Machine Learning for Everyone

• Machine Learning Model Fit - In this project, you can use any of the datasets from the open-source datasets available. After importing the data, it has to be cleaned and split into the training data, testing data and validation data. Following this, the different models or algorithms can be tested on the dataset to find out which model fits the data best. The benefits of this Machine Learning Projects for Beginners are:
  • Intuition for which model will fit which data
  • Familiarity with Data Preparation techniques
  • Easy to imagine

• Sports Analytics - Sports is one of things that unifies most people in their interest. Using sports as the base ensures that this is one of those Machine Learning Projects for beginners they would enjoy. There are several datasets of sporting data available. You can parse through the data and perform several machine learning techniques in order to find the most appropriate ones as well as draw conclusions (obvious or not). There are several different sources to find sporting data:
  • Rotowire (https://www.rotowire.com)
  • CricSheet (https://cricsheet.org/downloads/)

• Regression for Retail - There are several datasets for Walmart sales which can be used in order to learn how to properly learn regression. It may also include dimension reduction techniques such as Principal Component Analysis, Linear Discriminant Analysis, etc. It is good place to start for a beginner and can be completed is a reasonably short period of time.

• Movielens Database - The Movielens dataset is extremely rich in terms of the number of entries as well as the genre. This dataset can be very useful as Machine Learning Projects for beginners where they can learn to learn about preparing the data in adequate format as well as building recommendation engines on these datasets.

• Twitter Sentiment Analysis - In this project, you need to crawl the twitter database and select a certain set of it. After the tweets are collected, you have to conduct a sentiment analysis on the same. This project can be done on different brands or for identifying hate tweets. There are several other ways for which the sentiment analysis can be done. It is possible that the crawling may not provide the data necessary in which case, you can DM twitter ( @TwitterSupport) to get the data. It is also possible to conduct sentiment analysis on several other sources but twitter is ideal because of the word limit which ensures that classification of the tweet is easier.

Applications of Machine Learning in AI Virtual Assistant

One of the best known and widespread applications of machine learning is the AI virtual personal assistants. The smart devices are now taking over the guidance and assistance of our actions replacing the traditional activities of an assistant. Some of the popular Machine Learning based personal assistants that are taking the world by storm are Alexa, Google Now, and Siri. These advanced technology-based assistants have almost seamlessly become a part of our lives and made it simpler.

Applications of Machine Learning in Traveling

Google Maps has emerged as one of the most widely used technology of today’s age. It is ‘the’ app that everyone uses to make travelling to unfamiliar places possible. This is one of the applications of Machine Learning where Google utilizes vast amounts of data from the GPS as well as data about factors like traffic, distance, time, direction, etc. to build an accurate model of route that one must take to travel between places in the minimum amount of time.

Applications of Machine Learning in Social Media

Social Media is also one of the applications of Machine Learning with immensely popular platforms like Facebook now using automatic face detection/ image recognition through their proprietary Machine Learning-based technology named DeepFace. The DeepFace project by Facebook is one of the most formidable examples of the applications of Machine Learning. The many other ways where ML is applied are the Automatic Friend Tagging Suggestions, Automatic Tags, Alternate Tags, and more.

Applications of Machine Learning in Marketing

The applications of Machine Learning in marketing and sales have revolutionized the marketing world. Machine Learning can analyze the customer’s purchase history and lists similar products which may interest the customer as is common practice in E-Commerce today. Machine Learning has made it easier to plan for promotions and manufacturing cycles through analysis of past data and recognition of trends which may not have been visible to the human eye.

Applications of Machine learning in automated cars

Amongst the most advanced innovations of modern technology, the self-driving car is one of the most interesting and intriguing. The functioning of automated cars entirely depends on Machine Learning. The algorithms of machine learning algorithms are used to determine the self-driving car challenges/variables such as car directions, speech and gesture recognition, language detection, etc.

Applications of Machine Learning in Sales and pricing

In an age where the world is becoming a marketplace, the need for determination of the right pricing is crucial. While there are many strategies for pricing, the key is to find the main objective. Hectic market competitions call for a dynamic pricing and the application of Machine Learning enables the designing of competitive pricings by analyzing the buying and selling activities in the market.