Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to “learn” (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.

Machine learning is closely related to computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioural profiles for various entities and then used to find meaningful anomalies.

Content: 

1. Python with Mysql connectivity
2. Introduction of Machine Learning
3. Machine Learning Techniques

• Supervised Learning
• Unsupervised Learning

4. Supervised Learning

• Regression
• Classification

5. Supervised Learning – Regression
6. Supervised Learning – Classification
7. Unsupervised Learning

• Clustering
• Deep Learning

8. Classification Algorithms

• Tree-based
• Linear and other classifiers

9. Data Compression and Visualization
10. Anomaly Detection
11. Implement some libraries and perform

Mathematical computation

• Matplotlib
• Scipy
• Numpy
• Tensor Flow
• Keras
• Opencv
• Google API