Python ppt created on machine learning.pptx

Python ppt created by our group on machine learning.

1. Python Submitted To :- Dr. Ruchi Kawatra Submitted by :- Tanshul Deshwal(10322210047) Jai Kishan (10322210023) Javied Rain(10322210010)

2. TOPIC : MACHINE LEARNING

3. INTRODUCTION :- The goal of machine learning, a branch of artificial intelligence, is to create systems that can process and interpret data to learn new things. To assist you in understanding the fundamentals, here is a more structured introduction.

4. What is Machine Learning? Machine learning (ML) involves training algorithms to recognize patterns and make predictions or decisions without being explicitly programmed for each specific task. It essentially allows machines to learn from past experiences (or data) and apply this learning to new, unseen scenarios.

5. How Does Machine Learning Work? Data Collection Preprocessing Model Selection Training Evaluation Hyperparameter Tuning Deployment

6. Data Collection : He process starts with gathering large amounts of data relevant to the task. This data can be anything from images and text to numbers and real-world signals.

7. Preprocessing : Data is cleaned and organized. This step may involve removing errors, filling missing values, and converting data into a format suitable for analysis.

8. Model Selection : Depending on the problem, a suitable algorithm is chosen. Algorithms can range from simple linear regression to complex neural networks.

9. Training : The selected model is trained using a large portion of the data. During training, the model gradually improves its ability to predict or categorize based on input data.

10. Evaluation: The model's performance is tested using a separate set of data (the test set). This helps to check the accuracy and effectiveness of the model.

11. Hyperparameter Tuning : Hyperparameter tuning is the process of optimizing the hyperparameters of a machine learning algorithm improving the accuracy of machine learning models using random search , grid search, and hyper opt optimization methods .

12. Deployment : Once trained and evaluated, the model is deployed in real-world applications where it can provide predictions or automate decisions based on new data.

13. Types of Machine Learning :

14. Supervised Learning: The model learns using labeled data. It's like learning with a teacher to guide the learning process. Unsupervised Learning : The model learns from data without labels. It tries to identify patterns and relationships in the data on its own. Reinforcement Learning : The model learns to make sequences of decisions by receiving feedback in terms of rewards or penalties.

15. Applications of Machine Learning : Healthcare Finance Autonomous Vehicles Recommendation Systems Fraud Detection Self Driving Cars Medical Diagnosis Stock Market Trading

16. Conclusion : In conclusion, machine learning stands as a transformative force reshaping industries and revolutionizing the way we approach complex problems. Its ability to extract insights from vast datasets, adapt to changing environments, and automate tasks previously thought impossible has made it a cornerstone of innovation in fields ranging from healthcare to finance to transportation.

17. Thank you