Table of Contents

1. Introduction
2. Data Source
3. Setup
4. Exploratory Data Analysis
5. Conclusion
6. References

Introduction

Searching high and low for what makes a Tweet popular, we’ve scoured Twitter, gathering data on thousands of posts. But finding the secret to Tweet success by scrolling through data entries in an Excel spreadsheet is looking for a needle in a haystack. This is where data analysis comes in.

With data collected, we are one step closer to our goal of predicting the number of Retweets a STEM-related Tweet would receive.

Here in part 2, we’ll dive into just enough data exploration to build our final machine learning model.

Data Source

…

Table of Contents

1. Introduction
2. Data Collection
3. Conclusion

Introduction

Whether you are growing a business or wanting your ideas to reach as many people as possible, increasing your social media engagement is the key to victory. But what factors influence engagement? Time of post? Sentiment? Emojis? To a machine learning novice, this was the perfect challenge.

My goal was to predict the number of Retweets a STEM-related Tweet would receive.

So what’s step 1, you may ask? Data collection, of course! Data collection is one of the most vital steps in any machine learning project. …

Table of Contents

1. Introduction
2. Data Source
3. Setup
4. Data Preprocessing
5. Training the Model
6. Results
7. Conclusion
8. References

Introduction

The difference between music and speech is crystal clear to human ears, but how do you train a machine to learn the same?

My goal is to create a classifier that can differentiate between music and speech.

Like my earlier articles on Pokémon and waste classification, I’ll do this using a convolutional neural network.

I based my approach and model off of this TensorFlow tutorial, which builds a speech recognition network that recognizes 10 different keywords:

Data Source

For this project, I’ll use the GTZAN music speech dataset:

It…

Table of Contents

1. Introduction
2. Data Source
3. Setup
4. Data Preprocessing
5. Training the Models
6. Results
7. Conclusion
8. References

Introduction

With the amount of waste produced daily worldwide, waste management is a massive problem. A significant part of this issue is waste classification. But what if we could use AI to automate the process?

My goal is to build machine learning models that can classify waste as organic or recyclable from images.

Similar to my past article on Pokémon classification, I’ll do this using a convolutional neural network.

Data Source

This is the dataset that I’ll be using:

It is split into test and train directories that are both further…

Table of Contents

1. Introduction
2. Data Sources
3. Setup
4. Exploratory Data Analysis
5. Conclusion
6. References

Introduction

With the impacts of climate change ever-increasing, understanding the current state of clean technology is vital. By taking a look at where we are at and where we are going, we can figure out what we need to be doing today to prevent a climate disaster.

My goal is to explore and visualize clean technology data in various countries using Python.

To do this, I’ll use pandas, a Python library for data manipulation and analysis.

Data Sources

For this project, I’ll use three data sources:

1. CAIT | Country Clean Technology Data

This…

Table of Contents

1. Introduction
2. Data Source
3. Setup
4. Exploratory Data Analysis
5. Data Preprocessing
6. Training the Model
7. Conclusion
8. References

Introduction

Sarcasm can be incredibly difficult to spot over the internet. So, why not train machine learning models to discern it for us?

In this article, I go over how to build machine learning models that can detect sarcasm in news headlines.

I based my data preprocessing and deep learning model on the steps shown in this text classification tutorial by Google Developers:

Data Source

I used version 1 of this dataset for this project:

The sarcastic news headlines are from The Onion, while nonsarcastic ones are from HuffPost.

…

Table of Contents

1. Introduction
2. Data Source
3. Setup
4. Exploratory Data Analysis
5. Data Preprocessing
6. Training the Model
7. Results
8. Conclusion
9. References

Introduction

As a machine learning beginner and Pokémon fan, what better way to explore data visualization tools and neural networks than Pokémon classification?

My goal is to build a classifier that can predict whether a Pokémon is a fire-type or a water-type based on its image.

To do this, I’ll use a convolutional neural network, a class of deep neural networks most commonly applied to analyzing visual imagery.

Data Source

I’ll be using this dataset from Kaggle:

It contains images of all Pokémon from generation 1 to 7…