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Machine learning (ML) is a subfield of Artificial Intelligence (AI), which gives machines the ability to learn and adapt from their experience and enhance their ability to complete any specific tasks assigned to them. The most prevailing example of machine learning is the “people who bought XXX also saw XXXX”, which you must have seen across all e-commerce stores.
The term machine learning was coined by Arthur Samuel in the year 1959. Arthur Samuel is also considered as the pioneer of Artificial Intelligence. According to him, machine learning is a field of study that enables computers to adapt and learn for themselves without any explicit need for programming. Obviously, this was the beginning of what we are seeing today, but machine learning as we know it today is pretty much what Arthur defined way back in the 1950s.
In this blog, we will be covering all aspects of machine learning, including the working of machine learning and the machine learning process steps. We will also be looking at how does machine learning works in today’s world, as well as define some of the popular machine learning techniques used widely in different industries. Last but not least, we will also be looking at the best programming languages for machine learning, while finally rounding up our blog by summarizing the working of machine learning.
As said earlier, machine learning is a subfield of artificial intelligence. In the most basic terms, the machine learning algorithms are meant to create intelligent programs that are able to get trained for specific tasks by themselves and learn better ways to complete the tasks faster and with precision. It’s kind of similar to creating algorithms that replicate the human mind, with the ability to learn, adapt, and make intelligent decisions. Such algorithms are developed to minimize the need for human intervention. For the most part, the learning process of the machine is completed through supervised or unsupervised training with a large volume of the training dataset. The quality and volume of the data used to train machines are directly related to the precision of the machine learning models. Machine learning models can be developed for explicit tasks, where automation is desired.
Model, parameters, and the learners are three fundamental blocks of any machine learning model.
The model represents the system that is responsible for making forecasts/predictions.
Parameters are the characteristics that are considered by the model to make forecasts/predictions.
The learner component of a machine learning model enables models to learn and make necessary adjustments required to make accurate forecasts/predictions.
Now, to help you better understand how machine learning works, we can consider an example. In this scenario, let’s consider that you have two different drinks: beer and wine. Now, you want machine learning algorithms to differentiate between the two drinks based on some fixed parameters, which in this case would be the color and percentage of alcohol found in each drink.
Here are the machine learning process steps that will complete the given task for you:
Learning from the training set
The first and foremost step for any machine learning model is to feed the model with a structured and large volume of data for training. In this case, you would require several (possibly hundreds) samples of beer and wine with defined color and alcohol percentage. Now, you will feed the training data into the model and classify each of the samples according to their defined parameters. For instance, you would define the percentage of alcohol in samples of wine against the percentage of alcohol in samples of beer. Similarly, you will classify the other defined parameter which is ‘color,’ for samples of wine and beet.
For easy representation, you may define the ‘color’ as parameter ‘X’ and alcohol percentage as parameter ‘Y’. Now in this case the (X, Y) will be the defined parameters of the training dataset for the model and will help the algorithm to adapt and learn differentiation between each of the drinks.
Measuring Error
The next logical step in our “how does machine learning work” is to measure the errors and discrepancies in the results of the model. For this step, you will input a fresh dataset (different from the training data) and the outcome of this step could be either one of these four:
- True Positive: This is the ideal scenario where the machine learning model can predict each type of drink correctly
True Negative: In this scenario, the model misses out on the classification of drinks when it is present.
False Positive: In this scenario, the model wrongly classifies drinks when it isn’t present.
False Negative: In this scenario, the machine learning model isn’t able to classify drinks when it is present.
To estimate the total error from the model, you will use the sum of FP and FN
Manage Noise
Now, to help you understand “how does the machine learning work” better, we have kept the set of defined parameters limited to only two. However, in real-world scenarios, there may be hundreds and thousands of parameters that have to be defined in the training data to enable machine learning models to classify the items precisely.
In such scenarios, there will be a large number of errors estimated in the second step. These errors will be sourced from the noise present in the training data. Noise in this case represents the unwanted anomalies that deviate from the standards of the defined parameters; thus weakening the learning process of the model. The noise in training data may be present for various reasons, including:
Large volume of the training data.
Unstructured training data.
Data labeling errors.
Overlooked attributes that influence the classification.
Testing and Generalization
In many cases, the machine learning algorithm fits perfectly with training data; however, it fails to produce results when a fresh dataset is an input to the model (other than the training data). This is why it is important to evaluate the fitness of the algorithm to the new/fresh dataset. This is achieved by giving new/fresh data as input and analyzing the results produced by the algorithm. The generalization in this case refers to the fitness of the model to make predictions for a fresh dataset.
Supervised machine learning is one of the popular machine learning techniques. In this case, the model takes training data with known responses to the output to learn and build its capacity to make predictions for a new/fresh dataset. Supervised machine learning techniques should be used where users have a high volume of structured data for training the model. In this case, the higher the volume and quality of the data, the better and more precise results can be expected from machine learning models. The supervised learning model makes prediction models based on regression and classification techniques.
Classification techniques
The classification technique used by supervised machine learning models delivers discrete responses. For example, the model will simply inform if an email is spam or genuine (you experience it in your email inbox). In classification techniques, the input data is classified into the defined categories. This technique is widely used in medical imaging, image processes, and speech recognition.
You can use a classification technique if you have structured, tagged, or categorized data which is divided into discrete classes or groups. For instance, the technique is used in handwriting recognition, where it is capable of classifying the handwriting based on the recognition of letters and numbers style. Unsupervised pattern recognition is widely used in image processing and computer vision to identify objects and image segmentation.
Some of the widely used supervised learning algorithms in the industry include Neural networks, support vector machine (SVM), K-nearest neighbor, logistic regression, and more.
Regression techniques
The regression techniques forecast continuous responses. An example of these includes predicting the temperature changes or fluctuations in power demand. The regression techniques are used in algorithmic trading, energy load forecasting, among others.
The regression techniques for classification can be used when the input data is in the form of a continuous range, or real numbers. Some of the regression algorithms include stepwise regression, linear regression models, non-linear regression models, adaptive neuro-fuzzy learning, and others.
Unsupervised machine learning is used to identify hidden patterns and structures in data to draw inferences.
Clustering
Clustering is one of the most widely used unsupervised learning techniques, which is used for exploratory data analysis to identify hidden patterns and structures in data. The technique is used in different applications including gene sequence analytics, object recognition, image processing, and others.
An example case study for the clustering technique is identifying the optimum location of building a cell phone tower for a telecommunication company. In this case, the unsupervised machine learning algorithm can be used to identify clusters of users in different areas who rely on cell phone towers. Since a cell phone may only be connected to a single tower at a time, the clustering algorithm can process the dataset and come up with the most suitable cell tower placement design to optimize signal reception for users.
Some of the common algorithms used to perform clustering include: fuzzy c-means clustering, K-means, Gaussian mixture model, and others.
Now that we are over the machine learning process steps and working of machine learning, let’s see which the best programming language for machine learning.
This is rather a simple question, as Python is undoubtedly the most suited programming language that is also the most widely used to develop machine learning applications. We will come to the advantages and strengths of Python as the best programming language for machine learning algorithms a little later. Some of the other popular languages that are used to develop machine learning applications include R, C++, Shell, Java, and others.
What makes Python the ideal choice for machine learning application development is the simplicity and readability of the language, as compared to other languages. This is important because developing machine learning algorithms is already inherently difficult with various complex concepts like calculus and others involved in the process, which require a lot of time and effort to develop. The simplistic Python language can share some burden of the developers and ML engineers. There are various Python tutorials freely available online that can be checked to understand the basics of the language. Another great advantage of the Python programming language for use in machine learning algorithms is its various pre-built libraries for machine learning. The language comes with various packages that can be used directly for various applications. Some of the Python packages widely used in machine learning applications development include:
Numpy, Scikit (used widely for image processing)
Numpy, Scikit, and NLTK (great when working with text)
Librosa (Audio applications)
Matplotlib, Scikit, and Seaborn (used for data representation)
TensorFlow, Pytorch (used for deep learning applications)
Pandas (used for high-level structured data analysis)
The language gives ML engineers and developers an option to choose between scripting or object-oriented programming. Moreover, the changes can be easily implemented without having to recompile the code.
Folio3 is one of the leading machine learning application development companies in the world. With decades of experience in the development of high-tech and complex machine learning applications, Folio3 has partnered with some of the biggest companies from different industries and help them to initiate their digitalization process with robust customized solutions with advanced machine learning capabilities.
Breast Cancer HER2 Subtype Identification
Folio3 partnered with one of the oldest public sector medical universities and hospital in Pakistan, Dow University of Health Sciences to develop a robust computer-aided assistance system with cell segmentation and spot counting capabilities to enable medical practitioners and doctors to perform faster and more precise breast cancer tests. The powerful computer-aided system was able to digitize and store the images for further analysis and processing.
Converse Smartly®- Speech to Text Software
Converse Smartly (CS) is a hugely successful in-house project of the company that was developed to establish the capabilities and expertise of Folio3 ML engineers in the fields of machine learning and Natural Language Processing.
Converse Smartly (CS) is an advance and robust speech-to-text application that uses state-of-the-art technologies, including IBM Watson API, Amazon AWS, Microsoft Azure, Python’s Natural Language Toolkit, and Google Speech-to-Text API to deliver precise and outstanding results.
ATM Cash Forecasting
For this project, Folio3 partnered with one of the largest commercial banks in Pakistan, which is also a multinational bank, to develop an ATM cash forecasting system. The prediction model was able to forecast the cash-flow management for over 2000 ATMs of the bank in Pakistan and globally. Based on the unique requirements of the bank, we developed a sophisticated and innovative predictive solution that increased the ATM management profits of the bank by up to 6%.
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Google Assistant is a personal assistant that leverages image recognition, NLP, and Google's knowledge graph to converse with users. It’s much like a personalized chatbot that uses natural language processing to interact with the users and come up with the answers to users’ questions.
Machine learning is a subfield of a much broader Artificial Intelligence (AI) technology, which is meant to enable machines to execute tasks smartly. Machine Learning on its own is about developing intelligent algorithms for devices that can learn, adapt and execute tasks through their learned experiences.
Machine learning is a branch of AI where computers learn patterns from data to make predictions or decisions without being explicitly programmed.
Supervised, unsupervised, and reinforcement learning are each defined by how the model learns from data and feedback.
Popular algorithms include linear regression, decision trees, random forests, k-nearest neighbors, support vector machines, and neural networks.
Basic knowledge of Python or R is helpful, but many beginner-friendly tools and platforms let you start with minimal coding.
Examples include email spam detection, recommendation engines, fraud detection, speech recognition, and self-driving vehicles.
