A comprehensive introduction to data science and machine learning.

The topic including: Introduction to Data Science, Basic Python, Visualizing Data, Linear Algebra, Statistics, Probablility, Hypothesis and Inference, Gradient Descent, Getting Data from other format, Machine Learning, k-Nearest, Navie Bayes, Simple Linear Regression, Multiple Regression, Logistic Regression, Decision Trees, Neural Networks, Deep Learning, Clustering, Natural Language Processing, Network Analysis, Recommender Systems, Databases and SQL, MapReduce and Data Ethics.

The topic including: Basics NumPy, pandas, Data Loading, Data Cleaning and Preparation, Data Wrangling, Data Visualizing, Data Aggregation, Time Series, Statiscal modeling and provides practical examples and case studies to demonstrate how to apply these concepts in real-world scenarios.