AI and Machine Learning E-Degree - A Complete Masterclass

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This E-Degree includes 6 major courses which mainly focuses on Python, R, various tools for AI & Machine Learning, Algorithms, and much more. Going beyond the online tutorials, it also includes numerous projects, case studies, quizzes, tips & trick and many other exciting stuffs. All of these resources are sufficient enough to build your conceptual understanding and sharpen your practical skills

Artificial Intelligence vs. Machine Learning vs. Deep Learning

Artificial Intelligence vs. Machine Learning vs. Deep Learning

Artificial Intelligence vs. Machine Learning vs. Deep Learning. We are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning

We are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning

Furthermore, we will address the question of why Deep Learning as a young emerging field is far superior to traditional Machine Learning

Artificial Intelligence, Machine Learning, and Deep Learning are popular buzzwords that everyone seems to use nowadays.

But still, there is a big misconception among many people about the meaning of these terms.

In the worst case, one may think that these terms describe the same thing — which is simply false.

A large number of companies claim nowadays to incorporate some kind of “ Artificial Intelligence” (AI) in their applications or services.

But artificial intelligence is only a broader term that describes applications when a machine mimics “ cognitive “ functions that humans associate with other human minds, such as “learning” and “problem-solving”.

On a lower level, an AI can be only a programmed rule that determines the machine to behave in a certain way in certain situations. So basically Artificial Intelligence can be nothing more than just a bunch of if-else statements.

An if-else statement is a simple rule explicitly programmed by a human. Consider a very abstract, simple example of a robot who is moving on a road. A possible programmed rule for that robot could look as follows:

Instead, when speaking of Artificial Intelligence it's only worthwhile to consider two different approaches: Machine Learning and Deep Learning. Both are subfields of Artificial Intelligence

Machine Learning vs Deep Learning

Now that we now better understand what Artificial Intelligence means we can take a closer look at Machine Learning and Deep Learning and make a clearer distinguishment between these two.

Machine Learning incorporates “ classical” algorithms for various kinds of tasks such as clustering, regression or classification. Machine Learning algorithms must be trained on data. The more data you provide to your algorithm, the better it gets.

The “training” part of a Machine Learning model means that this model tries to optimize along a certain dimension. In other words, the Machine Learning models try to minimize the error between their predictions and the actual ground truth values.

For this we must define a so-called error function, also called a loss-function or an objective function … because after all the model has an objective. This objective could be for example classification of data into different categories (e.g. cat and dog pictures) or prediction of the expected price of a stock in the near future.

When someone says they are working with a machine-learning algorithm, you can get to the gist of its value by asking: What’s the objective function?

At this point, you may ask: How do we minimize the error?

One way would be to compare the prediction of the model with the ground truth value and adjust the parameters of the model in a way so that next time, the error between these two values is smaller. This is repeated again and again and again.

Thousands and millions of times, until the parameters of the model that determine the predictions are so good, that the difference between the predictions of the model and the ground truth labels are as small as possible.

In short machine learning models are optimization algorithms. If you tune them right, they minimize their error by guessing and guessing and guessing again.

Machine Learning is old…

The basic definition of machine learning is:

Algorithms that analyze data, learn from it and make informed decisions based on the learned insights.

Machine learning leads to a variety of automated tasks. It affects virtually every industry — from IT security malware search, to weather forecasting, to stockbrokers looking for cheap trades. Machine learning requires complex math and a lot of coding to finally get the desired functions and results.

Machine learning algorithms need to be trained on large amounts of data.
The more data you provide for your algorithm, the better it gets.

Machine Learning is a pretty old field and incorporates methods and algorithms that have been around for dozens of years, some of them since as early as the sixties.

These classic algorithms include algorithms such as the so-called Naive Bayes Classifier and the Support Vector Machines. Both are often used in the classification of data.

In addition to the classification, there are also cluster analysis algorithms such as the well-known K-Means and the tree-based clustering. To reduce the dimensionality of data and gain more insight into its nature, machine learning uses methods such as principal component analysis and tSNE.

Deep Learning — The next big Thing

Now let’s focus on the essential thing that is at stake here. On deep learning.
Deep Learning is a very young field of artificial intelligence based on artificial neural networks.

Again, deep learning can be seen as a part of machine learning because deep learning algorithms also need data to learn how to solve problems. Therefore, the terms of machine learning and deep learning are often treated as the same. However, these systems have different capabilities.

Deep Learning uses a multi-layered structure of algorithms called the neural network:

It can be viewed again as a subfield of Machine Learning since Deep Learning algorithms also require data in order to learn to solve tasks. Although methods of Deep Learning are able to perform the same tasks as classic Machine Learning algorithms, it is not the other way round.

Artificial neural networks have unique capabilities that enable Deep Learning models to solve tasks that Machine Learning models could never solve.

All recent advances in intelligence are due to Deep Learning. Without Deep Learning we would not have self-driving cars, chatbots or personal assistants like Alexa and Siri. Google Translate app would remain primitive and Netflix would have no idea which movies or TV series we like or dislike.

We can even go so far as to say that the new industrial revolution is driven by artificial neural networks and Deep Learning. This is the best and closest approach to true machine intelligence we have so far. The reason is that Deep Learning has two major advantages over Machine Learning.

Why is Deep Learning better than Machine Learning?

Feature Extraction

The first advantage of Deep Learning over machine learning is the needlessness of the so-called Feature Extraction.

Long before deep learning was used, traditional machine learning methods were popular, such as Decision Trees, SVM, Naïve Bayes Classifier and Logistic Regression. These algorithms are also called “flat algorithms”.

Flat means here that these algorithms can not normally be applied directly to the raw data (such as .csv, images, text, etc.). We require a preprocessing step called Feature Extraction.

The result of Feature Extraction is an abstract representation of the given raw data that can now be used by these classic machine learning algorithms to perform a task. For example, the classification of the data into several categories or classes.

Feature Extraction is usually pretty complicated and requires detailed knowledge of the problem domain. This step must be adapted, tested and refined over several iterations for optimal results.

On the other side are the artificial neural networks. These do not require the step of feature extraction. The layers are able to learn an implicit representation of the raw data directly on their own.

Here, a more and more abstract and compressed representation of the raw data is produced over several layers of an artificial neural network. This compressed representation of the input data is then used to produce the result. The result can be, for example, the classification of the input data into different classes.

In other words, we can also say that the feature extraction step is already a part of the process that takes place in an artificial neural network. During the training process, this step is also optimized by the neural network to obtain the best possible abstract representation of the input data. This means that the models of deep learning thus require little to no manual effort to perform and optimize the feature extraction process.

For example, if you want to use a machine learning model to determine whether a particular image shows a car or not, we humans first need to identify the unique features of a car (shape, size, windows, wheels, etc.), extract these features and give them to the algorithm as input data. This way, the machine learning algorithm would perform a classification of the image. That is, in machine learning, a programmer must intervene directly in the classification process.

In the case of a deep learning model, the feature extraction step is completely unnecessary. The model would recognize these unique characteristics of a car and make correct predictions- completely without the help of a human.

In fact, this applies to every other task you’ll ever do with neural networks.
They just give the raw data to the neural network, the rest is done by the model.

The Era of Big Data…

The second huge advantage of Deep Learning and a key part in understanding why it’s becoming so popular is that it’s powered by massive amounts of data. The “Big Data Era” of technology will provide huge amounts of opportunities for new innovations in deep learning. To quote Andrew Ng, the chief scientist of China’s major search engine Baidu and one of the leaders of the Google Brain Project:

The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms.

Deep Learning models scale better with a larger amount of data

Deep Learning models tend to increase their accuracy with the increasing amount of training data, where’s traditional machine learning models such as SVM and Naive Bayes classifier stop improving after a saturation point.

Special Announcement: We just released a free Course on Deep Learning!

I am the founder of DeepLearning Academy, an advanced Deep Learning education platform. We provide practical state-of-the-art Deep Learning education and mentoring to professionals and beginners.

Among our things we just released a free Introductory Course on Deep Learning with TensorFlow, where you can learn how to implement Neural Networks from Scratch for various use-cases using TensorFlow.

If you are interested in this topic, feel free to check it out ;)

Artificial Intelligence vs. Machine Learning vs. Deep Learning

Artificial Intelligence vs. Machine Learning vs. Deep Learning

Learn the Difference between the most popular Buzzwords in today's tech. World — AI, Machine Learning and Deep Learning

In this article, we are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning.

Furthermore, we will address the question of why Deep Learning as a young emerging field is far superior to traditional Machine Learning.

Artificial Intelligence, Machine Learning, and Deep Learning are popular buzzwords that everyone seems to use nowadays.

But still, there is a big misconception among many people about the meaning of these terms.

In the worst case, one may think that these terms describe the same thing — which is simply false.

A large number of companies claim nowadays to incorporate some kind of “ Artificial Intelligence” (AI) in their applications or services.

But artificial intelligence is only a broader term that describes applications when a machine mimics “ cognitive “ functions that humans associate with other human minds, such as “learning” and “problem-solving”.

On a lower level, an AI can be only a programmed rule that determines the machine to behave in a certain way in certain situations. So basically Artificial Intelligence can be nothing more than just a bunch of if-else statements.

An if-else statement is a simple rule explicitly programmed by a human. Consider a very abstract, simple example of a robot who is moving on a road. A possible programmed rule for that robot could look as follows:

Instead, when speaking of Artificial Intelligence it's only worthwhile to consider two different approaches: Machine Learning and Deep Learning. Both are subfields of Artificial Intelligence

Machine Learning vs Deep Learning

Now that we now better understand what Artificial Intelligence means we can take a closer look at Machine Learning and Deep Learning and make a clearer distinguishment between these two.

Machine Learning incorporates “ classical” algorithms for various kinds of tasks such as clustering, regression or classification. Machine Learning algorithms must be trained on data. The more data you provide to your algorithm, the better it gets.

The “training” part of a Machine Learning model means that this model tries to optimize along a certain dimension. In other words, the Machine Learning models try to minimize the error between their predictions and the actual ground truth values.

For this we must define a so-called error function, also called a loss-function or an objective function … because after all the model has an objective. This objective could be for example classification of data into different categories (e.g. cat and dog pictures) or prediction of the expected price of a stock in the near future.

When someone says they are working with a machine-learning algorithm, you can get to the gist of its value by asking: What’s the objective function?

At this point, you may ask: How do we minimize the error?

One way would be to compare the prediction of the model with the ground truth value and adjust the parameters of the model in a way so that next time, the error between these two values is smaller. This is repeated again and again and again.

Thousands and millions of times, until the parameters of the model that determine the predictions are so good, that the difference between the predictions of the model and the ground truth labels are as small as possible.

In short machine learning models are optimization algorithms. If you tune them right, they minimize their error by guessing and guessing and guessing again.

Machine Learning is old…

Machine Learning is a pretty old field and incorporates methods and algorithms that have been around for dozens of years, some of them since as early as the sixties.

Some known methods of classification and prediction are the Naive Bayes Classifier and the Support Vector Machines. In addition to the classification, there are also clustering algorithms such as the well-known K-Means and tree-based clustering. To reduce the dimensionality of data to gain more insights about it’ nature methods such as Principal component analysis and tSNE are used.

Deep Learning — The next big Thing

Deep Learning, on the other hand, is a very young field of Artificial Intelligence that is powered by artificial neural networks.

It can be viewed again as a subfield of Machine Learning since Deep Learning algorithms also require data in order to learn to solve tasks. Although methods of Deep Learning are able to perform the same tasks as classic Machine Learning algorithms, it is not the other way round.

Artificial neural networks have unique capabilities that enable Deep Learning models to solve tasks that Machine Learning models could never solve.

All recent advances in intelligence are due to Deep Learning. Without Deep Learning we would not have self-driving cars, chatbots or personal assistants like Alexa and Siri. Google Translate app would remain primitive and Netflix would have no idea which movies or TV series we like or dislike.

We can even go so far as to say that the new industrial revolution is driven by artificial neural networks and Deep Learning. This is the best and closest approach to true machine intelligence we have so far. The reason is that Deep Learning has two major advantages over Machine Learning.

Why is Deep Learning better than Machine Learning?

The first advantage is the needlessness of Feature Extraction. What do I mean by this?

Well if you want to use a Machine Learning model to determine whether a given picture shows a car or not, we as humans, must first program the unique features of a car (shape, size, windows, wheels etc.) into the algorithm. This way the algorithm would know what to look after in the given pictures.

In the case of a Deep Learning model, is step is completely unnecessary. The model would recognize all the unique characteristics of a car by itself and make correct predictions.

In fact, the needlessness of feature extraction applies to any other task for a deep learning model. You simply give the neural network the raw data, the rest is done by the model. While for a machine learning model, you would need to perform additional steps, such as the already mentioned extraction of the features of the given data.

The second huge advantage of Deep Learning and a key part in understanding why it’s becoming so popular is that it’s powered by massive amounts of data. The “Big Data Era” of technology will provide huge amounts of opportunities for new innovations in deep learning. To quote Andrew Ng, the chief scientist of China’s major search engine Baidu and one of the leaders of the Google Brain Project:

The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms.

Deep Learning models tend to increase their accuracy with the increasing amount of training data, where’s traditional machine learning models such as SVM and Naive Bayes classifier stop improving after a saturation point.

Why you Should Learn Artificial Intelligence

Why you Should Learn Artificial Intelligence

Artificial Intelligence (AI) uses intelligent machines built in a way that they react like humans. In this post, you'll see top 10 reasons why you should learn AI

Introduction

Artificial Intelligence has revolutionized the way people think, learn, and work in various fields, from finance to healthcare and mobile apps. What’s more interesting is that AI plays more role in our daily lives than we can imagine. From Siri and Ok Google to various virtual player games and social media apps, AI is everywhere. It sure is the most happening topic in every business right now. It is the most wanted and exciting career domain right now in the market. Let us know what Artificial Intelligence is.

What is Artificial Intelligence?

Artificial Intelligence uses intelligent machines built in a way that they react like humans. The primary process involved in making these smart machines is to carry out decision making, which analysis and uses data available in an enterprise. It is similar to the human mind absorbing and synthesizing information and providing with the required decision.

1. Artificial Intelligence in Healthcare industry:

We are now in a digital age where everything could be implemented with the help of technology and Internet. Nowadays we get to see that a doctor can monitor and diagnose a patient from a remote location. This has reduced the necessity of being in person. Image the same way where the patient’s health condition is checked against predefined medications and algorithms prescribing a solution to the doctor. This would be a great success in the entire Healthcare industry. The current healthcare industry is completely dependant on the doctor’s sole knowledge and no supporting decision-making system is available to advise the treatments or the medication. It is completely coming up from the Doctor’s experience and decision.

Imagine a condition where all the patient vitals and health records are pre-analyzed and a personalized treatment plan is produced for the doctor to review will change the entire treatment process.

2. Artificial Intelligence in responding to your emails:

If you have been using Gmail’s latest mobile application then responding to your emails would have been really easy and also exciting. So based on your email content, a predefined answer are already pre-populated as tags for you while responding back to the email. The latest version of Gmail mobile application has drastically reduced the turnaround time in terms of responding an email back. So the mobile applications are evaluating the emails now and giving us appropriate suggestions while writing back to the sender. Well, the possibilities are limitless and more importantly endless. So we got to wait for the future and see how it is going to affect the human interventions. The above list is a general observation of how Artificial Intelligence is already taking up its baby steps and improving the current processes. Well, the limitations are endless and one needs to understand to what extent it can be helpful. Involving and rebuilding the process by implementing Artificial Intelligence and Machine learning will be definitely the future and it makes sense to build the skill in this arena. A lot of possibilities are available where the implementations are not specific to an industry but this can be generalized.

3. Artificial Intelligence In Mobile World:

The smartphone nowadays is not only considered as a communication device anymore it can be called as your digital wallet and much more than that, even we can classify them as your personal assistants. Well, speaking about personal assistants, it is worth mentioning about “Siri”. It is one of the best examples of proper utilization of Artificial Intelligence and Machine Learning. So based on your habits and interests “Siri” will be able to answer all your questions and provide valuable suggestions. This is already happening and this is the start of next wave of technology utilization. We have seen days where mobile devices didn’t have touchscreens and now we are in a digital age where the majority of the devices are touch screens. The next age of mobiles will be working on the voice commands which is nothing but “Siri”. This change will be huge and it will completely change the way people are using their mobile phones at the moment.

4. Artificial Intelligence in Smart Home Devices:

Based on your preferences what if your home environment is changed from time to time. Wondering whether this is possible or not? Well, it is definitely possible. In past few years, we have seen a lot of smart devices coming up in the market which works in line with our preferences. So basically based on your preferred patterns, the lighting in the house and temperature of the refrigerator and other household devices can definitely be monitored and eventually project optimum utilization settings as well. All of this is happening because of underlying Machine Learning and Artificial Intelligence built into these devices.

5. Artificial Intelligence in Automobile Industry:

If you are updated with the latest technology happenings then you wouldn’t have missed this at all. The concept of self-driving cars and autopilot features are in the news lately and big players like “Google” and “Tesla” are already in this arena. Have you ever imagined that you will be traveling in a car which doesn’t need a driver to take you from point A to point B. Well, this is not at all a dream anymore, a lot of test runs have gone through were the concept cars going to hit the road soon. This is definitely going to be the future in the automobile industry. A lot more research and development needs to happen within this area as we have to consider the safety and security aspect of the passengers. Well, we have to just wait and watch what is going to happen.

6. Artificial Intelligence in Music and Movie Recommendation services:

Who doesn’t like watching movies and listening to music right?

What if your next song or movie is recommended to you by a system based on your interests and browsing history? This would be pretty cool right!!! Well, they are already few mobile applications that understand your choice of music and movies and recommend the same genre as a suggestion. This has been a massive success in terms of sales and promotions of various brands because the target market is available for the brands. The ads that you have seen on your browsers are also based on your previous activities. All of your activities are analyzed and a chain of recommendations are provided. With the help of the recommendations, it will definitely help the individuals to explore new options.

7. Artificial Intelligence in Retail industry:

This is going to be a huge game changer for all the retail companies because if they understand the purchase pattern and the requirements of their customers, they will definitely have to tailor their process to be the market leader. The Artificial Intelligence concept comes into the picture when the buying patterns are analyzed and understands the needs of the customer. The retail industry can gain huge profits by properly analyzing the customer needs vs buying the pattern and based on the consumption if the system could suggest:

  • Relevant coupons
  • Promote discounted offerings
  • Targeted marketing

Stocking the warehouses etc.All of these subprocesses with definitely be improved and to be honest it will help the customer a lot. As of now, we are going towards a clash where the businesses are legally obligated that they are invading an individual privacy by closely evaluating their buying pattern and the products that they buy.

In certain parts of the world, Amazon has started an offer called “Pantry” where they can select few products as essentials and they are automatically delivered to you on a periodic basis. Well, this is a perfect example for introducing the Artificial Intelligence into the process where a better operational and stocking activities are carried out.

8. Artificial Intelligence in Security Surveillance:

Safety and security are the important aspects and the basic needs of an individual or for an organization. The surveillance setup, i.e. security cameras monitoring important areas of the business is definitely a better idea. But watching too many screens for a very long time will be a boring job and ultimately we lose the option of attending the emergencies when there is a need.

So what if there are predefined algorithms that are fed into the security cameras and make them more powerful. Based on the surveillance and the activities the system would be able to analyze and let us know whether the situation is actually a threat or not. 

If it is a threat then it would immediately alert the human security officials associated with the business. If this sort of technology advancements are available right now then it would have made a positive impact on the security of the individuals and operationally the situation will be handled more efficiently.

9. Artificial Intelligence in Fraud Detection:

The fraud detection activity monitoring systems are actually a boom to the human kind where their money is protected by evaluating the transactions that they make. Have you ever received an email or a text message from your bank confirming the recent transaction activity was actually made by you or was it someone else who got hold of your card.

Well, most of this transaction monitoring is carried out by the fraud detection team which is powered by AI.

The transaction patterns of the individual, the usual withdrawal amount from the ATM and the frequency of the account logins. All of this data is stored and analyzed for suspicious activity. 

For example: if you have never used your ATM card for years and all of a sudden you have started withdrawing money from your card then this would be definitely flagged as a fraud alert by the system. So the AI algorithms are developed by considering different scenarios and situations which will ultimately alert the users to be cautious about their belongings. The same technique can be expanded and further used in other industries as well.

10. Artificial Intelligence in Online Customer Support

Nowadays every business has a website for sure because it has been a need vs a luxury. With the rapid use of smartphones and internet, it has been evident that most of the customers are tending to get information via online interactions rather than phone interactions.

So most of the websites have an online chat system which responds to your queries. Do you think that a real human is responding back to your queries all the time? Well, not all the time. To make sure the business is live and active 24/7 days businesses are opting for automated bots which actually does the same job as of a human. The responses are based on the content available on the website and the same is fed back to the customer based on his or her request.

Well, this process is gaining more and more acceptance and the underlying logic is also going through a makeover where it can accommodate more requests and provide more accurate information. All of this is happening because of the rapid development of Natural Language Processing (NLP).