Not everyone can take the time to learn data science, AI, or ML in a classroom, nor can not everyone afford the costs of formal learning to do business. data science, AI or ML.
As mentioned earlier in the subhead, time and cost are the biggest hurdles people face when trying to learn data science, artificial intelligence, or machine learning. Self-study is an art that requires discipline, dedication and thoroughness to master. It gives you the flexibility to couple learning with work or school if you master it properly. However, when you start to learn data science, AI or ML the first few steps are very difficult but trust me when I say it’s worth worrying about. The key to making good progress when learning on your own is to study at your own pace. In this story, I’m going to share a path that people looking to learn about data science, AI, and ML can follow and make good progress in learning new things. I will also share links to resources that I have personally used and can recommend without a doubt.
As boring as it sounds, it is very essential in this area. I think it’s safe to assume that anyone reading this document has some basic or intermediate math knowledge since high school. It’s a good start, but it’s nowhere near enough in the realm of data science, AI, and ML. You would have to dive a little deeper and learn a few concepts in statistics, algebra, and other areas
Code As a beginner, don’t jump straight into learning to write code for machine learning but rather learn basic programming concepts in general. Learn what programming is, learn about existing types of code, and how to write code correctly. This is very important because you will learn many essential concepts that you will live with for the rest of your life in this area. Take it easy in this step, don’t rush to learn advanced stuff, understanding most of the things in this step will determine your skill level in the field. You will find here a great video resource that introduces you to programming and computers. This video introduces you to all the important concepts of programming and computing in general. Take your time and make sure you understand every part of it.
Get comfortable with a programming language
There are many languages that scientists, artificial intelligence, and machine learning engineers use today to do their jobs, the most widely used being Python, R, Java, Julia and SQL. There are many other languages that can be used, but the ones I have listed are the most used for a number of reasons.
They are easy to learn and quick to develop if you devote enough time to them and stay consistent.
They allow you to do more with less code.
They have a good, strong community built around them to help and support you whenever you have a problem.
They have almost every library and package you will need in your Data Scientist, AI, or ML engineer job.
They are open source and are free to use.
There is absolutely nothing wrong with learning more than one language, in fact, knowing more than one language is good. However, you should take your time when learning programming languages and try as much as possible not to learn more than one language simultaneously as this can confuse you and leave you disoriented for a while. Take your time, learn one language at a time, and make sure you only learn the parts of a language that you need for your career. I suggest you learn python before any other language, as it is a relatively easy language to understand. I also recommend that you learn general python before you start learning the python for data science and AI / ML.
Learn how to get data
More often than not the data will not be delivered to you conscientiously, sometimes there will be no data at all for you but anyway you have to find a way to get data with which you can to work. The organization you work with may have a good data collection system, if they have one, that is a plus for you. Otherwise, you have to find a way to get data, not just any data, but good data that you can work with to achieve your goal. Obtaining data does not directly involve data extraction, it is a process that falls under data extraction. You can get free, open source data from many places on the internet, and sometimes you will need to retrieve data from websites. Website scraping is very important and I implore everyone to learn it because a need for it may arise in your career as a Data Scientist, AI or ML engineer. You can find web scraping tutorials from Youtube. The data can also be kept in a database. So, as a Data Scientist, AI or ML Engineer, you need to know a little bit about database administration in order to be able to connect and work directly from a database. Knowledge of SQL is very essential at this stage.
Learn how to process data
This is most often called Data Wrangling. This process involves cleaning up the data you have available, which can be done by performing exploratory analysis of the data you have available and removing unwanted portions of your data. This process also involves structuring the data you own into a form that allows you to work. This step is the most exhausting part of working on a data science, artificial intelligence, or machine learning project. In the training process, most of the data samples you will be working with have been preprocessed, but in the real world, the data may not have undergone a processing step. As someone aspiring to be gifted in this area, you have to find real world data and bypass it. Real world data can be found almost anywhere, but Kaggle is an amazing place to get real world data from companies around the world. Wrestling or dealing with data is an extremely tiring task, but with constant dedication and consistency it can be very interesting for you.
Learn to visualize data
Being a Data Scientist, AI or ML Engineer does not necessarily mean that everyone in your workplace or on your team will be able to understand the technical aspects of your field or make inferences from the data under their control. rough form. This is why it is necessary to learn to visualize the data. Data visualization basically refers to the process of presenting data in graphical form so that anyone, regardless of their knowledge of data science, AI, or ML, can understand the meaning of the data. There are many ways to visualize the data. As programmers, writing code to visualize data should be our preferred method because it is fast and free. Writing code to visualize data can be done using many free and open source libraries that come with the programming languages we use. Matplotlib, Seaborn, and Bokeh are all python libraries that we can use to visualize data. You can find a video tutorial on visualizing data with matplotlib here . Another way to visualize data is to use closed source tools like Tableau . There are many closed source tools for data visualization and they are used to achieve more elegant and complex visualizations, but they come at a cost. Tableau is the most common and it is a tool that I personally use very often. Learning to use Tableau is something I recommend to everyone. You can find a great tutorial on using Tableau here.
Artificial intelligence and machine learning
Rather, artificial intelligence and machine learning are subsets of data science because they are powered by data. They refer to the process of training machines or other inanimate objects to behave like human beings by feeding them with well-processed data. Machines can be taught to do a lot of things that human beings can do by teaching them and gradually guiding them. Think of machines in this case as babies who have no knowledge at all but are gradually taught to identify objects, speak, learn from their mistakes, and become better. Machines can also learn to do most of these things the same way. AI and ML is all about bringing machines to life using a lot of mathematical algorithms. The full potential of artificial intelligence and machine learning is not yet known, as it is one of the areas in constant improvement. But for now, AI and ML are widely used for cognitive functions such as object detection and recognition, facial recognition, speech recognition and natural language processing, fraud detection and spam, etc.
Learn how to make your machine learning models available for use on the Internet
The models you build using machine learning can be made available to anyone on the internet by deploying them. To do this, a good understanding of web development is necessary, as you will need to create a web page or a group of web pages to host your model. Your website interface should also communicate with the interface that contains your template. For that, you also need to know how to build and integrate APIs to manage the communication between the front end of your website and the back end that hosts your machine learning model. You may need to have a good knowledge of cloud computing and DevOPs if you intend to deploy your machine learning models on a cloud server, through a pipeline or a docker container.
Find Yourself a Mentor
Learning on your own is amazing, but nothing beats learning directly from an industry professional. This is because you are learning real life concepts and other things that only practical experience can teach. There are many benefits to having a mentor, but not all mentors can have an impact on your career or your life as a whole.
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