This Course will design to understand Machine Learning Algorithms with case Studies using Scikit Learn Library. The Machine Learning Algorithms such as Linear Regression, Logistic Regression, SVM, K Mean, KNN, Naïve Bayes, Decision Tree and Random Forest are covered with case studies using Scikit Learn library. The course provides path to start career in Data Science , Artificial Intelligence, Machine Learning. Machine Learning Types such as Supervise Learning, Unsupervised Learning, Reinforcement Learning are also covered. Machine Learning concept such as Train Test Split, Machine Learning Models, Model Evaluation are also covered.

A subfield of artificial intelligence (AI) and computer science called machine learning focuses on using data and algorithms to simulate how humans learn, gradually increasing the accuracy of the system.

With the use of machine learning (ML), which is a form of artificial intelligence (AI), software programmes can predict outcomes more accurately without having to be explicitly instructed to do so. In order to forecast new output values, machine learning algorithms use historical data as input.

Machine learning is frequently used in recommendation engines. Business process automation (BPA), predictive maintenance, spam filtering, malware threat detection, and fraud detection are a few additional common uses.

Machine learning is significant because it aids in the development of new goods and provides businesses with a picture of trends in consumer behaviour and operational business patterns. A significant portion of the operations of many of today's top businesses, like Facebook, Google, and Uber, revolve around machine learning. For many businesses, machine learning has emerged as a key competitive differentiation.