AI ML With Python

Python is the most popular programming language for artificial intelligence (AI) and machine learning (ML) development.
Let's explore basic workflow for Machine Learning and how Python is helpful in each phase.

1. Data Collection: First phase of ML where you have to collect data from different sources like Files, Web, API, and Databases. Libraries like BeautifulSoup, Scrapy, requests, SQLAlchemy, Psycopg2, and PyMongo help you collect data with ease.
2. Data Processing and Cleaning: Important phase where you make sure data is clean and correct. Libraries like Pandas and Numpy are useful for handling missing values, filter data, aggregations, numerical computations, and transformations.
3. Data Analysis and Visualisation: In this phase, we find data insights and patterns. Libraries like Matplotlib and Seaborn help to visualize the data by plotting graphs and charts.
4. Model Building: In this phase, we split the data into train and test, identify the right learning algorithm, and train the model with training data. Libraries like Scikit-Learn, TensorFlow, and PyTorch are famous for creating and training the models.
5. Model Evaluation: Here we evaluate the performance of the trained model using metrics like accuracy, precision, recall, F1 score, etc., and tune hyperparameters for optimized performance. Libraries like Scikit-Learn, Statsmodels, SciPy help you evaluate the performance of your models.
6. Model Deployment: Here we deploy our model to production. Python frameworks like Django, Flask, FastAPI help you to deploy your model for production use cases.
   

Python’s versatility and powerful libraries help you to build AI ML use cases across diverse industries like Healthcare, Finance, Retail, Logistics, Entertainment, Manufacturing, Energy, and many more. Drive innovation and efficiency with AI ML for your industry.