One of the most important technological advancements nowadays is Machine Learning (ML). It can be used to create Machine Learning models that are specifically purposed towards a task that can be learned by a machine to be able to handle automatically such as predictions, playing video games and diagnosing diseases.
Machine learning has been a consistent term in the general population nowadays.
When articles and blogs discuss this certain topic, they also bring up Machine Learning ‘models’, which sometimes puzzles the reader.
To understand the interconnectedness of Machine Learning models and Machine Learning itself, we should first get an insight into what Machine Learning is.
Table of Contents
- What is Machine Learning?
- Why Machine Learning Matters:
- Machine Learning Use Cases
- The steps of a simple machine learning project looks like below:
- Classification of Machine Learning
- What is a Machine Learning Model?
- What is a Machine Learning Algorithm?
- Machine Learning Models For Specific Tasks
- Pretrained Models
- Frequently Asked Questions
What is Machine Learning?
Machine learning, to put it simply, is a way for machines (humans) to learn using data (memory).
Things that we do as humans such as solving difficult mathematical equations, writing creative stories, doing research, identifying a car’s model and brand just by looking, hearing and translating other languages by listening et cetera.
These can all be ‘learned‘ by small children as they grow up, accumulate experiences and fully develop their brain until one day, they too can do the same things, perhaps in their own way.
These can all be also ‘learned‘ by machines as it is trained, accumulate data and fully realize a model until one, they too can do the same things, perhaps not as well as humans or perhaps better.
In slightly technical terms, Machine Learning is the concept of using coded automation in computers, using algorithms produced by brilliant minds of scientists, to produce outputs that we want the machine to output.
We input an accumulation of data for the machine to go through as it identifies patterns and develops certain key influencers when reaching the existing answers. Afterwards, it will be tested and used on data it has never seen before but structured in a similar way.
If programming is 1+1 = 2 and the machine can’t do anything other than 1+1=2, machine learning is the computer learning that 1 + 1 = 2, also means 2 + 2 = 4.
Why Machine Learning Matters:
All businesses depend on data to function. Making decisions based on data-driven insights might mean the difference between staying competitive or falling further behind. In order to harness the value of corporate and customer data and make decisions that keep a business ahead of the competition, machine learning may be the answer.
As the general population embraces the advent of modern technology, we use the said technology to solve many problems previously thought unsolvable either by humans or by machines. With the help of Machine Learning, we are able to write essays easier, code with automatic writing assistance, get our favorite genre of movies recommended as well as music, have less traffic, get deals exclusively tailored towards us and more.
Machine Learning Use Cases
Labeling thousands of images manually by humans is difficult, so we develop Machine Learning models to do this task automatically by first labelling 100 pictures manually, only to let the machine label 1,000 pictures automatically in a moment. The success rate may be worse or better than if the task was done by humans, but that depends heavily on the circumstances.
Machine Learning can be applied to a variety of real life, practical problems across many industries, improving our quality of life daily.
- Traffic: Predictive traffic control, dynamic road management
- Retail: Dynamic pricing, inventory procurement, supplier evaluation
- Hospital and medical sector: Likelihood of disease, diagnosis, recommended treatments
- Energy: Supply optimization, demand prediction during peak months
- Art and creatives: Image generation, open-ended creative writing et cetera.
- More and more.
The steps of a simple machine learning project looks like below:
- Procure the data
- Explore, analyze and clean the data
- Decide on the appropriate Machine Learning model
- Split data into two sets, training and test
- Feed data into Machine Learning model
- Get base results
- Re-tune the Machine Learning model or try different models or boost for optimal results
- Export and save model
- Predict unseen data using the exported model
There are various types of machine learning models available based on different business goals and data sets.
Classification of Machine Learning
There are three types of learning in Machine Learning:
- Supervised which can be used to let the machine understand the difference between a tree and a car, to classify them into their own respective categories or forecast next month’s inventory for a grocery store.
- Unsupervised which can be used to recognize and group data points in a data that would normally be impossible for humans to manually analyze and make sense of.
- Reinforcement learning which lets the machine understand the repetition of tasks have a certain goal by instilling in a reward and punishment system, leading to a literally perfect gameplay or replication of solution, due to the machine teaching itself.
What is a Machine Learning Model?
You probably have heard of ‘models’ when any article or media contains the subject of Machine Learning.
What are we exactly referring to when we talk about ‘models’?
A Machine Learning model is a file.
It is a file that contains the model.
The model is a trained Machine Learning algorithm.
This model is one of the many types of Machine Learning algorithms that has processed a certain amount of data and contains an adjusted settings tailored towards processing any other info that gets fed into it to provide the best solution it has learned from previously trained data.
For example, a developer decides to make a Machine Learning model to classify a giant folder of digital images depicting a goat and a sheep.
The folder contains 1000 images of sheep and 1000 images of goat, mixed together. Their goal is to produce 2 folders that contain only images of either goat or sheep.
So they decide to develop a Machine Learning model.
- They will first locate and identify 10 images of sheep and 10 images of goat.
- They will then decide on which Machine Learning algorithm to use (in this case K-means or ISODATA)
- They will then translate the image into a form readable for the algorithm.
- They will input the translated data into the algorithm with the appropriate labels.
- The machine will run the algorithm over this data, and adjust its parameters to match the labelling.
- The model will finish training.
- The developer will then test out the data on test data to evaluate the accuracy of the model.
- If the results are acceptable, then the model will be exported (most likely into a pickle [pkl] file) and if not, the developer can increase the number of images for the model to train on or finetune the settings of the algorithm.
- Once the model is exported, it can be used on additional images to determine whether the image contains a goat or a sheep.
- The developer inputs all the images into the model to get labelling for each one of them.
- The task is completed.
What is a Machine Learning Algorithm?
An algorithm for Machine Learning is a mathematical function geared towards recognizing patterns in given data.
There are many types of algorithms but the most notables ones are named below:
- Linear regression
- Logistic regression
- Decision trees
- K-means Clustering
- Support Vector Machine
- Random Forest
Machine Learning Models For Specific Tasks
Since Machine Learning can be considered a general topic, there are specifics involved once we get into the practical side of the subject.
There are a number of families of machine learning that are tailored towards achieving accurate automation in different subjects.
|Classification or regression
|Forecasting sales, differentiating between different animals
|Breaking down data, predicting outcomes
|Detecting fraudulent transactions based on user data, analyzing user sentiment for media
|Computer generated images, writing, audio transcription
If we take deep learning for example, Tesla’s new ‘smart cars’ are driven automatically by a deep learning model, specifically by a CNN (convolutional neural network) model to drive autonomously.
If we take the medical industry for example, many technologically advanced hospitals use CNN models to identify and diagnose tumors or diseases.
Additionally, RNNs (recurrent convolutional network) can be experienced in your daily life, from YouTube’s automatic subtitles and Apple’s Siri.
Developing a Machine Learning model from scratch is difficult, very difficult.
Which is why pretrained models exist.
If you wanted a personal mug for your coffee, you can just add paint or other aesthetic decorations on it instead of literally churning clay powder to make ceramic for 15 hours.
Similar to that, there are many models that have already been trained on extremely large datasets.
Pretrained models are as it describes, a pretrained Machine Learning model that was already trained on a huge datasets. They can be used by other individuals to ‘finetune’ towards their specific task.
If we take GPT-3 (Generative Pre-trained Transformer 3) for example, a laboratory with tremendous budget trained that model, costing them humongous amounts of money. Now, this model can handle any and all types of texts. It is general use.
For other businesses and individuals, this model can now be molded (finetuned) towards a specific task such as writing only high quality essays or generating code.
The potential application cases for machine learning are quite varied. Businesses’ success is demonstrated by the fact that they are starting the process of digital transformation by relying on Machine Learning’s capacity to automate workflows and create solutions. There may be no end to what machine learning can assist us within the near future with dependable insights and automated knowledge.
In the endeavor of automating our daily life as well as crucial machine-solvable problems, we have produced many large models that are geared towards the particular issue.
With the development of these models, general features such as automatic subtitles, dynamic translations, hyper-personalization (computer framework personalized just for you), automatic media manipulation, AI assistants, art and music generation and more become possible as time goes on.
From these models, many specific and niche issues are being solved such as a certain type of art style, code optimization for a personal website, writing hand written essays based on user’s writing et cetera.
Frequently Asked Questions
What is Machine Learning?
Models can anticipate outcomes correctly with Machine Learning (ML), a type of artificial intelligence (AI), without needing to be explicitly told to do so. Machine Learning algorithms employ previous data as input to successfully and accurately forecast values.
A machine can learn that an image of red car is different from an image of a blue car.
What is a Machine Learning Model?
A program that uses a dataset that has never been seen before to detect patterns or make choices is known as a machine learning model. A machine learning model can be trained to recognize items in images, such as vehicles or dogs.
Such tasks can be accomplished by a machine learning model that has been “trained” on a sizable dataset. Depending on the job, the machine learning algorithm is tuned during training to find particular patterns or outputs from the dataset. A machine learning model is the result of this process, which is frequently a program with certain rules and data structures specifically tuned towards solving the data it was trained on.
What is a Machine Learning Algorithm?
An algorithm for machine learning is a mathematical way to look for patterns in a set of data. Algorithms used in machine learning frequently use concepts from statistics, mathematics, and linear algebra.
The machine learning algorithms such as linear regression, decision trees, SVMs and XGBoost are some well-known examples.