python data analytics: how to learn python data science and use machine learning.introduction to deep learning to master python for beginners. - Tapa blanda

Sinopsis

Do you want to enter into the new world of Python data science as a beginner in an efficient and effective way?

Do you want to master Python programming language basic syntax, functions and library handling techniques with an ultimate guide book?

Are you curious to develop data analytics and data science models?

Machine learning and Data Science theories provide simple and effective techniques to automate processes and deliver long term advantages to businesses and industries. Python Data Analytics lets readers begin Python from scratch and develop strong skills for developing and implementing machine learning models. Python libraries including Pandas, NumPy, and Scikit-Learn are a major part of the model development process for which complete details and ideas have been shared to make programming simple and effective.

Along with Python programming details, the book also includes in-depth explanations regarding machine learning algorithms, deep learning concepts, and data analytics approaches. For effective learning, beginners can overview code samples and acquire sharp Python programming skills to develop reliable, accurate, and high-performing machine learning and deep analytics models.

In this book, you will learn:

• Basics of Python for Data Analysis
• NumPy
• 2-D and 3-D arrays
• SciPy
• Linear Algebra
• Pandas
• Operations
• Python IDE’s
• Sublme Text
• Atom
• Eclipse
• Basic Syntax
• 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
• Parallelization
• 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
• Keras
• 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
• Choosing Hyper Parameters
• Testing and Evaluation
• Search for More Data
• Deep Learning and Business
• Algorithm Tuning and Method Ensemble
• Model Interpretability
• Language Recognition
• Autonomous Vehicles
• Finding Useful Data
• Big Data
• Organizational Benefits
• Useful Deep Learning Methods and Techniques
• Quality Assurance
• Advanced AI

Even if you don’t have any experience you can  get excellent  results In a few days.

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