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Describe the behavior of data in Python models, Understand how to use the various Python libraries to manipulate data, like Numpy, Pandas and Scikit-Learn, Use Python libraries and work on data manipulation, data preparation and data explorations, Introduction to Machine Learning and It’s Technologies, Segment - 02-introduction-to-data-science-fin, Segment - 05-problem-definitions-and-collecting-data, Segment - 06-data-pipelines-preparation-cleaning-understanding, Segment - 07-model-building-validation-visualization-data-science-applications, Segment - 08-data-science-methodology-data-analytics-tools-open-source-tools, Segment - 09-data-science-future-readings, Segment - 10-ai-primer-and-machine-learning-concepts, Segment - 11-machine-learning-applications, Segment - 12-machine-learning-supervised-unsupervised, Segment - 12-types-of-machine-learning NUMBERING ISSUE, FIX, Segment - 13-supervised-unsupervised-learning-methodology-clustering, Segment - 15-tools-for-scalable-machine-learning, Segment - 20-introduction-to-python-notebook, Segment - 22-introduction-ids-and-juypter-notebook, Segment - 23-lab-tutorials-learning-juypter-notebook, Data Analysis using Pandas and Data Visualization, Segment 31 -review-session-python-for-data-science, Segment 35 - NumPy Array vs. Panda Series, Segment 39 - Dataframe Operations (Continued), Segment 40 - Statistical Analysis, Calculations and Operations, Segment 41 - Lab - Advanced Operations in Action, Segment 42 - Lab - Advanced Operations in Action (Continued), Segment 43 - Pandas Visualization and Matplotlib, Supervised (Regression and Classification) & Unsupervised (Clustering) Machine L, Segment 48 - Introduction to Scikit-Learn, Segment 49 - Scikit-Learn Uses and Applications, Segment 50 - Scikit-Learn vs. Other Tools, Segment 51 - Scikit-Learn Classes, Utils and Data Sets, Segment 53 - Preprocessing and Feature Engineering, Segment 57 - Principal Component Analysis, Segment 58 - Lab - Classification Algorithm, AWS Certified Solutions Architect - Associate, New Python developers looking to quickly develop and keen understanding of the power of Python, Early stage users of Python who need to use Python in serious, enterprise level applications, Individuals who are familiar with data science and need to understand the optimal uses for Python. 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