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MFCCs are very important in audio processing and speech recognition systems. Suppose you want to make a machine classify the audio sound, use MFCCs, and get fascinating results.

Want to learn how we can use python to do this complicated task and get the best results in the audio processing and classification tasks. Let us hop in then and get the basic idea of what an MFCC is and how we can get them.

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Machine Learning Projects | Beginners.

Finding Projects for machine learning is very difficult, and that is a fit for the current level of knowledge and expertise. Since projects are important for your machine learning carrier. Here is a list of Machine Learning Projects for beginners.

Here, we will see the 4 Projects for beginners to start machine learning. These 4 projects are the regression projects and are sorted in an easy to difficult fashion. By doing such projects, you will get insight into the regression problems, and as you will complete all 4 projects, you will have a good grasp of the Regression concepts and regression problems.

I will be providing the datasets with them. Try to solve them with good understanding. Don’t just copy and paste the code and make it done. Rather write the code by yourself, even if you copy from others. The writing will help you understand the problems better.

So let’s start.

Salary Prediction

This is by far the easiest regression problem. In this project, the aim is to predict an employee’s salary based on some input features. These input features may be age, experience, knowledge, etc.

The algorithm you will be using is Linear Regression. Linear regression is simply predicting a line that best fits the given data.

The dataset is from Kaggle. In this Dataset, three input variables are given. Experience, Test score, and interview score, and the output variable is the salary. Your job is to predict the salary based on these three input variables. There are empty values in the dataset, and you need to see google how to get rid of empty data. I could give you the formatted dataset with no empty values. But I want you to search google and learn the skills.

Predicting Students score

This is yet another simple problem of regression. In this problem, the aim is to find the students’ final scores based on their previous grades, attendance, and other feature.

The dataset is from UCI Machine Learning Repository and is one of the famous datasets on the website. This dataset can be used for regression and classification purposes. I advise you to use this dataset for regression and not classification. Try to find the final grades of the student using simple linear regression.

Boston Housing

This Project is more advance than the previous ones. It may require deep googling and certain more techniques to complete this. But Overall, it is the same as the previous one. The only difference is that there are many contributing factors to the output. The goal of the project is to predict housing prices in Boston. There are many input features such as latitude, longitude, number of rooms, etc.

The dataset has 504 rows and 14 columns. Your job is to predict the house price, which is dependent on the input features.

Movie Ticket Pricing Prediction

This is another simple application of linear regression. In this project, the goal is to predict the prices of movie tickets. This dataset has been collected by TMDB and provides movie details.

The dataset is from Kaggle and has 47 columns, i.e., input features that contribute to predicting the final price. It is difficult than others, so I advise you to google and doodle around to learn how this problem can be solved.

conclusions

With these 4 projects, I am certain you will gain many insights into the regression field of machine learning. Be with me for 4 classification projects for beginners. Do you want to read my previous post?

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The Types Of Machine Learning | Let's Understand Part 2.

In the previous post, we saw the first two types of machine learning. In this post, we will discuss the other two types of machine learning. These are - Semi-supervised machine learning and Reinforcement learning.

Semi-supervised Learning

This type of learning is new and is an important part of the machine learning field. We use this type of learning when we have some labeled data and a lot of unlabelled data in training. This type of learning is in between supervised and unsupervised learning. The part where we have the labeled data falls in the supervised domain, and the amount of data that is unlabelled falls in the unsupervised domain.

Getting the labeled data is very expensive and time-consuming while acquiring the unlabeled information is easy and inexpensive. The use of unlabeled data with the labeled data produced a considerable increase in learning accuracy.

Thus, situations may arise where we have a small amount of labeled data and a large amount of unlabeled data. Then in such cases, we can neither go to supervised nor unsupervised learning strategy. So, in such cases, semi-supervised learning comes to our rescue. We can use it to increase our accuracy significantly and save our time and effort of labeling every data point.

The analogy.

In layman’s language or in words that are easy to understand, semi-supervised learning is like supervising a student for a short amount of time and then letting him go and wander the field independently.

It solves classification problems. That means that you will need some supervised parts. Then at the same time, you have to train the model on large datasets of unlabelled data, for which you need the unsupervised part of machine learning.

The central concept is to cluster different data points in the same clusters and then use supervised learning to name data points or instances in those groups.

For example, suppose we have a data set of 1000 images of different vehicles. And we have four categories, and 100 data points or instances are labeled with those category names. We are then taking the concept of semi-supervised learning. First, we need to make clusters of images containing identical vehicles. Once the group of clusters has been formed using an unsupervised learning approach, then the task of supervised learning comes to play. We then assign the names to various collections and thus multiple instances in those clusters. In this way, we use semi-supervised learning to train the model using both the learning strategies i.e., supervised and unsupervised learning.

📷Semi supervised Learning

Reinforcement Learning

In this machine learning strategy, we are concerned about how an agent maximizes the rewards. There are some essential concepts in this learning, which need to be understood.

The First - Agent.

An agent is the action doer—the machine, which does the action in the environment. The whole aim of the agent is to make the rewards maximum by doing the activity right. If the agent does the action wrong, then the agent gets punished means, the reward is lessened from the total reward repository of the agent.

The Second - Action.

These are the actions that the agent does on the environment. Some examples are a robot walking, a robot playing tennis, etc.

The Third - Environment.

The environment is the playing field for the agent. It is where he does the actions. The environments can be different. For game-playing AI, it is the game, for mopping robot, it the house and for mars rover, it the mars.

The Fourth - Reward.

The reward is an achievement, increment the score of the agent. The learning process is continuously circulating the rewards. The agent tries to maximize the rewards, and it does the word so that its regards get up. An example of a reward can be, increment in the score while playing the game.

The Fifth - State.

The state means the updated environment. When an agent does some action on the environment, the environment gets updated. That updated environment is returned to the agent and is known as the state.

This usage of this type of learning is when we don’t have any data to start. In Reinforcement learning, the agent begins executing the tasks, and if he does the tasks correctly, the agent gets rewarded, and if he makes a mistake, he gets punished. Like in the above example, Increment in the score is a reward, and a decrement in the score is punishment.

For an analogy, it like leaving a man in the car and telling him to figure out how to drive. He will learn the driving by himself by making mistakes and rectifying them. Here, the reward can be money. If the driver does not make any mistake, he will receive 1$, and if he makes a mistake, he will give 50 cents.

📷Reinforcement Learning

Conclusions

These are the four types of machine learning strategies. All the machine learning algorithms fall into one of the above Learnings. They offer a vastly considerable number of algorithms that can be implemented. We will discuss them in future posts. If you liked the content, please share :) and if you think the content needs to be improved, please comment below with the suggestions.

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The Types Of Machine Learning | Let's Understand Part 1.

📷

Suppose you want an easy understanding of machine learning and its algorithms and also various other things in machine learning. But you could not get those explanations from anywhere. You are not alone; I myself was one of the new learners and even am learning today. And I am also trying to improve my knowledge in this field. If you know machine learning only by definition, you are ready for these learning strategies that we use to train machines. So let us talk about the types of machine learning that we use daily (like Machine Learning students, Machine Learning Engineers, etc.) But if you have no idea or have forgotten, check out my previous post.

This will be a two-post article. In the first, we will go through the first two types, and in the next, we will learn the next two. Now, as we are all set, let us dive into the good meet.

Types of Machine Learning Strategies.

There are three types of machine learning. Initially, there were three, but later type added one more type to the ranks of machine learning types. Thus in total, we have 4 types of machine learning strategies. Let see what those are and then define them one by one. We will go into the brief depth of these strategies to understand the upper creamy layer of them. When we are ready to learn them in-depth, I will be posting each algorithm that comes in these strategies. When we know the algorithms in detail, then we will be ready to implement them. Let me know if you want the practical implementation also using Python.

Let us see what different types of strategies that we have been talking about are.

📷

From the above diagram, we can see that there are FOUR types of machine learning. These are as under:

Supervised Learning.

Unsupervised Learning.

Semi-supervised Learning.

Reinforcement learning.

1. Supervised Machine Learning(First type of machine learning). 📷

Supervised machine learning is the learning strategy that maps an input to the output based on example input-output pairs

Wikipedia says.

Now, let us try to understand this most thoroughly. You remember the situation where you are given a question paper and an answering script for practice to prepare for the examination. You had to practice and come the next day prepared for the examination. The same happens here; in our case, the questions and answers were input and output, respectively. In the same manner, supervised learning involves giving the algorithm both questions and answers for learning. Now another concept comes into play. That concept is known as “Teacher.” The person who corrected us when we were wrong. In the case of machine learning, we may say that there is a supervisor. This supervisor is supervising the whole process of learning. When the algorithm makes a mistake, this supervisor corrects the mistake or at least moves closer to the correction.

In a nutshell, we may say that supervised learning is that learning, where the learning algorithm is given both the answers(Output feature) and questions(input features). The algorithm learns the mapping, and then once the model has learned this mapping, it is tested with the data it has never seen. This testing gives us an idea, How well the algorithm has learned and can deal with the new data?. This concept is known as Generalization (which we will talk about in coming articles). This testing is similar to us appearing in the exam and being tested with the questions we have not seen while preparing.

Supervised learning can be classified into two more subtypes:

Regression.

Classification.

📷

Supervised machine learning subclassification

Regression.

I will try to touch on these concepts, and we will talk about them in detail in the future.

Regression, in simple terms, is when we have to predict a continuous value, like predicting salary based on experience, age, etc. In regression, the basic concept is to fit the line in the data such that this sum of all the differences between the points and the line is minimum. This line-fitting concept is the same as what we have studied in high schools.

📷 Classification.

In these problems, the algorithm tries to predict the class of the input. This is the most common application of machine learning. It is like saying whether it is a cat or a dog when shown as a picture of one of them. Classifying animals into their habitats, classifying the emails as spam and not spam, etc. There are many more examples in this.

📷 2. Unsupervised Learning(Second type of machine learning). 📷

Unsupervised is different from supervised machine learning because we don’t have an outcome or output variable. The data we have been given is not labeled, which means we have not been told which one is a cat or a dog. In machine learning, we feed the unlabelled data to the machine, and it tries to find the similarities between the data points and comes with the clusters(in simplest form). The points in these clusters are similar, based on the characteristic which our algorithm has found.

If we talk about the analogy, it is like giving a child bunch of toys and telling him to separate the similar toys into their respective groups.

Unsupervised learning has further 2 sub-types:

Clustering.

Associations.

📷 Clustering.

Clustering is what we talked about. Making the groups of similar things or, in machine learning terms making groups of similar data. In the below example, our algorithm learns the differences between the circles, squares, and triangles. Thus making clusters of them.

📷 Associations

Associations mean finding the relations between various data points and associating them with each other. Malls, Walmart, etc., use this technique. They use this technique to recommend the things that are bought together to various customers. Let us take an example of parents buying Diapers for the baby, and it has been seen that those who buy diapers also buy baby food. This relation is learned by the association algorithms and recommends the products to parents based on these associations.

📷 Conclusion

This completes part 1 of the article. Be connected for the second part. If you like my work, share this with your machine learning friends :).

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All you need to know for starting basic Machine Learning.

The world yet to come and the world currently is in favor of machines that learn by themselves as humans do. This world is not there yet, where a machine can solely learn by itself but we are moving towards that era. It is that branch of life rather the computer science or statistics(some say) in which machines have the ability to learn from the environment. That environment could be the real one, where we live or that could be the one around the data we have to feed those machines. Thus Machine Learning will be playing an important part in our lives.

I myself am here to learn these things that I am teaching you. It seems vague, but it is true. I am also in the process of learning new things day after day, week after week, and month after month. There are tremendous things that are happening in this area that are worth learning and spreading.

So let us start, by understanding the basic things that everyone should know before going deep in Machine learning.

The Basic Question that everyone asks:

What is machine learning?

Everyone asks.

To this our trusted Wikipedia says:

“Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data”

So what does this mean – it simply means, giving the child knowledge about the environment [ all at the same time, superman baby], the child learns all the things and then improves as he grows. Not a perfect analogy but it would work.

So the whole aim of machine learning is to make them i.e. machines do tasks that are not explicitly programmed in them.

Now we have given the data to the machines, what does it do with the data?

The simple answer to this question is simple as it goes. It could make predictions, decisions, generate new things, same as the humans do, but off course in a different way.

There are usually three phases involved in the process of making machines learn and do some incredible things, that are not programmed in them. 1) Training 2) Testing 3) Deployment

Training: The first and foremost thing is to train the machine. How do we do that? Million-dollar question? [ joking, not million-dollar, but once a time it was.] It all starts, with the data. For machines to learn, the most important thing is data. So in the training step, we give this data to these machines, they do some mantras on them and learn the relations in the data.

Testing – Then is the testing. It is like making sure that an employee, after the probation period [training period] is ready for the real world. Machine learning applications have a wide range of applications from predictions, to most important decisions like health, etc. So they need to be very much perfect in things that they are trained to do. In this phase that is what we make sure, that they are ready for the real world by making them sit in the exam like students have to. If they pass, they are a go else, they are retrained with some modifications.

Deployment– This is the phase where they are let go in the wild to make predictions, decisions, and many many more things.

One more step can be added, that is as machines perform in the wild, occasionally its performance decreases. In those times, it is again retrained with new data and then tested and then again deployed.

The success in machine learning?

Table of Contents

The success in machine learning? How can you start machine learning? How often should you practice? Where to go after basics? Final Words.

This field has made tremendous achievements from the day it was born. In middle, it fell ill[analogy i.e. its scope almost vanished] but then again, it rose to the heights of the beasts.

The initial successes were, though small but very important. Some of them can be pointed as:

Anomaly detection – Detecting anomalies in the networking detecting attacks, etc.

Clustering in the markets – separating the market audience into separate groups and then targeting those groups based on their separate interests.

Fraud detection – Detecting Frauds like in banking websites, credit card fraud detection.

Spam filtering – Detecting spam in messages like emails, SMS, etc.

Some more start of the art achievements.

Image classification – classifying between two or more images. One most common example is classifying images as being the cat image or dog image.

Text Generation

Language Translation – Translating from one language to another without human intervention.

How can you start machine learning?

PrerequisitesYou should have a working knowledge of python. There are other languages that we can use in machine learning but python makes it simple and easy and there is a large community that is very happy to help you. Basics of machine learning i.e. theory that I talked about above and some more concepts which I will be talking about in the future. Then Proceed to the next section i.e. books. Books you should read.

For Learning Python I would suggest, watch one or two courses on YouTube or Udemy. That is enough to get you going. Don’t fall in tutorial hell.

If you want the book for Python, my Suggestion is Al Sweigart, Automating the boring stuff with python. It is the best book to get you starting in python. It gives you all the aspects of the language to get started. https://amzn.to/3szYcyY

Then if you wish to go beyond the basics, his another book named Beyond the basic stuff with python. https://amzn.to/3v5mlPB Then you should go for one book that satisfies your needs, my suggestion is to go for books that show hands-on learning i.e. teaches with examples not just theory. Some books that I would recommend:

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron – start with this book and complete all the exercises in the book. No need to do them all by yourself. Just write them and try to understand what they do and how they do. But for basics, you could read this book’s part 1. https://amzn.to/3n4f8MO

How often should you practice?

It depends:

Being a non-machine learning background. It depends-If you are going to be full-time, then you should practice the algorithms  2-3 hours daily and improve your knowledge about those algorithms. If you are part-time, and trying to achieve new skills, then 1 to 1.5 hours daily is sufficient for you. These are not hardcoded, After all, it is you who have to decide how to do the things.

Where to go after basics?

There are many paths that you could choose from here. You could master one of the traditional Machine learning types or You could do what is Prefeble i.e. go for Deep Learning. But before this have a good understanding of machine learning basics and some of the libraries that are being used in traditional machine learning.

Final Words.

Finally, I would suggest, it is you who has to take action and start doing what you intend to do. Don’t stop now, get up and open your system and start learning.

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