Machine Learning Projects For Beginners
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TOP 10 MACHINE LEARNING PROJECTS FOR BEGINNERS

The 21st century expects people to be technologically brilliant. Machine learning is essential as an emerging concept in this era. Many people have not heard about machine learning. So, let us understand the idea of machine learning in detail.

What is Machine Learning

Machine learning means the application of artificial intelligence which enables systems to learn automatically. It also allows improvement through the experience without any specific programming. It focuses on developing computer programs for accessing data and utilize it for self-learning.

Speech recognition is a typical example of machine learning. Alexa uses this technology of speech recognition to answer questions and obey orders. The main aim of machine learning is to let computers learn automatically. The intervention of humans needs to be at the bare minimum. It is the procedure of teaching a computer how to provide accurate predictions after getting accurate data.

Machine learning project ideas create a baseline for beginners. The clear concepts and give rise to a lot of questions in their minds. They frequently ask while doing the projects, “ What if we do it this way instead of that?” This way, it builds their analytical skills and makes them hungry for more.

Machine learning as a career

Machine learning is gradually becoming a popular subject. Those who are pursuing it as a subject are known as machine learning engineers. They need to show off the knowledge they have acquired through projects.

Beginners need to be assigned some basic projects in machine learning. People are worried about attending the practical sessions if they learn from home. However, a lot of hand-holding is done during the practical sessions of machine learning. Popularly, virtual practical sessions are beneficial.

Mechanical wizards say that machine learning has a bright future ahead. So, the students and ordinary people need to prepare themselves accordingly. However, machine learning is a progressive subject. Machine learning is getting enriched regularly. Thus, students need to keep themselves updated daily. They need to read a lot, check Google for the latest updates, and stay in touch with the latest news in their field. Just acquiring the degree doesn’t mean they have obtained all the knowledge in the subject. Machine learning is a vast subject.

How to be trained in machine learning skills

People often upskill themselves in the field of machine learning. Look for an organization that offers an all-encompassing training in Machine learning. Enroll yourself in a course that includes guiding sessions by an expert in the field of machine learning. The comprehensive, all-encompassing course must cover all the required topics. It must have everything from the basics to advanced classes like algorithm techniques. There should be a certification at the end of the course. The certificate is valued by any industry where the resources may be utilized. Such training provides practical applications in many machine learning skills.

In which fields can machine learning be applied?

Machine learning can be applied in several fields of daily lives. Let us check out some of the areas in which machine learning is applicable:

  • Agriculture
  • Anatomy
  • Detecting debit card fraud
  • Analyzing the financial market
  • Recognizing handwriting
  • Retrieving information
  • Linguistics
  • Software engineering
  • Recognizing speech

Various types of machine learning:

We have four different types of machine learning. They are as follows:

  1. Supervised machine learning
  2. Unsupervised machine learning
  3. Semi-supervised machine learning
  4. Reinforcement machine learning.

Let us know about these four types of machine learning in detail.

Supervised machine learning: Supervised machine learning applies what had been learned in the past to predict the future. A labeled example is used for that. A supervised machine learning algorithm develops a mathematical model of a set of data. It contains both inputs and desired outputs. 

It includes active learning, regression, and classification. The Classification algorithm may be used when the outputs have a limited set of values. A regression algorithm is used when the outputs may not have any numerical value within a range. A significant quantity of labeled data is needed for it.

This method offers teaching to computers through examples.

Unsupervised machine learning: Here, the information used for training is neither labeled nor classified. Unsupervised learning algorithms select a set of data containing only inputs. The system doesn’t figure out the correct output. Then, the structure is found in the data, like the clustering of data points. It identifies the common aspects of data science skills. The reaction of these common points is based on the presence or absence of common points in each new set of data. Unsupervised machine learning is independent of feedback. It is useful in the field of statistics. 

It identifies specific patterns in data. Also, it attempts to see the similarities that divide data into categories.

Semi-supervised learning: Semi-supervised learning is between supervised and unsupervised learning. It uses a tiny amount of labeled data and a large amount of unlabeled data. It is used when the data needs skillful resources for providing education.

Studies reveal when unlabeled training data (unsupervised learning) in combination with a small quantity of labeled data (supervised learning) provides authentic education.

Reinforcement learning: Reinforced learning (in the field of machine learning) deals with how software agents should take action to maximize rewards. Many algorithms for reinforced apprenticeships do not claim to have accurate mathematical knowledge of the MDP model.

Reinforcement learning models are useful in learning to play games against human opposition.

It provides action and identifies the rewards or mistakes. Hence, we understand that itis an interactive method. It permits machines to discover the ideal behaviour in a specific context. This way, it provides optimum performance.

Deep Q network of Google Deep Mind is an example of reinforcement learning.     

From where can we get a machine learning certification

Formal certification in machine learning is beneficial for the career. So, candidates may go in for training in machine learning certification from numerous online coaching center. It provides forty-four hours of training provided by an instructor. It is followed by a mentoring session from experts in the field. The experts offer dedicated courses to beginners. They are allowed to participate in real-life machine learning projects. Such experience enhances the interest as well as the knowledge of the candidates.

These courses cover everything from the fundamentals to advanced methods like unsupervised learning. The certification is an achievement in the professional field for beginners. This organization is a pioneer in providing online training in machine learning. The certificate of this course holds a lot of value. The practice offers a lot of practical training which helps beginners have a clear concept of machine learning. This course provides the perfect opportunity of getting upskilled to beginners.

The candidates get the golden chance to learn the subtle science of machine learning in partnership with the stalwarts of the industry. Such online courses also come with job assistance program which works in collaboration with Indian job portals.

The projects are meant to check the practical knowledge of students. They need to implement all their skills in machine learning.

Some important machine learning projects for beginners:

Some important beginner-friendly machine learning projects are discussed below:

  1. Tensorflow: It has been created by Google Brain group. This open-source library is for numerical computation. Tensorflow combines intense machine learning with models of neural networks with algorithms. This end to end encrypted library is difficult to learn. It is equally challenging to use. Veterans complain that it is not flexible at an elementary level. Students use this library of artificial intelligence to prepare Java projects; data flow graphs, and many other apps. Beginners need to put in a lot of effort to be a part of this machine-learning project. 
  2. Movielens Dataset: it is a virtual community designed to recommend movies to users. The users may develop their profile by voting movies. Users find it easier to decide which movies they will stream next with this dataset. As of now, it has more than one million ratings of 3.900 movies. Beginners may practice coding in different languages through data from here. 
  3.  Walmart: The technology used by Walmart at present provides the dataset for ninety-eight products across forty-five outlets. This way, Walmart utilizes machine learning to predict sales. It is difficult to predict sales accurately. However, the developers need to predict the weekly sales of a particular department, according to locations. Machine learning projects in collaboration with Walmart makes this prediction possible. Beginners may plan a machine learning project to predict weekly sales. It will help to plan out the inventory. Also, the stakeholders of Walmart will have the support of data(as provided by machine learning) while taking any drastic decision. This machine learning project will help the departmental stores and shopping malls to a great extent. 
  4. Predicting The Price of Stock Markets: The earlier prices, the primary indicators, and volatility indices help to predict the stock prices. Beginners may develop a machine learning project that predicts the stock price for the upcoming months. They may use the dataset for stock markets to predict the number of stocks. These stock market datasets may be downloaded from Quantum or Quandi. The price of the stock market is known to fluctuate at regular intervals. As a result, investors in stock markets are prone to losses. A machine learning project that predicts the prices of stock markets will help minimize the damages.
  5. Recognizing Human Activity Through a Smartphone: Almost the entire urban population uses smartphones. A smartphone seems to possess an uncanny ability to identify whether the owner is cycling or jogging. This ability to recognize the activity of the owner is an example of machine learning. Beginners may use it as a model to identify and classify the future events of the owner. It may also become a project of machine learning. They may be used for guiding the fitness tracking activities of the masses. This machine learning project will make tracking fitness over some time more accessible.
  6. Breast Cancer Prediction: Breast cancer is increasingly becoming a common problem among women. So, a dataset for determining whether a tumor is malign or not would be helpful. The machine should be able to decide on the factors, including the thickness of the lump. It should also be able to consider mitosis. Breast cancer is often diagnosed at the last stage. No treatment is possible at that stage. It provides beginners with an opportunity to try out R programming. Machine learning has enormous potential here. If this machine learning project goes through, breast cancer will be diagnosed at a very early stage. Then, the patient has a high chance of responding to treatment. Ultimately, she will have higher chances of complete recovery. This machine learning project will be a blessing in disguise for the medical world. 
  7. Predicting the Quality of Wine: Connoisseurs of wine agree that a variety of factors need to be considered while judging the quality of the wine. Factors like vintage and quality of wine play a critical factor in deciding its quality. Selecting good quality wine gets tricky unless the shopper is an expert. So, a machine learning project may be taken up to determine the quality of the wine. It should be a fun way to use data visualization, R programming and models of regression. Students who are beginners in machine learning may find this excellent opportunity. It gets very embarrassing to serve cheap wine or dregs during a party. Now, this machine learning project for predicting the quality of wine may help the owners choose premium quality wine.
  8. Fake News Detection Project: Fake news is widespread nowadays. Beginners of machine learning may take up a plan to detect fake news. At least the real story has to be distinguished from the fake. A dataset to identify fake news needs to be there in the pipeline. The ordinary people will be benefited from the outcome of the project. They will be saved from being misguided. So, there will be a clear indication of where they may believe. Fake news is spreading a lot of panic in the 21st century. Fake news is generated about celebrities, science, the discovery of some new medicines and so on. The parties about whom false report gets made are tired of clarifying themselves. So, this machine learning project on generating fake news is expected to clear the air at the earliest.
  9. Prediction of Loan for Individual: The tendency to take loans has increased over the years. So, beginners may do a project on machine learning to determine how much credit a particular user may choose. Will save the users some harassment. They won’t be left speculating between how much they need and how much they will be able to get. Different people need different amounts of loans. So, this machine learning app needs to be accurate so that the hassle may be saved.
  10. Machine Learning Project for Iris Classification: It is the most ancient and reputed machine learning project for beginners. The dataset of Iris flowers is one of the best. It needs to be downloaded from the CI ML Repository. The goal of this machine learning project is to divide the iris flowers among three different varieties.

Who are the trainers of machine learning at these online courses?

The online coaching centre puts prospective trainers through a stringent selection procedure. They need to provide a demo of training before they are certified as trainers. Apart from that, they are put through a technical evaluation. Their profile is screened. Only trainers with a high alumni rating are retained.

What are the pre-requisites of getting admitted to a machine learning course?

A beginner to a machine learning course needs to have a basic understanding of college-level maths and statistics. The knowledge of Python programming is also helpful. The beginner also needs to know the basic concept of statistics essential for data science and Python for data science.

For how long is the certificate of Machine learning from a reputed organization valid?

A certificate in the course of machine learning has lifelong validity.

Conclusion: While preparing machine learning projects, beginners need to understand that machine learning is not the same as artificial learning. There are many other ways (apart from machine learning) to develop artificial intelligence. Artificial intelligence is described as the ability of a machine to perform a duty needing human knowledge. Machine learning involves the ability of a computer to do a mission without human intelligence. Machine learning depends on data, the way artificial intelligence depends on human intelligence. 

 So, machine learning is being presented as the future of the world.  

It is a fascinating subject that requires a lot of guidance. People go in for professional courses that help them in career growth. Also, those who are passionate about the study of machines find the course a must-read.