Practical Data Science made Simple

The fundamentals of data science, exploratory data analysis, statistical methods, the role of data, the Python programming language, the difficulties of bias, variance, and overfitting, selecting the appropriate performance metrics, model evaluation techniques, model optimization using hyperparameter tuning and grid search cross validation techniques, etc. are all covered in this course.

In-depth data analysis utilizing Python, statistical methods, exploratory data analysis, and a variety of predictive modeling techniques—including a variety of classification algorithms, regression models, and clustering models—will all be covered in this course. The use cases and situations for implementing predictive models will be covered.

For anyone new to Python, this course is a must-have. It goes over Python for Data Science and Machine Learning in great detail.

With fully developed projects and examples that walk you through the approaches of exploratory data analysis, model construction, model optimization, and model evaluation, the majority of this course is hands-on.

This course goes into great detail on how to teach exploratory data analysis using the Numpy and Pandas libraries. It also covers the Seaborn and Marplotlib Libraries for Visualization creation.

A lecture on Deep Neural Networks is also included, which includes a worked-out example of Image Classification using TensorFlow and Keras.