What Gen-z should know about Machine Learning? Just a Click away

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Today I will be talking about one of the important fields emerging in 2023. I will be covering everything that you should know about Machine learning. If you want to build fundamentals of machine learning then you can't miss this blog. And in the end, i will be sharing how you can get started with machine learning as a career option.

What is Machine Learning?

It is one of the most fundamental questions and a lot of people complicate this simple term. So what is machine learning? Machine learning is just a branch of artificial intelligence and computer science.

Machine learning focuses on using data and algorithms to resemble the way humans learn with improving its accuracy.

Machine learning is growing very rapidly as making technological advancements in storage and processing power helps to build some innovative products. For example Netflix recommendation engines and self-driving cars.

Now let's discuss why machine learning is important.

What is the significance of Machine Learning?

In the growing field of data science machine learning is an important component. In data mining projects the use of statistical methods, and algorithms are trained to make predictions and help in uncovering key insights that help in growing the businesses.

As data is going to grow and expands that is why the demand for data scientists will increases.

How machine learning algorithms are created?

Machine learning algorithms are created by using some frameworks like TensorFlow and PyTorch.

Alot of people have this confused among the terms like machine learning, Deep learning, and Neural Networks. Let's clear this confusion one after the another.

What is the difference between Machine learning, Deep Learning, and Neural Networks?

If you start from the basics and are very clearly represented through the image machine learning, deep learning, and neural networks are the sub-field of artificial intelligence. To be precise neural network is a sub-field of machine learning and Deep learning is a sub-field of Neural networks.

Let us talk about deep learning first.

Deep Learning

Deep learning basically deals with larger and raw datasets. It helps in eliminating some of the human intervention. Deep learning is also called supervised learning.

Significance of deep learning

A non-deep machine learning becomes more dependent on human intervention for learning. Hence require more structured data to learn. Whereas deep learning makes it easier because it can read the raw datasets.

Neural Networks

Neural networks which are also known as artificial neural networks are built with a few Node layers which contain:

1. Input layer

2. One or more hidden layer

3. Output layer

Every node connects to each other and has a weight and threshold.

Now you must be thinking that what is "deep" in deep learning. So deep refers to the number of layers in a neural network.

If a neural network is having more than three layers it is considered to be a deep learning algorithm or deep neural network on the other hand if a neural network has only three layers it is just a basic neural network.

Use of Neural Networks and Deep Learning

The significance of having these is that it accelerates the progress in areas of speech recognition, natural language processing, and computer vision.

Now you must be having a question that how actually all these work. So let's figure this out.

How does machine learning Work?

So you can actually break down the learning system of machine learning broadly into three parts.

1. A Decision Process: The algorithms of machine learning are used to make a kind of prediction. Based on what? exactly the input data. Algorithms that you write help in producing an estimate to recognize or to read the pattern in the data.

2. An Error function: This function helps in evaluating the prediction of the model. Suppose it detects a known example then this function can easily make a comparison to see the accuracy of the model.

3. Model Optimization Process: To meet the threshold of accuracy the algorithm repeats the process of evaluation and optimization. This is called the model optimization process.

apart from these parts, there are a few methods of machine learning that are needed to be discussed.

What are the methods of Machine Learning?

There are three primary methods of machine learning which are.

Supervised Machine learning

To predict the outcome accurately the use of labeled datasets to train algorithms is defined as Supervised learning or Supervised Machine Learning.

Significance of Supervised Learning

The significance of Supervised learning is that it solves a variety of real-world problems and one of the most common examples is filtering out spam from your inbox.

Methods of Supervised Learning

Methods that are included in supervised learning are neural networks, naive Bayes, linear regression, logistic regression, random forest, and support vector machines.

Unsupervised Machine Learning

Analyzing and clustering unlabeled datasets with unsupervised machine learning algorithms come in handy. These algorithms help in discovering hidden patterns or data grouping without the need for human interventions.

Semi-Supervised Learning

Semi-supervised learning works as a medium between supervised and unsupervised learning. It solves the problem of not having labeled data for a supervised learning algorithm and cost-effectiveness is also one of the features.

Now let's talk about some standard machine learning algorithms.

What are the top 5 standard machine learning algorithms?

Algorithms that are used commonly in machine learning are:

1. Neural Networks: These simulate the way the human brain works. Builds a connection of a huge number of nodes connected to each other.

2. Linear regression: This algorithm helps and is used when there is a need for numerical values. This builds a linear relationship between different values.

3. Logistic Regression: These algorithms help in predicting categorical responses, and give answers in "yes or no" form.

4. Clustering: This algorithm helps in finding the patterns in data and helps them get grouped.

5. Random Forests: It predicts a value or category by combining the outcomes from the number of decision trees.

Now you must be thinking that what is the actual use of machine learning? So let's discuss some real-world examples.

Top 5 Real-World Machine Learning Examples

You might have encountered machine learning examples in day-to-day life. Some of them are:

1. Speech recognition: It is also called as automated speech recognition (ASR). One of the popular examples will be Siri.

2. Customer Services: It's an online chatbot that replaces human agents and helps people to resolve their queries.

3. Computer Vision: It helps in deriving meaningful information from digital images, videos, and other visual inputs.

4. Recommendation Engine: It helps in discovering the data trends using your past consumption behavior data.

   

5. Automated stock trading: It is designed to optimize stock portfolios. These high-frequency trading platforms make thousands of trades per day without any human intervention.

Now let's bring an elephant into the room. If you want to start a career in machine learning you must be thinking of how to get started. Let's check it out now.

How to get started with machine learning?

Python is the most preferred language for learning machine learning as it has a lot of libraries that can help you in managing the data.

Along with that a GPU and larger ram are also required to handle the large chunks of data.

After learning the python language there are a few very useful libraries that you need to master, like:

1. Pandas: Library for data analysis. It comes with inbuilt methods for grouping and combining.

2. Numpy: Library for matrix processing and handles multi-dimensional array.

3. Matplotlib: Library for data visualization. Just like seaborn.

4. Scikit-learn: Library contains machine learning inbuilt functions of both supervised and unsupervised learning.

5. Scipy: Contains different modules of optimizations such as linear algebra, integration, and statistics.

6. OpenCV: Library for computer vision and image processing

7. TensorFlow: it is popular for high numerical computations.

8. Keras: It has high-level neural networks API which is capable of running on top of the tensor flow, Theano, etc.

9. PyTorch: It has many tools that support computer vision, NLP, and many machine learning programs.

   

10. Theano: Library that is used to evaluate as well as optimize mathematical expression which involves multi-dimensional arrays in an efficient manner.

This the fundamentals that will help you in getting started with machine learning.

Thank You 🙏

I hope this article was helpful to you and I will be sharing a lot of similar informative blogs with you. Keep supporting me as always.