What is Machine Learning?

Well, I hope all of you are familiar with the name, Machine Learning. But, if you don’t know What is Machine Learning, don’t worry.

I have listed all the Top Courses on Machine learning, just make your choice by clicking the below button.

 

And at the bottom of this post, I have some Top Books on Machine Learning for FREE.

Before You Begin,

With any Machine Learning Projects, You SHOULD HAVE have an in-depth understanding of the problem that the data represents, the structure of the dataset and what all machine learning algorithms are best suited to solve the problem.

When studying the dataset, always use a statistical environment so that your focus remains on the questions you are looking to answer about the dataset instead of being distracted from a given technique and learning how to implement it in code.

Iris Flowers Classification

Iris flowers dataset is one of the best datasets in classification literature. The aim is to classify iris flowers among three species (setosa, Versicolor or virginica) from measurements of length and width of sepals and petals. The iris dataset contains 3 classes of 50 instances each, where each class refers to a type of iris plant. The dataset has numeric attributes and beginners need to figure out on how to load and handle data. The iris dataset is small which easily fits into the memory and does not require any special transformations or scaling to begin with. The Dataset is HERE.

Loan Prediction

The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. By examining factors such as class, sex, and age, we will experiment with different machine learning algorithms and build a program that can predict whether a given passenger would have survived this disaster. The Dataset is HERE.

Titanic Disaster

This data corresponds to a set of financial transactions associated with individuals. The data has been standardized, de-trended, and anonymized. You are provided with over two hundred thousand observations and nearly 800 features. Each observation is independent of the previous. For each observation, it was recorded whether a default was triggered. In case of a default, the loss was measured. This quantity lies between 0 and 100. It has been normalized, considering that the notional of each transaction at inception is 100. For example, a loss of 60 means that only 40 is reimbursed. If the loan did not default, the loss was 0. You are asked to predict the losses for each observation in the test set. The Dataset is HERE.

BigMart Sales Prediction

The data scientists at BigMart have collected 2013 sales data for 1559 products across 10 stores in different cities. The data also includes certain attributes of each product and store. The objective is to build a predictive model and find out the sales of each product at a particular store. Big Mart will use this model to understand the properties of products and stores which play a key role in increasing sales. The Dataset is HERE.

Walmart Recruiting – Store Sales Forecasting

You are provided with historical sales data for 45 Walmart stores located in different regions. Each store contains a number of departments, and you are tasked with predicting the department-wide sales for each store. Walmart runs several promotional markdown events throughout the year. These markdowns precede prominent holidays, the four largest of which are the Super Bowl, Labor Day, Thanksgiving, and Christmas. The weeks including these holidays are weighted five times higher in the evaluation than non-holiday weeks. Part of the challenge presented by this competition is modeling the effects of markdowns on these holiday weeks in the absence of complete/ideal historical data. The Dataset is HERE.

Stock Market Prediction

High-quality financial data is expensive to acquire and is therefore rarely shared for free. Here I provide the full historical daily price and volume data for all US-based stocks and ETFs trading on the NYSE, NASDAQ, and NYSE MKT. It’s one of the best datasets of its kind you can obtain. The Dataset is HERE.

Learn to build Recommender Systems

Learn how to build your own recommendation engine with the help of Python, from basic models to content-based and collaborative filtering recommender systems. The Dataset is HERE.

Sentiment Analyzer

Social media is thriving with tons of user-generated content. By creating an ML system that could analyze the sentiment behind texts, or a post, it would become so much easier for organizations to understand the consumer behavior. This, in turn, would allow them to improve their customer service, thereby providing the scope for optimal consumer satisfaction. The Dataset is HERE.

Health-Care

AI and ML applications have already started to penetrate the healthcare industry and are also rapidly transforming the face of global healthcare. Healthcare wearables, remote monitoring, telemedicine, robotic surgery, etc., are all possible because of machine learning algorithms powered by AI. They are not only helping HCPs (Health Care Providers) to deliver speedy and better healthcare services but are also reducing the dependency and workload of doctors to a significant extent. The Dataset is HERE.

MNIST Handwritten Digit Classification

Deep learning and neural networks play a vital role in image recognition, automatic text generation, and even self-driving cars. To begin working in these areas, you need to begin with a simple and manageable dataset like MNIST dataset. It is difficult to work with image data over flat relational data and as a beginner we suggest you can pick up and solve the MNIST Handwritten Digit Classification Challenge.

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