Machine learning is an area of artificial intelligence and computer science that covers topics such supervised learning and unsupervised learning and includes the development of software and algorithms that can make predictions based on data.

Machine learning is all about making computers perform intelligent tasks without explicitly coding them to do so. This is achieved by training the computer with lots of data. Machine learning can detect whether a mail is spam, recognize handwritten digits, detect fraud in transactions, and more.

machine learning remains a relatively 'hard' problem. There is no doubt the science of advancing machine learning algorithms through research is difficult. It requires creativity, experimentation and tenacity. The difficulty is that machine learning is a fundamentally hard debugging problem.

we have covered some of the the most important machine learning algorithms for data science: 5 supervised learning techniques- Linear Regression, Logistic Regression, CART, Naïve Bayes, KNN. 3 unsupervised learning techniques- Apriori, K-means, PCA

On one hand, data science focuses on data visualization and a better presentation, whereas machine learning focuses more on the learning algorithms and learning from real-time data and experience

1) Naïve Bayes Classifier Algorithm
2) K Means Clustering Algorithm
3) Support Vector Machine Algorithm
4) Apriori Algorithm
5) Linear Regression Algorithm
6) Logistic Regression Algorithm