Introduction of Artificial Intelligence and Machine Learning By the end of this lesson you will be able to Define artificial intelligence Describe the relationship between artificial intelligence and data science Define machine learning Describe the relationship between machine learning, artificial intelligence, and data science. Describe different machine learning approaches. Identify the applications of machine learning.
Let's understand how the field of artificial intelligence emerged. Let's first understand the reason behind the emergence of AI. Data economy is one of the factors behind the emergence of AI.
It refers to how much data has grown over the past few years and how much more it can grow in the coming years. When you look at this graph, you can clearly understand how the volume of data has grown. You can see that since 2009, the data volume has increased by 44 times with the help of social websites.
The explosion of data has given rise to a new economy, and there is a constant battle for ownership of data between companies. to derive benefits from it. Now that you know that data has grown at a rapid pace in the past few years and is going to continue to grow, let's understand the need for AI.
As you know, the increase in data volume has given rise to big data, which helps manage huge amounts of data. Data science helps analyze that data. So the science associated with data is going toward a new paradigm. where one can teach machines to learn from data and drive a variety of useful insights giving rise to artificial intelligence. Now, you may ask, what is artificial intelligence?
Artificial intelligence refers to the intelligence displayed by machines that simulates human and animal intelligence. It involves intelligence agents, the autonomous entities that perceive their environment and take actions that maximize their chances of success at a given goal. Artificial intelligence is a technique that enables computers to mimic human intelligence using logic. It is a program that can sense, reason, and act.
Let's look at some of the areas where artificial intelligence is used. Artificial intelligence is redefining industries by providing greater personalization to users and automating processes. One example of artificial intelligence in practice is self-driving cars. Self-driving cars are computer-controlled cars that drive themselves. In these cars, human drivers are never required to take control to safely operate the vehicle.
These cars are also known as autonomous or driverless cars. Let's see how Apple uses AI. iPhone users can experience the power of Siri, the voice.
It simplifies navigating through your iPhone as it listens to your voice commands to perform tasks. For instance, you can ask Siri to call your friend or to play music. Siri is fun and is extremely convenient to use. Another example is Google's AlphaGo, which is a computer program that plays the board game Go. It is the first computer program to defeat a world champion at the ancient Chinese game of Go.
Amazon Echo is another product. It's a home-controlled chatbot device that responds to humans according to what they are saying. It responds by playing music, movies, and more. If you've got compatible smart home devices, you can tell Echo to dim the lights or turn appliances on or off.
You can use AI and chess, and here is an example of a concierge robot from IBM called IBM Watson. The IBM Watson AI has typically been in the headlines for composing music, playing chess, and even cooking food. Let's move ahead and look at some sci-fi movies with the concept of artificial intelligence. The films featuring AI reflect the ever-changing spectrum of our emotions regarding the machines we have created.
Humans are fascinated by the concept of artificial intelligence and this is reflected in the wide range of movies on AI. Recommendations systems are used by a lot of e-commerce companies. Let's see how they work.
Amazon collects data from users and recommends the best product according to the user's buying or shopping pattern. For example, when you search for a specific product in the Amazon store and add it to your cart, Amazon recommends some relevant products based on your past shopping and searching pattern. So, before you buy the selected product, you get recommendations based on your interest, and there is a possibility that you may also buy the relevant product with the selected product.
If not, you have the chance to compare the selected product with the recommended products. Now, let's move ahead and understand the relationship between artificial intelligence, machine learning, and data science. Even though the terms artificial intelligence, AI, machine learning, and data science fall in the same domain and are connected to each other, they have their specific applications and meaning. Let's try to understand a little about each of these terms.
Artificial intelligence systems mimic or replicate human intelligence. Machine learning provides systems the ability to automatically learn and improve from the experiences without being explicitly programmed. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, artificial intelligence, and several other related disciplines. Let's look at the flow diagram and try to understand the relationship between AI, machine learning, and data science. Interestingly, ML is also an element of artificial intelligence.
So, the first step is data gathering and data transformation. This step basically comes under data science. Data transformation is the process of converting data from one format or structure into another format or structure.
Data transformation is important to activities, such as data management and data integration. After gathering data, we would want to use the data to make predictions. and derive insights.
In order to get predictions out of the data set, we use machine learning techniques such as supervised learning or unsupervised learning. On an overview level, supervised and unsupervised learning are the machine learning techniques used to extract predictions from a given data set. Now you must be thinking where deep learning comes into the picture.
Deep learning is a subfield of machine learning involved with algorithms. It uses artificial neural networks, which are modeled on the structure and performance of neurons in the human brain. Deep learning is most effective when there isn't a clear structure to the data that you can just exploit and build features around.
Now, the next step in the flow diagram is to get insights from predictions being made. In order to do so, you need to use data analysis, which actually is the process under data science. Now, when you are done with all of these, you must want your data to perform some actions.
This is where AI comes into the picture. Artificial intelligence combines predictions and insights to perform actions based on the human decision and automated decision. Now, let's move ahead and understand the relationship between artificial intelligence Machine Learning and Data Science Let's look at the relationship between artificial intelligence and machine learning. Artificial intelligence is the engineering of making intelligent machines and programs.
Machine learning provides systems the ability to learn from past experiences without being explicitly programmed. Machine learning allows machines to gain intelligence thereby enabling artificial intelligence. Let's now understand the relationship between machine learning and data science.
Data science and machine learning go hand in hand. Data science helps evaluate data for machine learning algorithms. Data science covers the whole spectrum of data processing, while machine learning has the algorithmic or statistical aspects. Data science is the use of statistical methods to find patterns in the data.
Statistical machine learning uses the same techniques as data science. Data science includes various techniques like statistical modeling, visualization, and pattern recognition. Machine learning focuses on developing algorithms from the data provided by making predictions. So, what is machine learning?
Machine learning is the capability of an artificial intelligence system to learn by extracting patterns from data. It usually delivers quicker, more accurate results to help you spot profitable opportunities or dangerous risks. Now, you must be curious to understand the features of machine learning.
Machine learning uses the data to detect patterns in a dataset and adjust program actions accordingly. Pattern detection can be defined as the classification of data based on knowledge already gained or on statistical information extracted from the patterns. It focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data by using a method called reinforcement learning.
It uses external feedback to teach the system to change its internal workings in order to guess better next time. It enables computers to find hidden insights using iterative algorithms without being explicitly programmed. Machine learning uses algorithms that learn from previous data to help produce reliable and repeatable decisions. It automates analytical model building using the statistical and machine learning algorithms that tease patterns and relationships from data and express them as mathematical equations.
Let's understand the different machine learning approaches. So, what is the actual difference between traditional programming and machine learning? In traditional programming, data and program is provided to the computer. It processes them and gives the output.
However, the machine learning approach is very different. In machine learning, algorithms are applied on the given data and output. The result of the applied algorithm and calculations is a learning model that helps machine to learn from the data. In traditional programming, you code the behavior of the program, but in machine learning, you leave a lot of that to the machine to learn from data. Now let's first understand the traditional programming approach.
Traditionally, you would hard-code the decision rules for a problem at hand, evaluate the results of the program, and if the results were satisfactory, the program would be deployed in production. If the results were not as expected, one would review the errors, change the program, and evaluate it again. This iterative process continues till one gets the expected result.
What is the machine learning approach? In the new machine learning approach, the decision rules are not hard-coded. The problem is solved by training a model with the training data in order to derive or learn an algorithm that best represents the relationship between the input and the output. This trained model is then evaluated against test data. If the results were satisfactory, the model would be deployed in production.
And if the results are not satisfactory, the training is repeated with some changes. Machine Learning Techniques Machine learning uses a number of theories and techniques from data science. Here are some machine learning techniques. Classification, Categorization Clustering, Trend Analysis, Anomaly Detection, Visualization and Decision Making. Let's look at these techniques.
Classification is a technique in which the computer program learns from the data input given to it and then uses this learning to classify new observations. Classification is used for predicting discrete responses. Classification is used when we are training a model to predict qualitative targets. Categorization is a technique of organizing data into categories for its most effective and efficient use. It makes free text searches faster and provides a better user experience.
Clustering is a technique of grouping a set of objects in such a way that objects in the same group are most similar to each other than to those in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them. Trend analysis is a technique aimed at projecting both current and future movement of events through the use of time series data analysis. It represents variations of low frequency in a time series, the high and medium frequency fluctuations being out. Anomaly detection is a technique to identify cases that are unusual within data that is seemingly homogenous.
Anomaly detection can be a key for solving intrusions by indicating a presence of intended or unintended induced attacks, defects, faults and so on. Visualization is a technique to present data in a pictorial or graphical format. It enables decision makers to see analytics presented visually. When data is shown in the form of pictures, it becomes easy for users to understand it. Decision making is a technique or skill that provides you with the ability to influence managerial decisions with data as evidence for those possibilities.
Now, I am sure you have a better understanding of the overview of machine learning. So, let's look at some real-time applications of machine learning. Artificial intelligence and machine learning are being increasingly used in various functions such as image processing, robotics, data mining, video games, text analysis and healthcare. Let's look at each of them in more details.
So what is image processing? It is a technique to convert an image into a digital format and perform some operations on it so as to induce an enhanced image or to extract some helpful information from it. Let's look at some of the examples of image processing. Facebook does automatic face tagging by recognizing a face from a previous user's tagged photos. Another example is optional character recognition which scans printed docs to digitize the text.
Self-driving cars are another big example of image processing. Autopilot is an optional drive system for Tesla cars. When autopilot is engaged, cars can self-steer, adjust speed, detect nearby obstacles, apply the brakes and park.
Now let's see how robotics uses machine learning. Robots are machines that can be used to do certain jobs. Some of the examples of robotics are where a humanoid robot can read the emotions of human beings or An industrial robot is used for assembling and manufacturing products. So, let's look at some real-time applications of machine learning. Let's see what data mining is.
It is the method of analyzing hidden patterns in data. Let's look at some of the applications of data mining. It is used for anomaly detection to detect credit card fraud and to determine which transactions vary from usual purchasing patterns. It is also used in market basket analysis, which is used to detect which items are often bought together.
It can be used for grouping where it classifies users based on their profiles. Machine learning is also applied in many video games in order to give predictions based on data. In a Pokémon GO battle, there is a lot of data to take into account to correctly predict the winner of a battle. And this is where machine learning becomes useful.
A machine learning classifier will predict the result of the match based on this data. Let's move on to one of the most popular applications of machine learning, which is text analysis. It is the automated process of obtaining information from text. One example of text analysis is spam filtering, which is used to detect spam in emails. Another example is sentimental analysis which is used for classifying an opinion as positive, negative or neutral.
It detects public sentiment in Twitter feed or filters customer complaints. It is also used for information extraction, such as extracting specific data address, keyword or entities. There are many applications of machine learning in the healthcare industry.
Identifying disease and diagnosis, drug discovery and manufacturing, medical imaging diagnosis, and so on. Some of the companies that use machine learning have revolutionized the healthcare industry are Google DeepMind Health, BioBeats, Health Fidelity, and Ginger.io.