product recommendations in online shopping platforms sentiment and emotion analysis anomaly detection fraud detection and prevention content recommendation (newsmusicmoviesand so onweather forecasting stock market forecasting market basket analysis customer segmentation object and scene recognition in images and video speech recognition churn analytics click through predictions failure/defect detection and prevention -mail spam filtering summary the intent of this was to get you familiarized with the foundations of machine learning before taking deep dive into machine learning pipelines and solving real-world problems the need for machine learning in today' world is introduced in the with focus on making data-driven decisions at scale we also talked about the various programming paradigms and how machine learning has disrupted the traditional programming paradigm next upwe explored the machine learning landscape starting from the formal definition to the various domains and fields associated with machine learning basic foundational concepts were covered in areas like mathematicsstatisticscomputer sciencedata sciencedata miningartificial intelligencenatural language processingand deep learning since all of them tie back to machine learning and we will also be using toolstechniquesmethodologiesand processes from these fields in future concepts relevant to the various machine learning methods have also been covered including supervisedunsupervisedsemi-supervisedand reinforcement learning other classifications of machine learning methods were depictedlike batch versus online based learning methods and online versus instance based learning methods detailed depiction of the crisp-dm process model was explained to give an overview of the industry standard process for data mining projects analogies were drawn from this model to build machine learning pipelineswhere we focus on both supervised and unsupervised learning pipelines we brought everything covered in this together in solving small real-world problem of predicting grant recommendations for students and building sample machine learning pipeline from scratch this should definitely get you ready for the next where you will be exploring each of the stages in machine learning pipeline in further details and cover ground on the python machine learning ecosystem last but not the leastchallengesand real-world applications of machine learning will give you good idea on the vast scope of machine learning and make you aware of the caveats and pitfalls associated with machine learning problems