart sales prediction project aims to predict the upcoming year’s sales performance of each of these 1559 products in every store. You need to employ unsupervised learning techniques to compute the predictions, helping BigMart identify the unique qualities across products and outlets that help increase their sales. - Sentiment Analysis using the Twitter Dataset –
Social media platforms like Twitter, Facebook, and YouTube are the breeding grounds of massive amounts of data. The main goal sentiment analysis is to mine this data to learn and analyze consumer behavior, which in turn aids branding, marketing, and even product design.
Twitter is the perfect place to start with for beginners wanting to practice sentiment analysis problems. The Twitter dataset comprises a comprehensive blend of various tweets and metadata (hashtags, retweets, etc.), what with a galaxy of user opinions across issues and topics, which aid data analysis and inference, thereby helping generate relevant insights. As a beginner project, you can start off with identifying and classifying tweets as positive or negative. - Sales Forecasting using the Walmart Dataset –
With sales data presenting the weekly sales per store, per department for over 98 products across 45 outlets, the Walmart dataset gives a pretty comprehensive sales picture if inferred properly. The main goal of the sales forecasting project is to forecast the sales for each department in every outlet to aid effective, placeholder='Add your personal notes here! (max 5000 chars)'>Recommended Posts:
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Update: This course has been updated to include 9 projects that will give you a real-world experience with different concepts of Machine Learning. Keep an eye out for more projects that will be added to this course in the future!
If you’ve ever wanted Jetsons to be real, well we aren’t that far off from a future like that. If you’ve ever chatted with automated robots, then you’ve definitely interacted with machine learning. From self-driving cars to AI bots, machine learning is slowly spreading it’s reach and making our devices smarter.
Artificial intelligence is the future of computers, where your devices will be able to decide what is right for you. Machine learning is the core for having a futuristic reality where robot maids and robodogs exist. Machine learning includes the algorithms that allow the computers to think and respond, as well as manipulate the data depending on the scenario that’s placed before them.
So, if you’ve ever wanted to play a role in the future of technology development, then here’s your chance to get started with Machine Learning. Because machine learning is complex and tough, we’ve designed a course to help break it down into more simple concepts that are easier to understand.
This course covers the basic concepts of machine learning that are crucial to get started on the journey of becoming a developer for machine learning. This course covers all the different algorithms that are required to simulate the right environment for your computer.
The course will start at the very beginning and delve right into machine learning, before breaking down the most important concepts principles. However, the course does require you to have a mathematical background as machine learning relies heavily on mathematical concepts. It also requires you to have some experience with Python principles which will be required when we put the algorithms to test in actual real-world Python projects.
The course covers a number of different machine learning algorithms such as supervised learning, unsupervised learning, reinforced learning and even neural networks. From there you will learn how to incorporate these algorithms into actual projects so you can see how they work in action! But, that’s not all. In addition to quizzes that you’ll find at the end of each section, the course also includes a 6 brand new projects that can help you experience the power of Machine Learning using real-world examples!
9 Projects That Are Included in This Course:
Project 1 -Board Game Review Prediction – In this project, you’ll see how to perform a linear regression analysis by predicting the average reviews on a board game in this project.
Project 2 – Credit Card Fraud Detection – In this project, you’ll learn to focus on anomaly detection by using probability densities to detect credit card fraud.
Project 3 – Stock Market Clustering – Learn how to use the K-means clustering algorithm to find related companies by finding correlations among stock market movements over a given time span.
Project 4 – Getting Started with Natural Language Processing In Python – This project will focus on Natural Language Processing (NLP) methodology, such as tokenizing words and sentences, part of speech identification and tagging, and phrase chunking.
Project 5– Obtaining Near State-of-the-Art Performance on Object Recognition Tasks Using Deep Learning – In this project, will use the CIFAR-10 object recognition dataset as a benchmark to implement a recently published deep neural network.
Project 6 – Image Super Resolution with the SRCNN – Learn how to implement and use a Tensorflow version of the Super Resolution Convolutional Neural Network (SRCNN) for improving image quality.
Project 7 – Natural Language Processing: Text Classification – In this project, you’ll learn an advanced approach to Natural Language
Processing by solving a text classification task using multiple classification algorithms.
Project 8 – K-Means Clustering For Image Analysis – In this project, you’ll learn how to use K-Means clustering in an unsupervised
learning method to analyze and classify 28 x 28 pixel images from the MNIST dataset.
Project 9 – Data Compression & Visualization Using Principle Component Analysis – This project will show you how to compress
our Iris dataset into a 2D feature set and how to visualize it through a normal x-y plot using k-means clustering.
All of this and so much more is included in this course. So, what are you waiting for?
Get started in machine learning with this epic course that makes machine learning simpler and easy to understand! Enroll now to step into the future of programming.
- Students who will like to understand and use Machine learning in real world projects will find this course very useful