While there are different resources out there that are dedicated to machine learning, nothing compares to experimenting with machine learning projects.

As a beginner, you can’t go wrong with this approach. The more you experiment with machine learning projects, the more knowledge you gain and the more expertise you exhibit.

Machine learning is about solving problems.

It isn’t in itself about the programming languages, coding, analytics or data.

Dedicating yourself to machine learning use cases is always the way to go. The earlier you start, the better.

As a starter, early experimentation with machine learning practical projects does not only show you are getting serious with learning machine learning, it will also validate your expertise in deploying machine learning methodology to solving problems. 

Now, let’s face it.

You know where you easily go as a beginner when you want to learn machine learning: random blogs, books, YouTubes and reading about machine learning. Right?

This approach isn’t all bad, at least you are getting to know some basics.

But if all you do is reading about machine learning without personally experimenting with projects, you aren’t going to move fast enough.

What does it feel like learning machine learning without running through use case projects?

I tell you, I also have been caught up in this before. I had this feeling that I’m good at machine learning mainly because of how quickly I ‘understand‘ and appreciate lessons, exercises and other stuff on the internet.

‘But this isn’t entirely bad.’, you might say.

Yes, I agree.

But the challenge here is, if you aren’t really practising with these projects on your own, you will never understand how far you are from acquiring true machine learning capacity.

It’s that simple!

Yes, you can know every step but have you practically engaged with it?

You can know a lot about machine learning algorithms, but if you haven’t really experimented with practical exercises on your own, you are just joking! 

The truth is, not practically engaging with machine learning projects isn’t the best way to go in learning machine learning.  

Until you sit down and start solving problems by applying machine learning tools, you haven’t started yet.

No amount of mental knowledge can replace hands-on coding and the practice of machine learning. 

I am writing the post for you so you can have the opportunity of exploring different free and easy-to-handle machine learning projects.

The more you experiment with these projects, the more knowledge you gain and the more expertise you exhibit.

Practising with real-world machine learning cases is so key, it helps you learn faster and meaningfully.

You can make great progress by applying these carefully recommended lists of top machine learning projects. 

Why building machine learning projects?

Machine learning projects prepare you for real-life problems.

There are a lot of benefits in building your own machine learning projects.

Knowing these benefits will help you to learn with purpose.

This is one of the reasons why most impactful machine learning resources out there emphasise the need for hands-on experience when it comes to starting machine learning. 

Here are a few benefits of practising with machine learning use cases:

  • It shortens your learning curve. You are not just learning; you are actually practising what you are learning. 
  • It prepares you for real-life problems with a relatively manageable scope and easy-to-relate-with applications.
  • It creates an opportunity for you to showcase your portfolio, thereby, re-establishing your expertise in handling machine learning use cases.
  • It helps you to have an idea of the machine learning project pipeline, documentation and implementation strategy.
  • It’s useful in helping you identify and pursue your interest in different categories of machine learning domains and applications.

How to experiment with these projects (step by step procedure: machine learning project building steps to follow)

  • Understand the problem of statement
  • Understand the socio-economic or business implications
  • Understand the theory of change and expected outcomes
  • Highlight the core objectives and goal
  • Explore the data and its characteristics
  • Fit and test your model
  • Document each stage
  • Create your project portfolio

The following are the practical machine learning projects well suited for beginners to showcase their expertise and ability to handle user cases.

  • Forecasting Company Sales Project
  • Music Recommendation System Project
  • Stock Prices Predictor using Time Series Project
  • Boston Housing Price Prediction Project
  • Social Media Sentiment Analysis using Twitter Dataset Project
  • Iris Flowers Classification Project
  • Fake News Detection Project
  • Sports Predictor Project
  • Object detection Project
  • Loan Prediction Project

Also, read these:

How To Start A Career In Machine Learning: A Complete Guide To Career Transition.

Reasons You Don’t Need to Learn Machine Learning (and what to do instead).

1. Forecasting Company Sales Project 

Why forecasting the sales data?

Why would the company be interested in sales prediction?

Sales prediction helps the company make better data-driven decisions concerning the expected sales and other crucial company indicators.

For example, the company sales forecasting exercise helps the efficient allocation of resources for future growth and cash flow.

It also aids the estimation of company costs and revenue within both short-and long-term performance. 

So, having understood the big picture, how do you go about this project? How do you apply the step-by-step procedures in executing a machine learning project in this case?

Obviously, we can talk about these steps, but wouldn’t it be better and more interesting if we present ourselves with real-life user case studies?

Yep, I think so too.

So, let’s take a look at the two classical datasets:

Every algorithm has its way of learning patterns and then predicting.

If we compare machine learning-powered apps to traditional trading software, we will quickly see the difference.

Machine learning applications combine the capabilities of both a trader and an algorithm.

Such software has its own pre-trained ‘experience’ and is quick enough to make real-time operations.

1. The Walmart Sales Dataset: The data can be found on Kaggle at here

This project showcases sales data for 98 products across 45 outlets. The datasets contain weekly sales per store and department.

The project primarily deals with forecasting sales trend.

2. BigMart Sales Dataset: The data can be found on Kaggle at here

The dataset represents another popular and highly interesting sales project.

The dataset consists of 2013 sales data for 1559 products across 10 different outlets in different cities.

Like the Walmart sales project, the core objective of the BigMart project is to help you experiment with forecasting sales values using data science and machine learning techniques.

Specifically, you are presented with the task of building a model to predict the sales of each of 1559 products for the following year in each of the 10 BigMart outlets.

In both instances, the sales dataset also consists of certain attributes for each product and store.

Ultimately, this model will help Walmart and BigMart understand the properties of products and stores that play an important role in increasing their overall sales. 

Also, read these:

How to Learn Machine Learning The Self-Starter Way: Complete Guide.

Start Here With Machine Learning: A Beginner-Friendly Step-By-Step Procedure

2. Music Recommendation System Project

Photo by Alireza Attari on Unsplash

How is the music playlist recommended?

You haven’t indicated your interest, yet the system gives you a set of recommendations of music you might like to play or download.

How does it work?

It is called a music recommendation system.

As you might be aware, this system is quite common with e-commerce websites, online streaming sites and platforms such as Netflix or Amazon Prime and also music apps like JioSaavn, Spotify and Hulu.

The system primarily recommends and customizes contents based on individual customer preferences and browsing history.

This is a typical case of applied machine learning.

The ‘machine’ learns your behavioural decisions and patterns based on how you’ve liked, listened to or downloaded some songs before.

The system analyses your revealed preference upon which the recommended lists are made.

This is cool. Isn’t it?

But do you know you can actually execute this system given some dataset?

Here is an example.

Music Recommendation System Project dataset can be found on Kaggle Here

3. Stock Prices Predictor using Time Series Project.

Photo by Austin Distel on Unsplash

One of the best hands-on machine learning projects for the beginner is the Stock Prices Predictor.

Business organizations and companies today are on the lookout for software that can monitor and analyze the company performance and predict future prices of various stocks.  

Though very complex and challenging, predicting stock prices has been a familiar exercise among investment analysts.

The core challenge here points to the fact that stock prices data is known to be very granular and are determined by both local and global indicators such as the macroeconomic, microeconomic, fundamental and other highly volatile indicators.

Due to its non-linear features, analysing stock future returns may present some challenges.  

Basically, using historical information (past stock performances), you can forecast the future prices which ultimately aids decision making.

Ultimately, this project is an interesting machine learning project because of its simplicity and practicality.

Apart from the linear approaches, algorithms such as Support Vector Machines (SVM) and Artificial Neural Networks (ANN) are can also be deployed for this purpose. 

Stock Prices Predictor data set can be found on Kaggle here

Also, read these:

7 Costly Mistakes Beginners Make When Starting A Machine Learning Career (plus tips on how to avoid them).

A Complete Guide To Learning Machine Learning In Your Spare Time: An enthusiast’s approach

4. Boston Housing Price Prediction Project.

The Boston House Prices Dataset consists of prices of houses across different places in Boston, information on areas of non-retail business, age of people who own a house, the crime rate in that locality, and several other attributes. 

There are different determinants of housing price both from the demand and supply side.

Forecasting these prices across locations and regions, therefore, helps real estate players manage price expectation.

How does the application of machine learning help here?

Applying machine learning algorithm helps in feeding on key features of housing prices such as the house size, types, previous prices, location, facilities, environmental considerations, etc.

The goal of this project is to predict the selling price of a new home by applying basic machine learning concepts to the housing prices data. 

This dataset is considered a good start for machine learning beginners to kick-start their hands-on practice on regression concepts.

Boston Housing Price Prediction dataset can be found on Kaggle here

5. Social Media Sentiment Analysis using Twitter Dataset Project.

Photo by Austin Distel on Unsplash

One of the most interesting machine learning projects a beginner can explore relates to the development of an algorithm that classifies tweets as positive or negative.

This is referred to as sentiment analysis.

Sentiment analysis entails a deep and comprehensive scoping of sentiments behind content through the application of machine learning.

A good example is the sentiment analysis of text or online contents.

Organisations and content creators are often interested in understanding their consumers’ behavioural patterns and characteristics. 

By way of illustration, most beginners often use Twitter contents and data as a starting point and ultimate entry point to practice sentiment analysis using a machine learning algorithm.

What does the algorithm do?

The algorithm feeds on crucial information and metadata such as hashtags, location, tweets, likes, retweets, users, images etc. for insightful analysis.

Other online platforms such as Facebook, Reddit and YouTube produce enormous data huge enough to understand trends, public sentiments and opinions.

This exercise can also be useful in branding, marketing and all forms of business promotions

Social Media Sentiment Analysis Dataset can be found on Kaggle here

Also, read these:

5 Key Most In-Demand Soft Skill Sets You Need to Develop as a Machine Learning Career Starter

How To Build A Compelling And Winning Machine Learning Portfolio That Will Get You That Job.

6. Iris Flowers Classification Project. 

Photo by Martin May on Unsplash

Iris flowers dataset is so popular in machine learning that it’s commonly referred to as the “Hello, World” project.

The dataset represents a typical case of the classification problem.

This class of predictive modelling is applied where a class label is predicted for a given example of input data. 

classification model primarily attempts to draw some conclusions from observed values. The machine learning system, thus, feeds on the given inputs to predict the value of one or more outcomes.

This is how it works.

You are expected to build a machine learning algorithm that classifies the flowers into three species – Virginica, Setosa, or Versicolor based on the length and width of petals and sepals.

Iris Flowers Classification dataset can be found on Kaggle here

7. Fake News Detection Project.

Photo by Markus Winkler on Unsplash

The issue of fake news- also commonly referred to as ‘yellow journalism’- has been predominant in social media and other conventional news outlets as well.

At the core of fake news problems is the role of misinformation.

Distinguishing the fake from authentic news has become crucial given the role of media in disseminating information.

Most news platforms are often faced with the challenge of curtailing the spread of fake news using machine learning algorithms that aim at viralizing these and create a filter bubble.

This is where the use of the machine learning algorithm becomes key. 

One of such algorithms is the Natural Language Processing (NLP) techniques such as text classification.

NLP helps in detecting or filtering spammy stories from the feeds of users, especially unreliable sources. 

Therefore, the objective here is to get a beginner introduced to developing a detection algorithm given an existing dataset from both real and fake news.

Fake News Detection dataset can be found on Kaggle here

8.     Sports Predictor Project.

Photo by Jonathan Chng on Unsplash

Could you have thought that an algorithm can actually predict the outcome of a game?

Using machine learning methodology to predict the likelihood of games outcomes revolutionized the sports industry all over the world.

The execution idea might look ambitious, but a beginner can actually handle this project.

So, what exactly is the aim of sport outcomes forecasting?

It all boils down to efficient and effective team management and better game outcome.

Applying machine learning algorithm can also help in predicting players future prospects in terms of career development and talent scouting.

Availability of sports data can afford you the opportunity of exploring the data features and build an efficient machine learning model with fun and flexibility.

Sports/Games Predictor dataset can be found on Kaggle here

9.     Object Detection Project 

Object detection or image classification projects are common in machine learning tutorials, especially for beginners.

In carrying out this exercise, Deep Neural Networks (DNNs) are often deployed.

Neural networks are well known for classification problems and are used in digits classification.

Basically, you are expected to develop a model that can classify objects and localize objects of different classes.

Also, you will define a multi-scale inference procedure that can generate high-resolution object detections at a minimal cost. 

Object Detection dataset can be found on Kaggle here

10.  Loan Prediction Project. 

Getting a loan is obviously a function of different determinants such as monthly salary income, marital status, education, number of dependents, and employments, etc.

Due to the risk of default, loan applications go through a careful and comprehensive process known as verification or validation before they are approved. 

This project is about building a machine learning model that classifies how much loan a customer can get and predict the repayment probability of any loan.

Let’s say you are interested in building a predictive model to automate the process of selecting the right client.

The steps are relatively simple and direct: get the data, explore the data (Exploratory Data Analysis (EDA)), work on the missing value and perform outlier treatment, build your model and evaluate the result.

Loan Prediction Dataset can be found on Kaggle here

Final note

Great you have gone through this.

This is a starting point to solidifying your knowledge of learning practical machine learning.

As you challenge yourself to move higher in your interest in machine learning, I have carefully selected the following resources to help you learn better and faster:

  1. How To Start A Career In Machine Learning: A Complete Guide To Career Transition.
  2. Reasons You Don’t Need to Learn Machine Learning (and what to do instead).
  3. How to Learn Machine Learning The Self-Starter Way: Complete Guide.
  4. Start Here With Machine Learning: A Beginner-Friendly Step-By-Step Procedure
  5. 7 Costly Mistakes Beginners Make When Starting A Machine Learning Career (plus tips on how to avoid them).
  6. A Complete Guide To Learning Machine Learning In Your Spare Time: An enthusiast’s approach
  7. 5 Key Most In-Demand Soft Skill Sets You Need to Develop as a Machine Learning Career Starter
  8. How To Build A Compelling And Winning Machine Learning Portfolio That Will Get You That Job.

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