Introduction to Data Science
In the modern world, using data as the fuel, Data Science drives different technologies toward automation. In simple words, Data Science is the study of data. Students study Data Science Tutorial in their baby steps to visualize and analyze data.
For analyzing and visualizing a huge amount of data, we use various statistical methods. So, we can clearly say that statistics is the heart of Data Science, Deep Learning, Artificial Intelligence, and Machine Learning.
Now, a question that pops up in our minds is, why do we need to study data?
To answer this question, let’s look at a real-life use case – Flipkart’s recommendation system.
Watch this video to understand the difference between Artificial Intelligence and Machine Learning:
How does Flipkart suggests products to us?
Well, the answer is it does that with the help of Data Science and Big Data, along with Machine Learning. When a user visits the website, Flipkart’s recommendation system marks and records the behavior and the movements of the visitor. Also, whenever the user clicks on a product, the system stores it, along with other user activities, in the database. In this way, the system records millions of events in a day!
Then, it uses collaborative filtering for suggesting products to customers who are likely to churn out for specific products.
For example, imagine that customer C1 likes products P1, P2, P3, and P4, and customer C2 likes P1, P2, P3, P4, and P5. Then, there is a possibility that the customer C1 would also like P5, and the machine would suggest P5 to C1.
Further, it also gathers and uses the data of ratings provided by buyers for a particular product.
Then, the recommendation engine combines and analyzes all this data with the help of tools available in Data Science. It uses statistical analysis for visualizing and understanding the behavior of data in a neat and clean manner.
After that, it feeds this structured data into the models that are built on top of Machine Learning.
Finally, the Machine Learning models recommend the products to the users that reflect on different websites. Also, the recommendation of the product can be seen in your Facebook accounts. This helps improve sales and increase business revenue. By this example, we can infer how crucial the data is and how the tools of Data Science pave way for the growth of any business.
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What is Artificial Intelligence?
Artificial Intelligence is the imposition of humans intelligence to robots or machines, especially computer systems. It involves a three-step process:
- Learning: The retrieval of data and building of rules for training machines
- Reasoning: Employing the constructed rules for reaching accurate results
- Self-correction: Applying algorithms and techniques to iteratively improve the performance and accuracy of the machines
Artificial Intelligence consists of various frameworks that can be employed for computations in deep neural networks.
Some of the popular frameworks are TensorFlow, Keras, PyTorch, Theano, Accord.NET, Spark MLlib, Scikit-learn, and Microsoft CNTK.
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What is the difference between Artificial Intelligence and Machine Learning?
‘Artificial Intelligence’ is a panoptic term under which Machine Learning and Deep Learning reside. The below diagram shows the categories of Artificial Intelligence by which we can easily visualize the connections and differences between AI and Machine Learning.
Let’s first understand the categories of Artificial Intelligence before we move on to discussing the types of Machine Learning and concluding the Artificial Intelligence vs Machine Learning discourse.
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Categories of Artificial Intelligence
Weak Artificial Intelligence: In weak AI, the reaction of a machine for a specific input is well-defined. Here, we create a set of rules for the machine. It is then bound to give responses according to those confined rules. The supervised Machine Learning technique comes under Weak Artificial Intelligence.
Example: If we instruct a coffee machine to provide a Cappuccino, then it will perform well-defined sequences of actions. It will not respond beyond the set of rules that we have pre-defined.
Strong Artificial Intelligence: In strong AI, the algorithms and instructions for a machine are designed such that they give the machine the ability to learn by itself from the given inputs and iteratively enhance accuracy by experience.
Example: Google Translate, Google Maps, Chatbots, recommendation engines, etc. are the real-life examples of strong Artificial Intelligence.
Now, we will look into Machine Learning and its types and will learn more about AI vs Machine Learning.
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Machine Learning and Its Types
Machine Learning is a fragment of Artificial Intelligence that involves modeling of algorithms; these algorithms inject abilities into a machine for performing distinct tasks without being denotatively programmed.
Before diving into the types of Machine Learning, if we talk about Machine Learning vs statistics, we would agree with the fact that visualization is not possible without performing accurate statistical analyses. With the help of statistics, we can draw useful insights from data and build effective Machine Learning models on top of it.
Types of Machine Learning:
1. Supervised Machine Learning: In supervised learning, the system consists of a designated dataset. It is labeled with parameters for the input and the output. So, as a fresh dataset arrives, the algorithms scrutinize the data and gives the exact output on the basis of the fixed parameters.
Example: Weather forecasting is done on the basis of some labeled factors such as humidity, wind, temperature, atmospheric pressure, and precipitation. It altogether gives a numeric value that predicts the weather condition, based on the above-mentioned factors.
Learn about the difference between three subsections of Machine Learning in this comparison blog on Supervised Learning vs Unsupervised Learning vs Reinforcement Learning.
2. Unsupervised Machine Learning: In unsupervised learning, there is no availability of labeled data. Here, the algorithms are constructed and designed in a way so that they learn from the data. They use different techniques, such as regression and clustering, for self-learning.
Example: Suppose, a set of inputs, such as Apple, Banana, and Orange. Here, the algorithms don’t identify the objects (inputs) as fruits.
They just try to make clusters of similar entities by identifying the specific features of these objects. Then, they give the output according to the clusters they created.
3. Reinforcement Machine Learning: In Reinforcement Learning, the design and construction of algorithms lead the machine to find an optimal solution for a given problem. It is achieved by the principle of iterative improvement cycle (to learn by past mistakes).
Example: Consider a computer game where a player has to cross a maze to escape from the enemy. Every time the player gets stuck in a dead-end, −10 points (punishment) are given. Then, the player tries hard to escape from the situation. When the player makes a right move, +10 points (reward) are given. Finally, after receiving many punishments and rewards, the player finds the right path to escape.
This is a demonstration of the reward and punishment principle used in reinforcement learning. In a similar way, the machines are employed for learning by this principal.
Now, the below image shows the altogether differences between AI and Machine Learning:
From the above image, the differences between AI and Machine Learning is clear. Now, we will move on to Deep Learning.
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What is Deep Learning?
Deep Learning is a subset of Machine Learning where we build algorithms that follow a layered architecture. The layered architecture (deep neural network) in Deep Learning is inspired by the human biological neural network. It is known as Deep Neural Network because the system is constructed with the help of a dense neural network. All the training process of the machine is executed by this deep neural network.
The below image of Deep Learning vs Machine Learning shows the difference in the mechanisms of both the fields.
Working of a Deep Neural Network
Initially, we provide the input dataset to the neural network. Then, the input data enters into a neuron. The input is multiplied with a corresponding weight value that is associated with each input. A weight value is used to show the strength of the link between artificial neurons. The outcome of this augmentation continues to move to the subsequent layer and acts as the input for that layer. This process is iterated for every layer of the network.
After that, at the output layer, the neural network yields an actual value for the regression task. For classification, the neural network gives the probability of every class. Then, using the computation algorithms, the weights of all the neurons are updated iteratively.
Finally, when the corresponding values of the weights give the output near to the actual, the neural network is fully trained. Interestingly, a fully-trained neural network is capable of identifying an entity with greater efficiency in comparison with the regular neural network.
In Machine Learning, we can train the algorithms using a small amount of data. But, in Deep Learning, we need an extensive amount of data to recognize a new input.
Furthermore, Machine Learning affords a faster-trained model, while Deep Learning models take a long time for training. The advantages of Deep Learning over Machine Learning are high accuracy and automated feature selection. In Deep Learning, a neural network learns the selection of significant features by itself. But, in Machine Learning, we need to manually select the features for the model.
Deep Learning vs Machine Learning
The below image explains Deep Learning vs Machine Learning:
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Applications of Data Science, Machine Learning, Artificial Intelligence, and Deep Learning
All of us use the Google Search engine almost every day. We use it for gathering information from multiple sources. When we type a certain text or a word in Google, it recommends the relevant searches according to the keyword.
So, a curiosity arises, how does Google show the relevant searches to us? This is where Data Science, Deep Learning, Machine Learning, and Artificial Intelligence show their shades.
- Google Search uses Data Science for predictive analysis. It gathers tons of data, cleans it, scales it, visualizes it, and prepares it for feeding into the Machine Learning models.
- Then, the Machine Learning techniques are used to construct algorithms for the models that can give accurate results for the searches. Suppose, a user enters ‘Data Science vs Machine Learning,’ then it would give the user the best possible result.
- Now, AI assembles all such information with the help of Machine Learning. Whenever a user enters the phrase ‘Data Science vs,’ AI gets active and, with the help of predictive analysis, it suggests the most expected phrase that the user is searching for. Also, AI employs Deep Learning to make the searches and predictive analysis optimized.
Hopefully, the above explanation clears your idea of Data Science vs Machine Learning vs Artificial Intelligence vs Deep Learning.
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