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People think Artificial Intelligence is a new technology. But it was introduced by John McCarthy in 1956. It is a new demand in the present as well as in the future. AI is the future.
Python is the best-suited coding language for AI. Python is most popular because for easy to learn and use, is platform-independent, has pre-built libraries, less code, has community support, and many more.
If you are keen to learn AI, then you are at the correct place because the python AI ML course in Mumbai takes care of your every basic need to make smarter in this specialised technology, which is most demanding. Help you to Build AI Models with Python.
Let us begin:
Assume you’ve been given a problem that needs to be solved using AI to understand the process better.
- Define the Problem objective:
Let us take the example of weather forecasting, whether sunny, foggy, or cloudy outside. The main concern is to predict the situation. We need to follow the steps: what would be the requirement to get these outputs?
- Gathering data:
We must think of “what and how” scenarios in this stage. Need to collect information from different sources like web scrolling or doing it manually. Many sources are available on the internet, and you have to check for the following: humidity level, temperature, pressure, and locality.
After collecting data, further processing can be continued.
- Preparing data:
Now, in this step, you have to scan the data you have collected from a different source, which is the correct data for your location. You have to check it by hit and trial methods.
- EDA( Exploratory Data Analysis) :
You have to find the treasure by correlating the data in these steps. For example: If it is sunny and not cloudy, then it wouldn’t rain. But if it’s bright and grey, there are chances of rain.
- Building a model:
All the data collected in the above steps will be merged here. Now training and testing of data will be done. Training data is used in building and analysing data. There are several algorithms. The tricky part is choosing the suitable algorithm, which depends on the data type.
- Evaluation & Optimization of data:
Once the model is built, it’s time to implement it, and testing will be done in this phase. Further, changes can be made depending on the requirements. Cross-validation can be done for improvement.
- Predictions
After the evaluation of data, the next crucial step is prediction. The final output can be discrete or continuous. In this example, the rainfall data is discrete type (true or false).
