Top 5 Machine Learning Projects for Beginners

Top 5 Machine Learning Projects for Beginners

✅Top 5 Machine Learning Projects for Beginners. ✅As a beginner, jumping into a new machine learning project can be overwhelming. The whole process starts with picking a data set

, and second of all, study the data set in order to find out which machine learning algorithm class or type will fit best on the set of data.

Here are some tips from experts on how to get started:

  • Find a modestly sized data set which is relatively easy to analyze. Good places to search are the UCI ML Repository and Kaggle.
  • Experiment with the data set. To get a good “feeling” with the data set, you can run several top machine learning algorithms on the data to see how it behaves and what performance each algorithm achieves.
  • Pick the algorithm with the best performance and tune it accordingly.

Ok, now we are packed with a couple of general tips to get started on your ML project, let’s take a look at 10 interesting examples that will teach you how to use ML algorithms, tune them, but also how to analyze the given data.

1. Supervised Machine Learning w/ Iris Flowers Classification

The Iris Flowers dataset is seen as the “Hello World” of ML as it’s the classic example of classification. This dataset offers a great introduction as it requires you to learn how to explore data and how to load it. The benefit of this dataset is that is small to load into your memory (150 rows) and it has only four properties: Petal length, Petal width, Sepal length, and Sepal width.

The project involves the identification of four different species of Iris flowers using the four known properties. The dataset allows you to use a supervised learning algorithm as the data is labeled whereas unsupervised means that we are looking for hidden structures in the data as the data is unlabeled.

Classification Type? We are using Multiclass Classification here. This means that we should be able to predict accurately to which class a data point belongs.

Goal: Classify flowers among three species based on the properties of the flower: dimensions of petals and sepals.

Download: Iris Flowers Dataset
Full guide: To solving the problem can be found here.

2. Transactions Predictions w/ GNY

Machine Learning has been a trending topic for years now but many popular services are inaccessible for most developers primarily because of cost. A group called GNY is solving that with a decentralize their powerful machine learning platform that will be free to download and install. The machine learning platform is actually embedded within a blockchain so a user’s data is protected from potential hacks.

The team has released a demo that shows how this platform can predict groups of retail transactions through their powerful neural net, and a fully downloadable and customizable version of the platform is launching this Summer. GNY will have a library of selectable machine learning code sets that can be selected depending on the requirements of each individual and can be applied to their sidechain (as GNY will use Lisk’s sidechain technology).

Why is this so important? Almost all businesses are looking for an affordable way to unlock hidden value in their data, but not if it exposes them to security risks. The inherent structure of a blockchain helps to control data consistency and allow you to remain in control over your data

Performance increases as the validation can already be started for the subsequent block while the previous block is still active. Validation includes checking if the user has sufficient balance. Only for the wrongly predicted transactions, this work needs to be redone.

This demo is a fun starter project for people who want to predict simple numbers and the full platform launching this Summer should provide developers with much more power and customization. A good data set can be found at MLWave for predicting repeat buyers using purchase history.

Goal: Predict future transactions based on spending history.

3. Sentiment Analysis w/ Twitter

One interesting application of machine learning is sentiment analysis. Sentiment analysis has seen a major breakthrough with the rise of cryptocurrencies. Many have tried to build trading bots that incorporate sentiment analysis to make better trading decisions.

There are many other platforms that can be used for sentiment analysis like Reddit, Facebook, or LinkedIn as they all offer easy-to-use APIs for retrieving data. However, due to the consistent format of the data on the Twitter platform, this is the preferred data for machine learning. It is also much easier to pre-process as the tweets mainly consist of text, URLs, and hashtags.

The Twitter API knows many API libraries that can be used for integrating into your project. The wrapper for Python can be installed via pip with !pip install python-twitter . However, watch out when using the API as excessive usage can get you blacklisted. Therefore, Twitter provides guidelines on how to avoid being rate limited. If you require real-time data, the Twitter streaming API can save you.

A couple of fun examples to analyze:

  • Sentiment surrounding a newly released movie and compare it with reviews on IMDB and other rating websites.
  • Sentiment surrounding a particular election or any other trending political topic.
  • Predict the future direction of the price of a top 50 cryptocurrency based on the sentiments of its tweets.

Goal: A sentiment analyzer learns the various sentiments behind a piece of content. This task helps you think about designing various models to label a tweet as positive or negative. In a later phase, we can label tweets in a more nuanced way like ‘neutral’, ‘angry’, ‘optimistic’, …

Github Overview: of all Twitter-related data sets.

4. Recommender Systems w/ Movielens

Recommender systems are one of the most successful and widespread applications of machine learning technologies in business. You find recommender systems everywhere in your daily life. For example, when watching Youtube videos, the Youtube algorithm will propose you recommended videos based on your watching habits but also key insights they gained on watching patterns from running ML algorithms on the watching behavior of people all across the world.

We can find two types of algorithms for recommender systems:

  1. Content-based: As the label says, it looks for similarity in content.
  2. Collaborative filtering methods: This method looks for similarity in interactions. An example of an interaction can be looking at the ratings of a user and comparing them with others to find similar behavior/likings. The below picture illustrates this.

Currently, Movielens provides one of the most popular data sets for movie ratings which is an ideal dataset for beginners to experiment with.

Goal: Predict which movies users will like based on their ratings.


Tutorial: Towardsdatascience provides a tutorial for building a simple Recommender System in Python.

5. Stock Price Predictions w/ Quandl

Stock prices predictor is a system that learns about the performance of a company and predicts future stock prices. The tricky thing with stock price predictions is that many types and sources of data can be used:

  • Volatility indices
  • Historical prices
  • Global macroeconomic indicators
  • Fundamental analysis
  • Technical analysis using indicators

The benefit of analyzing the stock market is that it has shorter feedback cycles which makes it easier to validate your predictions. If you don’t know market cycles, I suggest to read up about this topic to understand how a typical cycle looks like.

To start off easy, you can pick up a simple machine learning example where we predict the 6-month price movement based on fundamental indicators from an organization his quarterly report.

Goal: Predict future price using fundamental and technical indicators.

Download: Stock market datasets from or

Further reading:

Machine Learning Guide: Learn Machine Learning Algorithms

Hands-On Machine Learning: Learn TensorFlow, Python, & Java!

Learn Azure Machine Learning from scratch

Master Machine Learning , Deep Learning with Python

Artificial Intelligence - TensorFlow Machine Learning

Introduction to Machine Learning with TensorFlow.js

Introduction to Machine Learning with TensorFlow.js

Learn how to build and train Neural Networks using the most popular Machine Learning framework for javascript, TensorFlow.js.

Learn how to build and train Neural Networks using the most popular Machine Learning framework for javascript, TensorFlow.js.

This is a practical workshop where you'll learn "hands-on" by building several different applications from scratch using TensorFlow.js.

If you have ever been interested in Machine Learning, if you want to get a taste for what this exciting field has to offer, if you want to be able to talk to other Machine Learning/AI specialists in a language they understand, then this workshop is for you.

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Further reading about Machine Learning and TensorFlow.js

Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning In Node.js With TensorFlow.js

Machine Learning in JavaScript with TensorFlow.js

A Complete Machine Learning Project Walk-Through in Python

Top 10 Machine Learning Algorithms You Should Know to Become a Data Scientist

TensorFlow Vs PyTorch: Comparison of the Machine Learning Libraries

TensorFlow Vs PyTorch: Comparison of the Machine Learning Libraries

Libraries play an important role when developers decide to work in Machine Learning or Deep Learning researches. In this article, we list down 10 comparisons between TensorFlow and PyTorch these two Machine Learning Libraries.

According to this article, a survey based on a sample of 1,616 ML developers and data scientists, for every one developer using PyTorch, there are 3.4 developers using TensorFlow. In this article, we list down 10 comparisons between these two Machine Learning Libraries

1 - Origin

PyTorch has been developed by Facebook which is based on Torch while TensorFlow, an open sourced Machine Learning Library, developed by Google Brain is based on the idea of data flow graphs for building models.

2 - Features

TensorFlow has some attracting features such as TensorBoard which serves as a great option while visualising a Machine Learning model, it also has TensorFlow Serving which is a specific grpc server that is used during the deployment of models in production. On the other hand, PyTorch has several distinguished features too such as dynamic computation graphs, naive support for Python, support for CUDA which ensures less time for running the code and increase in performance.

3 - Community

TensorFlow is adopted by many researchers of various fields like academics, business organisations, etc. It has a much bigger community than PyTorch which implies that it is easier to find for resources or solutions in TensorFlow. There is a vast amount of tutorials, codes, as well as support in TensorFlow and PyTorch, being the newcomer into play as compared to TensorFlow, it lacks these benefits.

4 - Visualisation

Visualisation plays as a protagonist while presenting any project in an organisation. TensorFlow has TensorBoard for visualising Machine Learning models which helps during training the model and spot the errors quickly. It is a real-time representation of the graphs of a model which not only depicts the graphic representation but also shows the accuracy graphs in real-time. This eye-catching feature is lacked by PyTorch.

5 - Defining Computational Graphs

In TensorFlow, defining computational graph is a lengthy process as you have to build and run the computations within sessions. Also, you will have to use other parameters such as placeholders, variable scoping, etc. On the other hand, Python wins this point as it has the dynamic computation graphs which help id building the graphs dynamically. Here, the graph is built at every point of execution and you can manipulate the graph at run-time.

6 - Debugging

PyTorch being the dynamic computational process, the debugging process is a painless method. You can easily use Python debugging tools like pdb or ipdb, etc. for instance, you can put “pdb.set_trace()” at any line of code and then proceed for executions of further computations, pinpoint the cause of the errors, etc. While, for TensorFlow you have to use the TensorFlow debugger tool, tfdbg which lets you view the internal structure and states of running TensorFlow graphs during training and inference.

7 - Deployment

For now, deployment in TensorFlow is much more supportive as compared to PyTorch. It has the advantage of TensorFlow Serving which is a flexible, high-performance serving system for deploying Machine Learning models, designed for production environments. However, in PyTorch, you can use the Microframework for Python, Flask for deployment of models.

8 - Documentation

The documentation of both frameworks is broadly available as there are examples and tutorials in abundance for both the libraries. You can say, it is a tie between both the frameworks.

Click here for TensorFlow documentation and click here for PyTorch documentation.

9 - Serialisation

The serialisation in TensorFlow can be said as one of the advantages for this framework users. Here, you can save your entire graph as a protocol buffer and then later it can be loaded in other supported languages, however, PyTorch lacks this feature. 

10 - Device Management

By default, Tensorflow maps nearly all of the GPU memory of all GPUs visible to the process which is a comedown but here it automatically presumes that you want to run your code on the GPU because of the well-set defaults and thus result in fair management of the device. On the other hand, PyTorch keeps track of the currently selected GPU and all the CUDA tensors which will be allocated.

TensorFlow Extended (TFX): Machine Learning Pipelines

TensorFlow Extended (TFX): Machine Learning Pipelines

TensorFlow Extended (TFX): Machine Learning Pipelines

TensorFlow Extended (TFX): Machine Learning Pipelines

Speaker: Martin Andrews

Event: Google I/O Recap 2019 Singapore AI - From Model to Device by BigDataX

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Further reading about TensorFlow and Machine Learning

Machine Learning In Node.js With TensorFlow.js

Machine Learning A-Z™: Hands-On Python & R In Data Science

TensorFlow is dead, long live TensorFlow!

A Complete Machine Learning Project Walk-Through in Python

Top 18 Machine Learning Platforms For Developers