Publisher's Synopsis
Are you looking for a beginner's guide?
Basics of Python for Data Analysis
- NumPy
- 2-D and 3-D arrays
- SciPy
- Linear Algebra
- Pandas
- Operations
- Python IDE
- Atom
- Eclipse
- Variables and Data Types
- Decision Making and Basic Operators
- Object Oriented Programming
- Regular Expressions
- Data Handling
- Load date from different server such as CSV, URL or SQL
- Python Aggregation
- Building Machine Learning Models
- Data Science
- Data Pipelines
- Data Segregation
- Importance of Metadata
- Machine Learning Algorithms
- Scikit Learn
- Effective Data Visualization
- Evaluating Accuracy of the Model
- Advantages of Naïve Bayes
- K-Means Clustering
- Expectation-Minimization Algorithm
- Mean Shift Algorithm
- Artificial Neural Networks
- Deep Neural Networks
- Architecture of ANN's
- Data Science in Real World
- Virtual Assistants
- Risk and Fraud Detection
- Data Analytics in Detail
- Types and Categories of Data Analytics
- Steps in Data Mining
- Data Science Lifecycle and Model Building
- Improving Data Science Models
- Determine Problems
- Search for More Data
- Deep Learning and Business
- Model Interpretability
- Autonomous Vehicles
- Finding Useful DataBig Data
