You might have seen plenty of roadmaps on AI. Most of those are quiet vast and learners get confused while trying to learn all of these concepts. In this blog, I've cut short all the key topics which would help learners to master the domain of Artificial Intelligence and see what roles require what all skills. I'll also be sharing resources which were useful during my learning phase.
Mathematics is the key concept in Artificial Intelligence. You don't need to solve any mathematical problem unless you're studying for your university exam. While doing projects, all the mathematics runs at the backend of the software. So you just need to be aware of the following concepts.
Excel is one of the key tools in data science which allows users to create data. Excel also provides users with sorting, filtering and summarizing data makiing it easy for data analysis tasks.
Excel also contains built in functions to perform mathematical, data transforming and logical operations in a single command.
Excel provides various data visualization tools to create charts, graphs, and other visual representations of data. These visualizations help analysts understand trends, patterns, and relationships within the data, making it easier to interpret and communicate insights.
After learning mathematics, it is essential to have a knowledge of a programming language in artificial intelligence.
You can learn Python, R or Scala language in Artificial Intelligence.
Python language is usually preferrable as it has libraries which are useful in every domain such as machine learning, deep learning, natural language processing, computer vision and even web and app development! If you want to create a software or an application out of your AI product then Python is very useful since its libraries can integrate the application and the machine learning model.
R language is another good option but it limits it applications only in the field of machine learning and deep learning. In most of the industries Python is preferred due to its versatility. R is not much used in other domains that I mentioned in Python as it's not well documented.
Data Science is a domain that is common in all AI fields. It includes the following steps
The most essential skill is to learn machine learning algorithms. Machine learning is a subset of artificial intelligence that trains the dataset and predicts values based on the trained dataset. There are three types of machine learning algorithms.
Some algorithms which are used in both classification and regression:
Deep learning is a subset of machine learning that makes use of artificial neurons called neural networks that mimic the human behaviour. It is most commonly used for image datasets to identify the characteristics of images. The following concepts are must while learning deep learning.
Natural Language Processing is another subset of machine learning that combines the concepts of deep learning to enable computers to understand and generate human language.
NLP makes use of the NLTK and the SpaCy libraries which offer a wide range of tools and functionalities for various NLP tasks, including text cleaning, tokenization, part-of-speech tagging, parsing, named entity recognition, and sentiment analysis.
NLP is also useful for the creation of chatbots.
Computer vision is an interdisciplinary field that focuses on enabling computers to understand and interpret visual data from digital images and videos. It seeks to replicate human visual capabilities, allowing computers to identify objects, track movements, and analyze scenes.
OpenCV library of python can be used for learning Computer Vision.
Business intelligence tools are software applications that collect, analyze, and visualize data to help businesses make better decisions. They are majorly used for creating charts, graphs, dashboards and generate reports that provide analysis of the key business metrics.
Popular business intelligence tools include Power BI, Tableau, Google Data Studio. Learn any one of these to master business intelligence.
Big Data Analytics should be placed way higher since it doesn't have much relation with machine learning but people usually prefer to learn it at the end by prioritizing machine learning concepts.
The AI model is trained using Data. And if the data size is big, then you should know Big Data Tools to manage this huge amount of Data.
Big companies like YouTube and Google are using recommendation systems to recommend something based on the previous search history, this is the blend of AI and Big Data.
Hadoop, Spark, MongoDB are some of the Big Data tools. You can learn any one of these.
That's all about learning Artificial Intelligence. Make sure you note down the concepts while learning and practice the concepts by building projects and products from the resources you learn. Also public datasets are available for projects and research on platforms such as Kaggle, UCI Machine Learning Repository, Datahub.io, etc
As mentioned earlier, we'll discuss about industrial roles in artificial intelligence and the skills required for each role.
For any queries drop down a message at any of my social handles mentioned in the footer. Happy learning! :)