Machine Learning College Project BCA.pptx

ML ppt

1. Reimagining Possibilities with Machine Learning

2. Introduction What is machine learning? 20 NOVEMBER 2023 Aditya Kumar Mach ine learning is a branch of  artificial intelligence (AI)  and computer science which focuses on the use of data an d algorithms to imitate the way that humans learn, gradually improving its accuracy. IBM has a rich history with machine learning. One of its own, Arthur Samuel, is credited for coining the term, “machine learning” with his  research  (link resides outside ibm.com) around the game of checkers. Robert Nealey, the self-proclaimed checkers master, played the game on an IBM 7094 computer in 1962, and he lost to the computer. Compared to what can be done today, this feat seems trivial, but it’s considered a major milestone in the field of artificial intelligence.

3. Applications Use of machine learning in various industries Real-world Solutions powered by artificial intelligence Examples 20 NOVEMBER 2023

4. Algorithms Labelled training data used Supervised Patterns and relationships discovered Unsupervised Rewards and feedback based Reinforcement Complex neural networks employed Deep Learning 20 NOVEMBER 2023

5. Supervised Learning Training Supervised learning is the process of training a machine learning model using labeled data, where each input data point has a corresponding target label. By providing the model with these labeled examples, it learns to make predictions or classifications based on new, unseen data. 20 NOVEMBER 2023

6. Unsupervised Learning Without Unsupervised learning is a powerful technique in machine learning that enables training models without the need for labeled data. By leveraging algorithms and statistical techniques, we can extract meaningful patterns and relationships from unstructured or unlabeled data. 20 NOVEMBER 2023

7. Deep Learning AI Advancement The power of neural networks Data Transformation Enabling predictive analytics 20 NOVEMBER 2023

8. Challenges Simplify Complexity Quality Data Fairness Bias 20 NOVEMBER 2023

9. Ensuring fairness and accountability in ML 20 NOVEMBER 2023

10. Enhanced Prediction Automated Processes Personalized Experiences Ethical Considerations APRIL JAN FEB MAR MAY JUN JUL AUG SEPT OCT NOV DEC Future Trends

11. Key takeaways and final thoughts on ML 20 NOVEMBER 2023