Day 32/100 of @codewithankitto Learning Data Science #dataanalysis #datascience #python #coding

Day 32/100 of My Data Science Learning Journey

On Day 32, I delved into exploring the diverse applications of ML in B2B business models. I delved into various case studies and techniques like Sentiment Analysis. Additionally, I learned about the Machine Learning Development Life Cycle (MLDLC).

The MLDLC represents the iterative process of building effective ML projects. It comprises seven crucial steps:

1. Gathering Data: Collecting diverse and relevant data.
2. Data Preparation: Cleaning and organizing data for analysis.
3. Data Wrangling: Further processing and refining data.
4. Analyzing Data: Exploring and understanding the data.
5. Training the Model: Developing and fine-tuning the ML model.
6. Testing the Model: Evaluating the model's performance.
7. Deployment: Implementing the model into production.

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