People are sharing their Python scripts, data sets, and techniques in ways that you can learn from and contribute to. But there is an ever-growing amount of information spread throughout blogs, forums, and YouTube videos!
It becomes difficult to know what is real advice and what is just someone’s opinion or trick of the trade.
That is why we have created this article and bullet point series about some easy ways to get started with machine learning (ML) via blogging.
We will be talking about how to start your own ML blog, what types of content is needed for success, and more. So stay tuned and read on!
Disclaimer: This article may contain affiliate links where we could receive compensation at no cost to you. We keep our articles unbiased so as not to promote one product over another. You should do the same by reading through our article guidelines.
Start Your Own Machine Learning Blog The field of artificial intelligence has exploded in popularity due to the availability of powerful software such as Microsoft’s AI Studio.
There are many great free, open source software (FOSS) tools available for data analysis in statistics and machine learning. Some of the most well-known include:
* The popular statistical programming language is Python, so why not use it for some basic linear regression? You can with R via what’s known as “rPython.”
There are several excellent rPyton libraries that perform this task for you, such as Scikit-Learn. Many people begin experimenting with these tools by trying out simple linear regression. Once you have mastered that, try more difficult classification or even deep neural network applications!
Here are some helpful resources to read about using rPython to do linear regression: How to Do Linear Regression Using NumPy and Pandas — With Examples in R and Python. Also, reading through our article about setting up an interactive python environment will help you get started quickly.
There are several different types of machine learning. Some use supervised learning, where the algorithm is taught what information corresponds to what label or category before it can be used for prediction.
Other algorithms go beyond just classification by trying to predict more than one attribute at a time. These are referred to as regression models because they try to determine a numerical value for a dependent variable (in this case, the outcome) given an independent variable (such as age).
Still other applications apply pattern recognition. This involves looking at features of the data set and determining how well these patterns match known patterns. For example, if there are many instances of people with two middle names that begin with the same letter, then you could assume that most people with a new middle name will also start with the same letter.
By doing this repeatedly for many examples, you’ll get very good predictions. Because the model doesn’t have any labels attached yet, this type of analysis is sometimes called unsupervised learning.
Neural networks are one of the most powerful machine learning algorithms out there! They work by using layers to teach the network how to recognize patterns in data. Layers are connected together, creating an internal system that processes information and transfers it onto the next layer or output.
The key difference between feed-forward neural nets and other ML models is that they have more than one input layer and more than one output layer. This allows them to process not just discrete (like with binary answers) data, but also continuous (data that can be anything from numbers to images).
Because they learn complex concepts sequentially, neural networks are great at figuring out things like language, pictures, and music. In fact, some use this ability to create chatbots and programs that “speak” human languages!
There are many types of neurons you could choose for your net, too. Some are fully connected, while others are not. Fully connected means all inputs go into every neuron, and then those outputs are combined in different ways depending on the algorithm being trained.
What is deep learning?
Deep learning is an advanced type of machine learning that uses neural networks to perform tasks for computer vision, natural language processing (NLP), speech recognition, and other related fields. Neural networks are computational models inspired by how our brains work.
Neural networks have nodes or layers in between inputs and outputs. The strength of each connection node is adjusted to influence the output of the network. This process is referred to as training or education. For example, when teaching a child, you can increase the strength of certain connections to help them learn more quickly.
The most well-known types of neural networks are convolutional neural networks (CNNs) and recurrent neural networks (RNNs). CNNs focus on spatial relationships, while RNNs recognize patterns across time.
Computer programs using these techniques are typically one or many dedicated software packages trained through what’s known as reinforcement learning. Programs use large amounts of data to determine optimal strengths for individual nodes and overall performance of the algorithm.
That said, there are some cases where people create their own architectures and train them themselves! This is called architecture search and it’s pretty popular right now. In fact, Google has its very own AI research group working on this exact thing.
Why is it so important?
There are lots of reasons why having access to powerful tools like deep learning is crucial to advancing technology.
One of the most important things about applying ML to solve business problems is determining the right application for it. There are many ways to apply AI and automation in your organization, but not all applications are appropriate.
It’s easy to get excited when you see how well-trained ML algorithms perform certain tasks, so why not just use that algorithm to solve every problem? That’s what people have been doing with deep neural networks for some time now!
However, this isn’t necessarily the best approach. For example, if we applied an NN to classify pictures, then our computer would happily learn to identify any object and assign it a category. While this may be very helpful at times, it also means that it can never really determine what kind of picture something is because there is no structure to compare it against.
Similarly, if we used an NN to predict credit scores, then it would only ever know about numbers and whether or not they were larger than another number. It wouldn’t know anything about people, relationships, or concepts like “good credit score” or “bad credit score.”
Instead, we should look towards more structured methods of data analysis for these types of situations. By using techniques such as linear regression or logistic regresssion instead of NNs, we open up the model to include additional features beyond just numbers.
Recent developments in AI have been focused largely around two different strategies, which we can refer to as incremental or transformative. Incremental ML seeks to improve upon already existing algorithms by adding new features or components to them, for example, by incorporating computer vision into natural language processing.
Transcendental ML takes an entirely new approach that goes beyond what’s possible with traditional statistical techniques. This includes approaches such as generative adversarial networks (GANs) and reinforcement learning (RL).
Both types of ML aim to find patterns within large datasets so that they may be learned automatically. Systems using this technology often outperform those that require lots of human input or supervision.
However, these advanced methods come at a cost. They typically take longer to train than simpler alternatives, making them less practical when you need results quickly. On top of that, many experts feel that transcendent ML technologies are not only difficult to learn, but also hard to understand!
This article will discuss some potential causes of this skepticism and how professionals are addressing them.
One of the biggest mistakes new bloggers make is writing too much content without establishing strong themes or introducing important concepts. Writing about every little thing will not get you very far!
Content should be written with an audience in mind. Your readers should feel like they’re getting valuable information after reading what you have to say.
Your potential reader might be looking for tips on how to improve their personal style, learn more about marketing, or find ways to start a business. If your article does not clearly focus on one topic, then it may lose its readability.
Too many writers tend to over-explain things instead of showing examples. Use past experiences, pictures, and testimonials to provide insights and motivate people to do similar things.
Don’t just tell people something, prove it! An example is better than a thousand words.
If you are reading this article, then chances are you have already experimented with some of the many powerful free tools available for statistical learning. You may have played around in Python or Julia to learn what regression is and how to do logistic regression. Or you could have tried out Weka, an open source machine learning tool that works similarly to Google’s own product called K-Nearest Neighbor (KNN).
But even if you don’t feel like diving into those more advanced applications just yet, there is still one thing most people will find very helpful when it comes to intuitively understanding what algorithms do — classification!
Classification is the task of finding which group an item belongs to, be it a person, place, thing, or concept. For example, predicting whether someone is likely to become obese might use a prediction algorithm that looks at their income, body mass index, and whether they enjoy eating foods high in calories.
In fact, several companies now offer predictive analytics services that perform classifications using such methods. These types of systems can help employers determine who is eligible for benefits, assess risk for disease or disability, and identify potential job applicants. In other words, they play a crucial role in our society!
That is why it makes sense to develop basic skills in classification.
