Machine Learning is a subset of AI. That is, all machine learning counts as AI, but not all AI counts as machine learning. Machine learning refers to the system that can learn by themselves. Machine learning is the study of computer algorithms that comprises algorithms and statistical models that allow computer programs to automatically improve through experience. Here are the complete guides to start a career on Machine Learning.
- Start with the Basic
First, it is important to understand the complete workflow of Python and build a strong foundation in this. Having a good knowledge of the basic syntax, functions, loops, class, and objects is very necessary to move forward. Unless you know the basic syntax, it’s hard to implement anything. That said, don’t spend too long on this. The goal is to learn the very basics, so you know enough to start working on your own projects in your area(s) of interest.
- Get used to with Libraries
There is a vast array of libraries and modules available in Python. Mastering them based on your domain of expertise is necessary. They all have their use cases and functionalities in different domains. For instance, Machine Learning based libraries in Python include Pandas, Numpy, Scikit-learn, Scipy, Matplotlib, etc., Data Analytics based libraries include Bokeh, Matplotlib, PyPlot, etc. Anaconda is an open source distribution for Python and R for large scale data processing, scientific computing and predictive analytics.
- Theory or Mathematics ?
Implementing built-in models with frameworks like Keras, Tensorflow, and Scikit is not unique and special, especially, when we are following an online tutorial and simply changing the data to ours. Coming up and understanding the things behind those architectures is extremely hard. We need a full understanding of probability and statistics, information theory, calculus, etc to just grasp the main ideas.
- Start Doing Projects with Further Learning
Start working on the projects along. Define a problem, prepare datasets, evaluate using the algorithms, keep tracks of the result and keep trying to find the optimal result. You will make mistakes, get stuck many times, but gradually you will find ways to come out of your problems. On the journey of finding answers to your queries you will learn new things and here the real learning will start.
- Participate in Hackathon, Coding Exams
Keep increasing the difficulty and scope of your projects. If you’re completely comfortable with what you’re building, it means it’s time to try something harder. Participate in coding Competitions, hackathons and other exams which can enrich your talent and help you to increase your efficiency.
AI components are made up of mathematically proven methods or implementation which are coded to work on data. The use of AI depends upon the type of problems they are designed to work on. At first, you need to go through scratch and you need to learn mathematics behind them. Then you can code those algorithms on your own from Scratch and compare the result with the built-in algorithm of scikit-learn.
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