One form of artificial intelligence (AI) that enables computers to learn and make judgments without explicit programming is machine learning (ML). It entails supplying data to algorithms so they can spot trends and forecast fresh data. Natural language processing, recommender systems, and images and speech recognition are just a few of the many uses for machine learning. Traditional programming cannot solve or forecast complicated problems; machine learning can learn and train from data. It helps us make better decisions and find efficient solutions to challenging business issues. Applications of machine learning can be found in many domains, including healthcare, finance, education, sports, and more. The objectives of this book are: i. To comprehend the fundamental ideas behind machine learning. ii. Should be capable of formulating machine learning challenges that align with various applications. iii. To comprehend various machine learning algorithms and their advantages and disadvantages. iv. The capacity to use machine learning methods to resolve somewhat complex issues. v. Optimize the learnt models and report on the expected accuracy that can be attained by applying the models to a real-world problem.