In this Artificial Intelligence tutorial, we shall be covering Machine Learning, Deep Learning, neural networks, real-life applications of Artificial Intelligence, Python and various packages available in it, TensorFlow, Keras, multilayer perceptron, convolution neural networks, recurrent neural networks, long short-term memory, OpenCV, and much more.
Overview of Artificial Intelligence
Today, a few applications of Artificial Intelligence seem to bring us closer to the future. The most convincing pieces of evidence are self-driving cars, Google Translate, and Sophia (humanoid robots). Moreover, have you ever wondered how Cyborg technology (a technique for creating artificial body parts for people with disabilities that enables them to function normally) works? If yes, this Artificial Intelligence tutorial will give you an introduction to AI right from the basics.
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What are the goals of Artificial Intelligence?
Creativity and ideas never end as they are limitless. Likewise, there are a lot more things to create, improve, implement, and invent in the field of Artificial Intelligence. AI is far from reaching its saturation level of creating new things.
In short, here are the goals of Artificial Intelligence:
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- Create machines that can replicate human intelligence
- Improve machine efficiency and accuracy
- Develop tools to help people solve real-world problems, e.g., robotics for people with disabilities, auto-driving cars to avoid accidents caused by human error, etc.
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What is Artificial Intelligence?
Artificial Intelligence is the intelligence that machines demonstrate. It allows us to create machines that can perform multiple tasks and solve real problems without error. AI can improve efficiency and productivity by automating repetitive tasks. Additionally, it can create an immersive and responsive experience and understand human emotions.
This Artificial Intelligence tutorial will teach you all the AI techniques that you can implement in real life.
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Applications of Artificial Intelligence
Artificial Intelligence machines can make decisions, and when exposed to large amounts of real-world data, they try to learn and improve themselves. To illustrate this, here are some practical applications of Artificial Intelligence:
- Self-driving cars: Tesla’s famous self-driving cars are a magnificent real-life application of Artificial Intelligence. These cars have built-in IoT sensors for image recognition, forehead collision, spot monitoring, and many more complex mechanisms that allow them to navigate and work in real life.
- Google Translate: Google Translate is another great application of Artificial Intelligence. It helps us translate sentences formed in one language to another. It can also translate the entire text on websites, which is possible only because of Artificial Intelligence.
- Amazon’s Alexa: Alexa includes a speech recognition system that listens to our voice commands and gives answers. It recognizes our voice and interprets it as a series of commands and then returns the results to us. It uses AVS (Alexa Voice Service), which Amazon provides free of cost.
- Google Maps: Today, without Google Maps, it is impossible to survive in the city. With Google Maps, we can travel from one place to another without any difficulty. All we have to do is open Google Maps and enter our location. Then, its navigation will lead us with the most optimized path to our destination. This is also one of the wonderful applications of Artificial Intelligence.
Now, in this Artificial Intelligence tutorial, we will head on to learn about the subsets of Artificial Intelligence.
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Subsets of Artificial Intelligence
Artificial Intelligence is an umbrella term. There are two subsets of Artificial Intelligence: Machine Learning and Deep Learning.
Machine Learning
Machine Learning is a branch of Artificial Intelligence, in which a program or machine uses a set of algorithms to find patterns in the dataset(s). Above all, we don’t have to write individual instructions for every action. As Machine Learning models capture more and more data, they become smarter and self-improving.
Further, Machine Learning can be sub-categorized into three subsets:
- Supervised Machine Learning
- Unsupervised Machine Learning
- Reinforcement Learning
Now, moving ahead with this Artificial Intelligence tutorial, we will look at some applications of Machine Learning.
Applications of Machine Learning
- Amazon recommendation system: Amazon’s recommendation system works on Machine Learning algorithms. To find patterns and similarities in our searches and to recommend similar search results, it leverages its unique design.
- Machine Learning used in fraud detection: Today, as the number of fraud cases increases, Machine Learning plays an important role in solving this problem. Fraud detection algorithms help distinguish between authorized and fraudulent transactions.
- Machine Learning used in social media: Facebook uses Machine Learning algorithms to suggest friends to us with its ‘People You May Know’ feature. Also, it uses Machine Learning for face detection, i.e., recognizing faces in a group photo. Isn’t it a wonderful application of Machine Learning?
Since Artificial Intelligence and Machine Learning make our lives better, it is very satisfying to learn these. This Artificial Intelligence tutorial will teach you everything you need to know about the basics of Artificial Intelligence.
Deep Learning
Further developments in Machine Learning have led to a different sub-category, i.e., Deep Learning. Deep Learning makes use of artificial neural networks that consist of layers of networks working on different parameters to give the desired output.
Applications of Deep Learning
- Predicting earthquakes: This is one of the most important applications where the field of Deep Learning is playing a key role. Unquestionably, time is an important factor when it comes to the prediction of earthquakes. Fortunately, with Deep Learning, we are now able to boost the computation time by almost 50,000 percent.
- Adding sounds to silent movies: This is a fascinating application for Deep Learning. The idea behind this Deep Learning model is to produce sounds that exactly match a silent video. The DL models are provided with thousands of videos and audio files with which they try to learn by separating video frames for different categories of sounds. Thus, when a new video is given to them for adding audio to it, they use this learning for prediction. Also, they use convolutional neural networks, recurrent neural networks, and LSTM (long short-term memory) for achieving their successful implementation.
- Netflix recommendation system: People using Netflix are familiar with its recommendation system. It uses Deep Learning for recording the responses of different kinds of audiences. The Deep Learning models are designed in such a way that they record the history of watching, time of watching, and our show preferences to recommend shows. It saves a lot of human effort.
AI vs ML vs DL
The most common doubt is AI vs ML vs DL. In simple terms, Deep Learning is a subset of Machine Learning, which in turn is a part of Artificial Intelligence. Artificial Intelligence acts as an umbrella, under which Machine Learning and Deep Learning exist.
Although Machine Learning and Deep Learning are both a subset of Artificial Intelligence and are focused to achieve the same goal (thinking like humans), the approach to doing so is different for each discipline.
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Below are the key points that differentiate between Artificial intelligence, Machine Learning, and Deep Learning:
| Artificial Intelligence |
Machine Learning |
Deep Learning |
| AI is a set of techniques and processes for machines to imitate human behavior. Both ML and DL are used to develop an AI application. |
Machine Learning is a subspace of AI and is used to process large amounts of data and predict future events. |
Deep Learning uses neural networks, where a bunch of nodes or artificial neurons try to solve a problem in ways similar to that of biological neurons. |
| AI systems replicate human behavior and solve problems like missionaries and cannibals, the Bayes theorem, the shortest path, etc. |
Machine Learning inherits symbolic approaches from AI and uses models, statistics, and probability theories to make data-driven decisions. |
Deep Learning provides a multi-level abstraction, which makes it easier to train models without depending on specific algorithms. |
| AI requires high-end computational devices to simulate the desired output. |
ML can work with smaller datasets, thereby reducing the need for high-end graphics and computer systems. |
DL requires high-end systems as the performance of the models highly depends on the amount of data fed in. |
| AI takes a long time to train a machine and make it capable enough to produce impeccable results. |
ML takes much less time to train, ranging from few hours to a day. |
DL models a take longer time than ML ones as they have so many parameters to take into consideration. |
| Interpretability depends on implementation methods used to solve a particular problem. |
It is easy to interpret the final results as ML uses decision trees that have rules for what and how it chooses. |
In Deep Learning, it is hard to interpret the final result. |
Artificial Intelligence is about incorporating human abilities into a machine by designing algorithms such that these algorithms involve self-learning and provide the machine with the ability to think like the way humans do. With this, the machine would be able to solve problems without explicit human inputs. However, a lot of creativity and computation is required to make it successful.
Artificial Intelligence is gaining immense popularity because it has a lot of things to explore and create around the world. The applications of Artificial Intelligence that we come across in our day-to-day life are just a small demonstration of AI’s wonder-struck start.
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Recommended Audience
This Artificial Intelligence tutorial has been prepared to help you learn Artificial Intelligence the right way, and it is meant for beginners and professionals to help them understand basic-to-advanced concepts related to AI. This Artificial Intelligence tutorial will help you master AI, with which you will be able to take yourself to a higher level of expertise for implementing Artificial Intelligence concepts in real life.
Prerequisites
Before going through this Artificial Intelligence tutorial, you should have a fundamental knowledge of the field of information technology, be familiar with computers and the Internet, and have a basic working knowledge of data. Such basics will help you understand the AI concepts better and will let you move faster on your learning path.
This AI tutorial further covers an introduction to AI, its history and goals, applications of AI, AI vs ML vs DL, various Data Science packages, artificial neural networks, backpropagation algorithm, multilayer perceptron, problems of overfitting and underfitting, convolutional neural networks, recurrent neural networks, long short-term memory, various Machine Learning concepts, and OpenCV.
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Popular AI Algorithms
Backpropagation Algorithm
It is a supervised learning algorithm used to train multi-layered perceptrons. The backpropagation algorithm uses a technique called the gradient descent or delta rule to find out the minimum values of the error function in the weights, random variables, or any random values for the fact.
The algorithm finds out the weights that minimize the error function and considers them to be the solution to the learning problem. Unlike the direct computation of the gradient with individual weights, backpropagation computes the gradient of loss function concerning the gradient of the network for better performance.
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Naive Bayes Classification
It is a classification algorithm that utilizes the Bayes’ theorem to check whether an input belongs to the given list of classes or not. For each class, there is a set of probabilities, which the algorithm updates after the data is fed in. Then, it forms a posterior probability and tells if the given input belongs to a class or not.
Once the data is fed in, the algorithm converts that into a frequency table and calculates the likelihood table by finding the probabilities. From there, the Bayesian equation is used to calculate the posterior probability for each class. The outcome of the prediction will be the class with the highest posterior probability.
Decision Trees
Decision trees fall under the category of supervised learning and can be used to solve classification and regression problems. A decision tree has a root node, decision node, and leaf nodes. Decision nodes have multiple branches and are used to make decisions based on some conditions.
The algorithm divides a complex problem into simple questions assigned to every single node. On each node, a decision is made, and the tree gets divided into more branches. If there is a dead-end on a leaf node, the algorithm performs backtracking and starts from the point where it made the right decision.
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KNN Algorithm
The k-near neighbor algorithm is used on both regression and classification models. Based on supervised learning, it is a simple Machine Learning algorithm that assumes similar data points near each other.
After loading data and initializing the chosen number of neighbors (k), the algorithm calculates the distance between the current neighbors and the query neighbors from the data. Distance between each neighbor is indexed to the ordered collection and sorted in ascending order. These sorted collections are then picked and labeled to either find the mean for regression or the mode for classification.
Linear Regression
It is a Machine Learning algorithm generally used to find the relationship between variables and forecasting. It predicts the value of a dependent variable based on the value of an independent variable.
A regression line has a straight line equation in the form of Y=a+bX, where Y is the dependent variable and X is an independent or explanatory variable. The intercept of the line is a, and the slope of the line is b.
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