LEARNING PATH: Statistics for Machine Learning

Course description
Machine learning worries a lot of developers when it comes to analyzing complex statistical problems. Knowing that statistics helps you build strong machine learning models that optimizes a given problem statement. This Learning Path will teach you all it takes to perform complex statistical computations required for machine learning. So, if you are a developer with little or no background in statistics and want to implement machine learning in their systems, then go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.  The highlights of this Learning Path are: Learn Machine learning terminology for model building and validation Explore and execute unsupervised and reinforcement learning models You will start off with the basics of statistical terminology and machine learning. You will perform complex statistical computations required for machine learning and understand the real-world examples that discuss the statistical side of machine learning. You will then implement frequently used algorithms on various domain problems, using both Python and R programming. You will use libraries such as scikit-learn, NumPy, random Forest and so on. Next, you will acquire a deep knowledge of the various models of unsupervised and reinforcement learning, and explore the fundamentals of deep learning with the help of the Keras software. Finally, you will gain an overview of reinforcement learning with the Python programming language. By the end of this Learning Path, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Meet Your Expert: We have the best works of the following esteemed author to ensure that your learning journey is smooth: PratapDangeti develops machine learning and deep learning solutions for structured, image, and text data at TCS, analytics and insights, innovation lab in Bangalore. He has acquired a lot of experience in both analytics and data science. He received his master's degree from IIT Bombay in its industrial engineering and operations research program. He is an artificial intelligence enthusiast. When not working, he likes to read about next-gen technologies and innovative methodologies




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