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Demonstrates a working example of every concept, making it easier to relate to. – All the modules are followed with practical projects that give you the opportunity to integrate and apply the covered theory. – Access the lessons for free during the trial period to check if it suits your learning style. Once you have a strong footing in the content covered in the lectures, you can move on to real-life case studies to get an understanding of how such challenges are dealt with, in daily scenarios. I originally just wanted to learn to program, without true goal, now I have one thanks!! To make the most of the content covered you are expected to have an intermediate level of programming experience in Python. – Write efficient code in Python to analyze challenges and engineer appropriate features. WCM Coordinator at Canpack India Pvt. 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Ltd.(Six Sigma Black Belt Batch), CoE Supply Chain Management Business Processes, Bayer Pharmaceuticals, Berlin (Six Sigma Black Belt Batch), Training & Quality Manager, Working at Tata Communications Ltd, Get Free Career Advice By Putting Your Mobile Number Below, Our Counselor will get back to you, You can Directly Contact Our Counselor On Below Help-Line, Post Graduate Program In Data Engineering, Six Sigma Green Belt Certification Online, Six Sigma Master Black Belt Certification, Certificate Course in Data Analytics using R Programing, Certificate Course in Data Visualizaton using Power BI, Certificate Course in Data Visualiztion using Tableau, Certificate Course in Machine Learning using Python, Post Graduate Program in Data Engineering, Applying Business Intelligence via Statistical Modeling, Matplotlib & Seaborn for Data Visualization, Pandas & Numpy for Data Preprocessing & Cleanup, Scikit-learn for Machine Learning Modeling,  Understanding End-to-End Small Machine Learning Project,  Draw plots using seaborn for gaining data insights, Doing Data Scaling, Fill Missing Values using Pandas, Choosing machine learning algorithm for modeling,  Improving accuracy by feature engineering,  Hyper-Parameter Fine Tuning to improve accuracy,  Understanding metrics of Model Evaluation,  Trying different models & creating final ensemble model, Different trade-offs while deciding the final ML Model, Model Complexity vs Model Interpretability. 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