Practical Data Science Using Python.

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

Type
video
Category
Data
publication date
2022-08-24
what you will learn

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

duration
1786
key features
Detailed coverage of Python for data science and machine learning * Learn about model optimization using hyperparameter tuning * Learn about unsupervised learning using K-Means clustering
approach
Most of this course is hands-on; completely worked out projects and examples will take you through exploratory data analysis, model development, model optimization, and model evaluation techniques.
audience
This course is for Python, machine learning developers, data scientists, data analysts, and business analysts. This course will also be beneficial for aspiring data science professionals and machine learning engineers.

Exposure to programming languages will be useful.
meta description
Explore data science using Python, statistical techniques, EDA, NumPy, Pandas, Scikit Learn, and Statsmodel libraries and take your first step toward becoming a data scientist or a machine learning engineer.
short description
This course covers Python for data science and machine learning in detail and is for a beginner in Python. You will also learn about core concepts of data science, exploratory data analysis, statistical methods, role of data, challenges of bias, variance and overfitting, model evaluation techniques, model optimization using hyperparameter tuning, grid search cross-validation techniques, and more.
subtitle
Apply Data Science Using Python, Statistical Techniques, EDA, NumPy, Pandas, Scikit Learn, and Statsmodel Libraries
keywords
Data Science, Machine Learning, Analytics, Matplotlib, Python, EDA, Bias, Variance and Overfitting, Performance metrics, model evaluation, model optimization, regression models, clustering models
Product ISBN
9781804611814