Machine Learning Development Life Cycle | MLDLC in Data Science 

Machine learning has given computer systems the ability to automatically learn without being explicitly programmed. But how does a machine learning system work? So, it can be described using the life cycle of machine learning. Machine learning life cycle is a cyclic process to build an efficient machine learning project. The main purpose of the life cycle is to find a solution to the problem or project.
The machine learning life cycle involves seven major steps, which are given below:
1. Gathering Data
2. Data preparation
3. Data Wrangling
4. Analise Data
5. Train the model
6. Test the model
7. Deployment
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⌚Time Stamps⌚
00:00 - Intro
01:02 - Background of the Topic
01:40 - What is Software Development Life Cycle
04:40 - Framing the problem
06:00 - Gathering the Data
08:32 - Data Pre-Processing
10:25 - EDA
13:20 - Feature Engineering and Selection
15:43 - Model Training, Evaluation and Selection+
19:23 - Model Deployment
21:23 - Beta Testing
22:45 - Optimizing the Model