Overview
    Today, more than ever, businesses realize the importance of Machine Learning and Artificial intelligence to stay ahead in the cut-throat competitive market. By leveraging AI and ML technologies, businesses are diving deep into data to achieve their core goals, gain actionable insights, foster data-driven decisions, boost cost-efficiency, increase ROI, and most importantly, to create innovative products/services to cater to the specific tastes of their customer groups.
    Here a few ways in which ML and AI applications are helping businesses grow:
    • Predictive maintenance.
    • Automated data entry.
    • Spam detection.
    • Fraud detection and prevention.
    • Sentiment analysis.
    • Product recommendation lists.
    • Customer lifetime value prediction.
    • Enhancing customer satisfaction.
    • Strengthening cybersecurity and preventing data breaches.
    As for the future and scope of Machine Learning & AI, there are predictions that by 2020, AI will generate around 2.3 million job opportunities. According to another report, by the end of 2022, the global ML industry will stand at USD 9 billion, growing at an incredible CAGR of 42%. Although the adoption of AI and ML are at a nascent stage in India, essential steps are being taken to boost the AI and ML foothold in India.
    Artifical Intellgence is a study or a concept of developing smart machines, using technological means. Machine learning is one those means. ML is one of the ways we can execute the concept of AI. Machine Leaning is a subset of Artifical Intellgence. Machine Learning is the study of computer algorithms that allow computer programs to automatically improve through experience.
    Just like ML is the subset of AI, Deep Learning is a subset of Machine Learning. Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as a nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.

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    The path to becoming a Machine Learning Engineer is pretty straightforward:
    • Get a Bachelor’s/Master’s degree in Computer Science/Data Science/Statistics/Software Engineering.
    • Choose a programming language and master coding in it. For beginners, Python is an excellent choice owing to its simple syntax and easy learning curve.
    • Sharpen your knowledge of Statistics and Probability concepts as they are the foundation of many ML algorithms.
    • Brush up on the fundamental concepts of Software Engineering.
    • Gain data modelling and evaluation experience.
    • Learn how to work with different ML algorithms and libraries.
    • Learn how to use advanced signal processing techniques for feature extraction.
    • Start small and build your own ML projects.
    • Participate in online coding competitions and hackathons. Kaggle is a great platform to start.
    • Read journals, articles, blogs, books, newsletters, etc., to stay updated on the latest advances in ML and AI.
    • Attend Data Science conferences, seminars, webinars, and local meet-ups to connect with people from the field.

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