How can Facebook recognize your face in photos? How does Netflix predict what people will enjoy watching? The answer lies in machine learning algorithms (statisticians might argue this is the same as statistics, just done by computers) and neural networks.
Machine learning and neural networks play an important role in many aspects of our lives, including online search engines, social media, e-commerce, and transportation.
Some real-world applications of machine learning include:
Personalized recommendations on websites and apps, such as Netflix or Amazon, based on users' browsing and purchase history
Spam filtering in email systems
Fraud detection in financial transactions
Image and speech recognition in artificial intelligence assistants, such as Siri or Alexa
Predictive maintenance in manufacturing and other industries to identify potential equipment failures before they occur
Video Surveillance
A few examples of how neural networks are used include:
Image and video recognition in self-driving cars
Facial recognition for identification
Natural language processing (NLP) in language translation and chatbots
Drug discovery and development in the pharmaceutical industry
Weather forecasting and climate modeling
Forecasting stock prices and predicting market trends
Now that we've established that machine learning and neural networks play an integral role in our daily lives, let's take a closer look at what they are and how they function.
What is Machine Learning?
Machine Learning is a branch of artificial intelligence (AI) that uses algorithms and statistical models to teach the computer to learn from data and then make predictions or decisions.
The purpose of machine learning is to enable computers to learn and adapt on their own to enhance their performance and decision-making, without any explicit programming or help from humans.
Steps in a Machine Learning Algorithm
The entire machine learning algorithm can be broken down into the seven stages:
Gathering Data: The first and maybe the most important step in any machine learning process is amassing a sufficient amount of good quality data to train and test our model. This matters greatly since the data you collect directly dictates the efficacy of the prediction model.
Preparing Data: The data is then cleaned and prepared, so that it produces unbiased results.
Choosing a Model: Depending on the type of problem, an appropriate model is selected for testing and training. Some examples of available models include decision trees, linear regression, and neural networks.
Training: The model is next trained to recognize the correct patterns in the data.
Evaluation: The model is then checked on testing data to verify its efficacy and accuracy.
Hyperparameter Tuning: A lot of adjustments are then made to the parameters of the model to increase accuracy and optimize performance.
Prediction: If the model is effective, it is used to make predictions or decisions from new data points.
Relationship between Statistics and Machine Learning
Machine learning relies heavily on the statistical methods of collecting, analyzing, interpreting, and visualizing empirical data in order to discover hidden patterns. Statistics' foundational concepts such as population and sample, central tendency methods, type of distribution form the basis for all machine learning algorithms.
The relationship between machine learning and statistics can be seen in the use of statistical models and techniques in the development of machine learning algorithms. For example, linear regression is a statistical model that is often used in machine learning to predict the value of a continuous variable based on one or more independent variables. Similarly, decision trees, a machine learning technique, use statistical methods to determine the best decision or prediction based on data. Additionally, machine learning algorithms often rely on statistical concepts such as probability and statistical inference to make predictions or decisions based on data.
One could also argue that machine learning is a result of statistics. The data accumulated at such a fast pace and suddenly became so complex that some models provided by traditional statistics could not handle them. As a result, so-called non-parametric approaches, which do not assume that the population distribution is normal but are open to the structure behind the data, were needed, which could provide estimates like means or very complex regression models.
Here are some other specific ways in which statistics is used in machine learning:
Model evaluation: Statistics is used to measure the accuracy of the predictions or decisions made by the machine learning models. This can involve calculating metrics such as accuracy, precision, and recall, which provide information about the number of correct and incorrect predictions made by the model
Model selection: Statistics is also used to compare the performance of different machine learning models and select the best one for a given task. This may involve using statistical tests to determine whether the difference in performance between two models is statistically significant.
Feature selection: Statistics is further used to identify the most relevant and predictive features (independent variables or inputs) for a machine learning model, by analyzing the relationships between the features and the target variable (the value the machine learning model has to predict).
Data preprocessing: In addition, Statistics is used to find and handle missing or incorrect values in the data, remove outliers and normalize the data so that it is in a suitable format for the machine learning model.
What are Neural Networks?
A neural networks is a specific kind of machine learning algorithm that takes its cues from the way the human brain works. It is made up of interconnected artificial "neurons" (data processors), and it can be taught to recognize patterns in data and make decisions based on that analysis.
There are three layers in a simple neural network: the input layer, the output layer, and the hidden layer.
A deep learning network is defined as one that has more than one hidden layer.
How do Neural Networks work?
Here is a general overview of how neural networks work:
1. Input data is passed through the input layer of the neural network.
2.The hidden layers of neurons transform the data before passing it on to the next layer. The more layers there are, the more complicated information can be recognized based on what was learned in earlier layers.
3. The last hidden layer's output is sent to the output layer, where the final neural network prediction or decision is generated.
4. In order to minimize the error between the predicted and actual outputs, the weights (connections) between the neurons are fine-tuned during the training process.
5. The neural network is put to the test on new, unseen data to evaluate its performance.
Neural networks helps us save a ton of time when working with vast, complex datasets. They can recognize patterns we humans might miss. As technology continues to advance and data becomes increasingly complex, the importance of neural networks will only continue to grow.
Key Takeaways:
Machine learning and neural networks have grown ubiquitous in many facets of modern life, from internet search and social media to online shopping and transportation.
Machine learning uses algorithms and statistical models to train computers to learn from data and make predictions or decisions based on their own analysis.
Neural networks are a type of machine learning algorithm that mimic the structure and function of the human brain to perform tasks like image and speech recognition, natural language processing, and predictive modeling.
The employment of statistical models and methods in the creation of machine learning algorithms demonstrates the close connectivity between the two fields.
References
CrashCourse. (2017, November 1). Machine Learning & Artificial Intelligence: Crash Course Computer Science #34 [Video]. YouTube. https://youtu.be/z-EtmaFJieY
Google Cloud Tech. (2017, August 31). The 7 steps of machine learning [Video]. YouTube. https://youtu.be/nKW8Ndu7Mjw
IBE Munich. (2022, February 26). Traditional Statistics vs Machine Learning [Video]. YouTube. https://youtu.be/ObbTLep4Hxo
What is Machine Learning? https://www.ibm.com/topics/machine-learning
What is a Neural Network? https://www.mastersindatascience.org/learning/what-is-ai/neural-network/