In this course, you will learn about core concepts of data science, exploratory data analysis, statistical methods, role of data, Python language, challenges of bias, variance and overfitting, choosing the right performance metrics, model evaluation techniques, model optimization using hyperparameter tuning and grid search cross validation techniques, and more.
You will learn how to perform detailed data analysis using Python, statistical techniques, and exploratory data analysis, using various predictive modeling techniques such as a range of classification algorithms, regression models, and clustering models. You will learn the scenarios and use cases of deploying predictive models.
This course also covers classification using decision trees, which include the Gini index and entropy measures and hyperparameter tuning. It covers the use of NumPy and Pandas libraries extensively for teaching exploratory data analysis. In addition, you will also explore advanced classification techniques and support vector machine predictions. There is also an introductory lesson included on Deep Neural Networks with a worked-out example on image classification using TensorFlow and Keras.
By the end of the course, you will learn some basic foundations of data science using Python.
All resources and code files are placed here: https://github.com/PacktPublishing/Practical-Data-Science-using-Python
Learn all about exploratory data analysis (EDA)
Explore various statistical techniques
Understand Dimensionality Reduction Techniques (PCA)
Learn about feature engineering techniques
Learn about data science use cases, life cycle and methodologies
Learn about Deep Neural Networks
Exposure to programming languages will be useful.