Artificial intelligence is one of the most significant discoveries of the technological era and a powerful up-and-coming area of the IT industry. Entering the realm of Artificial Intelligence paves way to a pool of opportunities, seeing its presence in varied range of application areas from education to healthcare. Once you learn the basics of AI, you are ready to be a part of the mainstream, equipped with the capability of making machines mimic the human behaviour. Machine Learning is a subset of AI that uses data to solve tasks. It is the most sought-after jobs in industry and the market is witnessing alarming demand for professionals with AI skillset. The pinnacle global firms like Microsoft, Google, IBM, Amazon have AI Engineers and Data Scientists on their top hiring priority. The field that is making a machine intelligent is being served with multidisciplinary elements suiting the electronics, electrical and computer professionals fairly.
If you understand the business and you are a tech-inquisitive beginner, you’re set to enter the field.
Let’s get you oriented on how to start learning AI in 6 easy steps:
1.Choose a Programming Language
AI is a field that is prospectively replicating human intelligence and for actuating the systems based on Artificial Intelligence, a know-how of at least one programming language becomes essential. There are several languages you can add to your tech-kit that includes LISP, C++, Java, SQL, R, and Python. Python has gained its popularity in the tech-community with ample support to machine learning & Neural Network facets. If you are a non-programmer or looking for a start in AI, Python can be your first take. This favoured language supports rich and flexible libraries like Pandas, Sci-kit learn, NumPy, matplotlib and many more. Python is one of the easiest to learn programming language, acknowledged in the AI community for its simplicity and minimal codes.
2.Touch up with Mathematical Modelling & Statistics
Mathematics has an important role to play in the field of AI. The preliminary ground of artificial intelligence is to create an admissible model of human conception. And these models can be adapted with ideas and techniques from various Mathematics classifications. This would seem appalling as mathematics is a broad field to look at. However, you can consider taking up the essential topics rather than the entire subject on whole. Learn the basic requisites of mathematical facet and explore the unknown as and how you start delving into the field.
Let’s look at some important mathematical concepts you can start with:
• Statistics, Probability, Distributions
• Linear Algebra with Vectors, Matrices, PCA (Principal Component Analysis)
• Functions, Gradients
3.Explore the world of Data
Data preparation and exploration is an important part of Artificial Intelligence. AI and Data is emerging as blended terminologies with intensive interdependence, where application of AI requires big data and getting the knack of data is baffling without AI. To witness the prediction capability of AI and data together in every business area is fascinating. Mapping to the goals of AI that includes reasoning, natural language processing, learning autonomy, robotics, computer Vision and Image Processing requires massive amount of data handling & interpretation. Learning the data essentials will ease out the way into Artificial Intelligence. Explore the data on the following lines to make data suitable for AI adoption.
There are several secondary sources of dataset available on internet. Probe into the famous datasets available like Titanic, CIFAR, MNIST, etc. with some famous portals like Kaggle and start applying data manipulation and modelling.
Data preparation is required before being employed for further scrutiny. It is the stage where raw data is transformed into some useful information with analysis for completeness, errors, and redundancy. For example, the below data set is accustomed to various discrepancies that needs to be rectified before taking it to processing stage.
Exploratory Data Analysis & Data Processing
Delve into the Employment of Machine Learning Algorithms (discussed in further sections) to generate patterns and meaningful insights from the prepared data.
Data Interpretation & Communication
Powerful Visualization is an important part of data exploration that can give comprehensible results and outputs.
4.Build AI Models & Crack the Machine Learning Ways
AI Modelling refers to training a set of data to identify the existing patterns by applying algorithms and reasoning. Machine Learning is the subset of AI. It The field of ML makes the machine capable of human intelligence. It uses statistical methods, making the machine learn with experience. From Netflix to Amazon Alexa employs Machine Learning Algorithms. The 3 major Algorithms to be taken up are Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Select the right model, train it with the data set, apply optimization and validate the model. To deep dive into ML, enter the deep learning facets for which Neural Network forms the foundation.
5.Take up Kaggle Mini Projects
Take up a mini project on Kaggle and apply all that you learn during the course of Artificial Intelligence. Kaggle has gained its popularity amongst the knowledge community that allows users to explore the data sets, build models, work with other AI professionals, and enrol in competitions to solve data driven challenges.
6.Signup for GitHub
Join Millions of AI developers, build, and maintain your solutions on GitHub. It is the largest and most advanced development platforms in the world. Learn, Collaborate, and showcase your learning journey in your field. Start posting your projects and notify the world on your presence with this buzzing technology.
Start small, understand data, build a model, and visualize the result!
Kickstart your journey into the field of Artificial Intelligence Training today and learn the basic skillset required to make a machine mimic human behaviour. Equip yourself with the powerful technology and enter the future dominated by autonomy of AI.
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