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Data Science Projects with Python training course is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The course will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you’ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. By the end of this course, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from data.
Private Team Training is available for this course
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Lesson 1: Data Exploration and Cleaning
Lesson 2: Introduction to Scikit-Learn and Model Evaluation
Lesson 3: Details of Logistic Regression and Feature Exploration
Lesson 4: The Bias-Variance Trade-off
Lesson 5: Decision Trees and Random Forests
Lesson 6: Imputation of Missing Data, Financial Analysis, and Delivery to Client
We deliver this course either on or off-site in various regions around the world, and can customise your delivery to suit your exact business needs. Talk to us about how we can fine-tune a course to suit your team's current skillset and ultimate learning objectives.
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