What follows are outlines of these 2 supervised machine learning approaches, a brief comparison, and an attempt to reconcile the two into a third framework highlighting the most important areas of the (supervised) machine learning process.
How does this compare with Guo's above framework? Let's have a look at the 7 steps of Chollet's treatment (keeping in mind that, while not explicitly stated as being specifically tailored for them, his blueprint is written for a book on neural networks):
Chollet's workflow is higher level, and focuses more on getting your model from good to great, as opposed to Guo's, which seems more concerned with going from zero to good. While it does not necessarily jettison any other important steps in order to do so, the blueprint places more emphasis on hyperparameter tuning and regularization in its pursuit of greatness. A simplification here seems to be:
We can reasonably conclude that Guo's framework outlines a "beginner" approach to the machine learning process, more explicitly defining early steps, while Chollet's is a more advanced approach, emphasizing both the explicit decisions regarding model evaluation and the tweaking of machine learning models. Both approaches are equally valid, and do not prescribe anything fundamentally different from one another; you could superimpose Chollet's on top of Guo's and find that, while the 7 steps of the 2 models would not line up, they would end up covering the same tasks in sum.
Mapping Chollet's to Guo's, here is where I see the steps lining up (Guo's are numbered, while Chollet's are listed underneath the corresponding Guo step with their Chollet workflow step number in parenthesis):
In my view, this presents something important: both frameworks agree, and together place emphasis, on particular points of the framework. It should be clear that model evaluation and parameter tuning are important aspects of machine learning. Addition agreed-upon areas of importance are the assembly/preparation of data and original model selection/training.
As you may have guessed, this has really been less about deciding on or contrasting specific frameworks than it has been an investigation of what a reasonable machine learning process should look like.
Matthew Mayo (@mattmayo13) is a Data Scientist and the Editor-in-Chief of KDnuggets, the seminal online Data Science and Machine Learning resource. His interests lie in natural language processing, algorithm design and optimization, unsupervised learning, neural networks, and automated approaches to machine learning. Matthew holds a Master's degree in computer science and a graduate diploma in data mining. He can be reached at editor1 at kdnuggets[dot]com.
Machine Learning is a fantastic new branch of science that is slowly taking over day-to-day life. From targeted ads to even cancer cell recognition, machine learning is everywhere. The high-level tasks performed by simple code blocks raise the question, "How is machine learning done?".
The ultimate goal of machine learning is to design algorithms that automatically help a system gather data and use that data to learn more. Systems are expected to look for patterns in the data collected and use them to make vital decisions for themselves.
In general, machine learning is getting systems to think and act like humans, show human-like intelligence, and give them a brain. In the real world, there are existing machine learning models capable of tasks like :
As you know, machines initially learn from the data that you give them. It is of the utmost importance to collect reliable data so that your machine learning model can find the correct patterns. The quality of the data that you feed to the machine will determine how accurate your model is. If you have incorrect or outdated data, you will have wrong outcomes or predictions which are not relevant.
A machine learning model determines the output you get after running a machine learning algorithm on the collected data. It is important to choose a model which is relevant to the task at hand. Over the years, scientists and engineers developed various models suited for different tasks like speech recognition, image recognition, prediction, etc. Apart from this, you also have to see if your model is suited for numerical or categorical data and choose accordingly.
Training is the most important step in machine learning. In training, you pass the prepared data to your machine learning model to find patterns and make predictions. It results in the model learning from the data so that it can accomplish the task set. Over time, with training, the model gets better at predicting.
From the detection of skin cancer to detecting escalators in need of repairs, or to its evolution today, machine learning has granted computer systems new abilities that we could have never thought of. But what is machine learning and how does it really work under the hood? What actually are the steps to machine Learning?
Machine learning is a field of computer science that gives computers the ability to learn without being programmed explicitly. The power of machine learning is that you can determine how to differentiate using models, rather than using human judgment. The basic steps that lead to machine learning and will teach you how it works are described below in a big picture:
The link is to create a system that answers a particular question. This question answering system called a model is created via a process termed as training. The main goal of training is to create an accurate model that answers our questions correctly, at least for most of the times. But in order to train a model, you also need to collect data on what you'd want to train on. This is where you start and then the rest follows. The detailed information to the next steps is given below:
Once you know exactly what you want and the equipments are in hand, it takes you to the first real step of machine learning- Gathering Data. This step is very crucial as the quality and quantity of data gathered will directly determine how good the predictive model will turn out to be. The data collected is then tabulated and called as Training Data.
After the training data is gathered, you move on to the next step of machine learning: Data preparation, where the data is loaded into a suitable place and then prepared for use in machine learning training. Here, the data is first put all together and then the order is randomized as the order of data should not affect what is learned.
Once the evaluation is over, any further improvement in your training can be possible by tuning the parameters. There were a few parameters that were implicitly assumed when the training was done. Another parameter included is the learning rate that defines how far the line is shifted during each step, based on the information from the previous training step. These values all play a role in the accuracy of the training model, and how long the training will take.
Machine learning is basically using data to answer questions. So this is the final step where you get to answer few questions. This is the point where the value of machine learning is realized. Here you can Finally use your model to predict the outcome of what you want.
In this article, I describe the various steps involved in managing a machine learning process from beginning to end. Depending on which company you work for, you may or may not be involved in all the steps. In larger companies, you typically focus on one or two specialized aspects of a project. In small companies, you may be involved in all the steps. Here the focus is on large projects, such as developing a taxonomy, as opposed to ad-hoc or one-time analyses. I also mention all the people involved, besides machine learning professionals.
In chronological order, here are the main steps. Sometimes it is necessary to recognize errors in the process and move back and start again at an earlier step. This is by no mean a linear process, but more like trial and error experimentation.
5. The true machine learning / modeling step. At this point, we assume that the data collected is stable enough, and can be used for its original purpose. Predictive models are being tested, neural networks or other algorithms / models are being trained with goodness-of-fit tests and cross-validation. The data is available for various analyses, such as post-mortem, fraud detection, or proof of concept. Algorithms are prototyped, automated and eventually implemented in production mode. Output data is stored in auxiliary tables for further use, such as email alerts or to populate dashboards. External data sources may be added and integrated. As this point, major data issues have been fixed.
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Machine learning has given the computer systems the abilities to automatically learn without being explicitly programmed. But how does a machine learning system work? So, it can be described using the life cycle of machine learning. Machine learning life cycle is a cyclic process to build an efficient machine learning project. The main purpose of the life cycle is to find a solution to the problem or project.
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