Hey Folks 👋🏻
You may be wondering what is Machine Learning? What is it? Where can it be found? How to implement Machine Learning yourself? All of these questions, let's get to the answers! Grab a coffee and get reading!
Machine learning is an application of Artificial Intelligence where we give machines access to data and let them use that data to learn for themselves. It’s getting a computer to perform a task without explicitly being programmed to do so.
When most people hear Machine Learning, they picture a robot — a dependable butler or a deadly Terminator depending on who you ask. But Machine Learning is not just a futuristic fantasy, it’s already here.
As kids, you have probably watched a battle bots competition before, right? You know, where robots are coded with an algorithm (a set of instructions that are followed to accomplish a task; it’s a computer’s thought process) to attack and “battle” each other.
Well, if machine learning was used in this situation, the robot itself would decide at the moment based on the information it has been given. This means, that the robot would choose to perform either option A or option B, rather than being told through code to always perform option A no matter what.
So, instead of coding software with specific instructions, machine learning trains an algorithm so it can learn how to make decisions for itself, kinda imitating how humans make decisions.
As mentioned before, Machine learning is teaching computers to think in a similar way to how humans do: Learning and improving upon past experiences. It works by exploring data and identifying patterns and involves minimal human intervention.
Almost any task can be done via Machine Learning. You might not even know, but this post that you are reading was filtered within so many others and all of it was seamlessly done via Machine Learning! Cool, right?
Supervised Machine Learning allows you to collect data or produce a data output from a previous ML deployment. Supervised learning is exciting because it works in much the same way humans learn. In supervised tasks, we present the computer with a collection of labeled data points called a training set (for example a set of readouts from a system of train terminals and markers where they had delays in the last three months). The two common supervised learning tasks are regression and classification tasks.
In regression, we aim to predict numerical values, for example, predicting stock prices via market sentiments or predicting house market prices via historical data (past data).
In classification, as assumed by the word, we try to classify inputs into different categories. One very good example is spam prediction, in which a Machine Learning model, predicts whether an email is a ham or spam. It was also one of the very first Machine Learning projects ever done!
Unsupervised Machine Learning helps you find all kinds of unknown patterns in data. In unsupervised learning, the algorithm tries to learn some inherent structure to the data with only unlabeled examples. Two common unsupervised learning tasks are clustering and dimensionality reduction.
In clustering, we attempt to group data points into meaningful clusters such that elements within a given cluster are similar to each other but dissimilar to those from other clusters. Clustering is useful for tasks such as market segmentation.
Dimension reduction models reduce the number of variables in a dataset by grouping similar or correlated attributes for better interpretation (and more effective model training).
From automating tedious manual data entry to more complex use cases like insurance risk assessments or fraud detection, machine learning has many applications, including client-facing functions like customer service, product recommendations (for example, Youtube, Spotify, and Netflix recommendations), and internal applications inside organizations to help speed up processes and reduce manual workloads.
A major part of what makes machine learning so valuable is its ability to detect what the human eye misses. Machine learning models can catch complex patterns that would have been overlooked during human analysis.
Thanks to **cognitive **technology like natural language processing, computer vision, and deep learning, machine learning is freeing up human workers to focus on tasks like product innovation and perfecting service quality and efficiency.
You might be good at sifting through a massive but organized spreadsheet and identifying a pattern, but thanks to machine learning and artificial intelligence, algorithms can examine much larger sets of data and understand patterns much faster, saving loads of time, also without reducing quality.
With all the hype regarding Web 3 and Web 5, there must be some usecase of Machine Learning in it? Right...
We can create many better machine learning models. Utilizing blockchain technology's decentralized data architecture feature would help it. ML models can use the data stored in the blockchain network to make predictions or analyze data. Storing data on the blockchain network also helps reduce ML model errors.
Companies that use blockchain for data trade all over the world can also improve service speed by utilizing the blockchain’s ML models. Whereas the ML models’ job is to control the data’s trading routes. It can also help in data validation and encryption.
Data created by devices and saved on the blockchain is accessible to all blockchain nodes. This raises the possibility of a privacy issue for data kept private or confidential. Private blockchains, regulated access, and encryption could help to ease such problems. Yet, the use of ML models on such restricted data imposes limitations on prediction and analytics.
Combining blockchain with machine learning complements each other excellently. They are the two pillars upon which future breakthroughs will work on. Together, these two are bound to provide ground-breaking breakthroughs. Also, they would increase the security of our presence.
Most machine learning engineers use Python Programming Language, but of course, there are plenty of other language possibilities as well, depending on the type of model or project needs. Machine learning and AI tools are often software libraries, toolkits, or suites that aid in executing tasks. However, because of its widespread support and multitude of libraries to choose from, Python is considered the most popular programming language for machine learning.
In fact, according to GitHub (GitHub is a code hosting platform for version control and collaboration. It lets you and others work together on projects from anywhere.), Python is number one on the list of the top machine learning languages on their site. Python is often used for data mining and data analysis and supports the implementation of a wide range of machine learning models and algorithms.
Supported algorithms in Python include classification, regression, clustering, and dimensionality reduction. This is so because Python supports many Machine Learning Libraries like Sciket-Learn and Scipy, which help in developing such algorithms. Many are already pre-coded, so you just have to give the input and use the pre-coded algorithms, making your life much more straightforward.
What are the best recourses to learn Machine Learning?
Well, I’m presuming you’re 13, 14, or 15 years old. If that is so, then you probably will not be familiar with Linear Algebra, Calculus, Statistics, and Probability. But for right now, I don’t think it’s needed unless you are wanting to know what’s going on behind the scenes. If not, then just basic math will do the trick. If you are older, you might know this all already!
First of all, you should start learning a Programming Langauge, I’d recommend Python over all of them out there, due to its simplicity and capabilities. Then you could move on to a Data Science Course, there is this amazing course by IBM on Cognitive AI (Both on Python and Data Science). It is one of those courses, which provide a certificate of accomplishment for free. It also has a Machine Learning course, which is undoubtedly one of the best! Also how could I forget the Holy Grail of Machine Learning Courses, by Andrew Ng on Coursera!
Then you could go on and learn Deep Learning via Pytorch or Tensorflow or Keras for many different tasks, like Natural Langauge Processing(VOICE TO TEXT) and Image Recognition, kinda like the Siri on the iPhone and Google Assistant on Android.
- Machine Learning is just trying to immatate the human brain in a computer. To learn from mistakes and past experiences or HISTORICAL DATA.
- It is widely used everyone, from your home to massive data centers. It is undoubtedly the future.
- Python is the most used language in AI, it is easy, concise, to the point and very begginer freindly.
- If you want a decent amount of salary, AI is the way to go!
- There is a huge community, to always lend a hand when you're stuck! Keep Learning!!!
The last thing I want to say is that don’t work hard — work SMART and be passionate about your goals, If you are younger, don’t get overwhelmed by all of this — just start learning and move on STEP BY STEP!
I hope you benefitted from this reading and I’ll see you in the next one! Have a great day!