Course Curriculum
- 5 Modules
- 24 Sessions
- 6 hour 6 sec
- 1.Reading CSV, Excel, JSON, APIs with pandas
- 2.Data Cleaning Essentials: Duplicates, Outliers, and Data Type Conversion
- 3. Data type conversions
- Working with Data in Pandas: Reading, Cleaning, and Conversions
- Working with Real-World Data: Loading, Cleaning, and Transforming with Pandas
- 4. Mastering Data Visualization with Matplotlib and Seaborn
- 5. Exploring Bar Charts and Histograms
- 6. Mastering Box Plots in Python
- 7. Exploring Relationships with Scatterplots
- 8. Mastering Line Charts: Visualizing Trends Over Time
- Mastering Data Visualization in Python
- Mastering Data Visualization with Python
- 9. Unveiling Insights Through Summary Statistics
- 10. Exploring Group By, Pivot Tables, and Correlation Analysis
- 11. Exploring Data Insights with Pivot Tables in Python Pandas
- 12. Understanding Correlation Analysis for Data Insights
- 13. The Power of Data Transformation in EDA
- Unveiling Insights Through Summary Statistics and EDA Techniques
- 14. Data Transformation Essentials: Scaling and Encoding
- 15. Saving and Loading Machine Learning Models with Joblib
- Data Transformation & Model Persistence in Machine Learning
- 16. Building a Linear Regression Model in Python
- 17. Enhancing Customer Churn Prediction with Classification Models
- Mini Machine Learning Project
- 8 Modules
- 29 Sessions
- 7 hour 37 min 33 sec
- 1. Setting up Python (Anaconda, Jupyter, or VS Code)
- 2. Exploring Jupyter Notebook: Foundations and Shortcuts
- Python Foundations: Jupyter Notebook Basics
- 3. Mastering Markdown and Operators in Jupyter Notebook
- 4. Understanding Python Operators, Precedence, and Typecasting
- 5. Modern Python String Formatting Techniques
- Mastering Python Essentials in Jupyter
- 6. Exploring Python Lists: Slicing and Modifying
- 7. Exploring Tuples in Python
- 8. Mastering Python Sets: Unordered, Unique, and Efficient Operations
- 9. Mastering Python Dictionaries
- 10. Mastering Indexing, Slicing, and Comprehension in Python
- 11. Applying Data Structures in Real-World Scenarios
- Mastering Dictionaries, Indexing, Slicing, and Comprehensions
- 12. Mastering Python Conditionals and Loops
- 13. Mastering Python Loops: For and While
- 14. Mastering Loop Control in Python
- Mastering Conditionals & Loops: Logic in Action
- 15. Functions and Modular Thinking in Python
- 16. Exploring Python's Built-in Functions
- 17. Refactoring a Modular Function-Based Calculator
- Mastering Functions and Modular Programming in Python
- 18. Dive into Data Science with NumPy: Practical Python Applications
- 19. Exploring NumPy, Pandas, and Data Structures in Python
- 20. Mastering Pandas for Data Analysis
- Foundations of Data Science with NumPy and Pandas
- 21. Resume Keyword Matcher Project Overview
- Resume Keyword Matcher: Project Overview Quiz
- 22. Python for AI Foundations Journey
/Frequently Asked Questions/
- 1. Setting up Python (Anaconda, Jupyter, or VS Code)
- 2. Exploring Jupyter Notebook: Foundations and Shortcuts
- Python Foundations: Jupyter Notebook Basics
- 3. Mastering Markdown and Operators in Jupyter Notebook
- 4. Understanding Python Operators, Precedence, and Typecasting
- 5. Modern Python String Formatting Techniques
- Mastering Python Essentials in Jupyter
- 6. Exploring Python Lists: Slicing and Modifying
- 7. Exploring Tuples in Python
- 8. Mastering Python Sets: Unordered, Unique, and Efficient Operations
- 9. Mastering Python Dictionaries
- 10. Mastering Indexing, Slicing, and Comprehension in Python
- 11. Applying Data Structures in Real-World Scenarios
- Mastering Dictionaries, Indexing, Slicing, and Comprehensions
- 12. Mastering Python Conditionals and Loops
- 13. Mastering Python Loops: For and While
- 14. Mastering Loop Control in Python
- Mastering Conditionals & Loops: Logic in Action
- 15. Functions and Modular Thinking in Python
- 16. Exploring Python's Built-in Functions
- 17. Refactoring a Modular Function-Based Calculator
- Mastering Functions and Modular Programming in Python
- 18. Dive into Data Science with NumPy: Practical Python Applications
- 19. Exploring NumPy, Pandas, and Data Structures in Python
- 20. Mastering Pandas for Data Analysis
- Foundations of Data Science with NumPy and Pandas
- 21. Resume Keyword Matcher Project Overview
- Resume Keyword Matcher: Project Overview Quiz
- 22. Python for AI Foundations Journey
Course Curriculum
Course Curriculum
- 8 Modules
- 29 Sessions
- 7 hour 37 min 33 sec
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Python Foundations for AI & Data Science
- 8 Modules
- 29 Sessions
- 7 hour 37 min 33 sec
- 1. Setting up Python (Anaconda, Jupyter, or VS Code)
- 2. Exploring Jupyter Notebook: Foundations and Shortcuts
- Python Foundations: Jupyter Notebook Basics
- 3. Mastering Markdown and Operators in Jupyter Notebook
- 4. Understanding Python Operators, Precedence, and Typecasting
- 5. Modern Python String Formatting Techniques
- Mastering Python Essentials in Jupyter
- 6. Exploring Python Lists: Slicing and Modifying
- 7. Exploring Tuples in Python
- 8. Mastering Python Sets: Unordered, Unique, and Efficient Operations
- 9. Mastering Python Dictionaries
- 10. Mastering Indexing, Slicing, and Comprehension in Python
- 11. Applying Data Structures in Real-World Scenarios
- Mastering Dictionaries, Indexing, Slicing, and Comprehensions
- 12. Mastering Python Conditionals and Loops
- 13. Mastering Python Loops: For and While
- 14. Mastering Loop Control in Python
- Mastering Conditionals & Loops: Logic in Action
- 15. Functions and Modular Thinking in Python
- 16. Exploring Python's Built-in Functions
- 17. Refactoring a Modular Function-Based Calculator
- Mastering Functions and Modular Programming in Python
- 18. Dive into Data Science with NumPy: Practical Python Applications
- 19. Exploring NumPy, Pandas, and Data Structures in Python
- 20. Mastering Pandas for Data Analysis
- Foundations of Data Science with NumPy and Pandas
- 21. Resume Keyword Matcher Project Overview
- Resume Keyword Matcher: Project Overview Quiz
- 22. Python for AI Foundations Journey
Python for Intermediate
- 5 Modules
- 24 Sessions
- 6 hour 6 sec
- 1.Reading CSV, Excel, JSON, APIs with pandas
- 2.Data Cleaning Essentials: Duplicates, Outliers, and Data Type Conversion
- 3. Data type conversions
- Working with Data in Pandas: Reading, Cleaning, and Conversions
- Working with Real-World Data: Loading, Cleaning, and Transforming with Pandas
- 4. Mastering Data Visualization with Matplotlib and Seaborn
- 5. Exploring Bar Charts and Histograms
- 6. Mastering Box Plots in Python
- 7. Exploring Relationships with Scatterplots
- 8. Mastering Line Charts: Visualizing Trends Over Time
- Mastering Data Visualization in Python
- Mastering Data Visualization with Python
- 9. Unveiling Insights Through Summary Statistics
- 10. Exploring Group By, Pivot Tables, and Correlation Analysis
- 11. Exploring Data Insights with Pivot Tables in Python Pandas
- 12. Understanding Correlation Analysis for Data Insights
- 13. The Power of Data Transformation in EDA
- Unveiling Insights Through Summary Statistics and EDA Techniques
- 14. Data Transformation Essentials: Scaling and Encoding
- 15. Saving and Loading Machine Learning Models with Joblib
- Data Transformation & Model Persistence in Machine Learning
- 16. Building a Linear Regression Model in Python
- 17. Enhancing Customer Churn Prediction with Classification Models
- Mini Machine Learning Project
Featured Online Course
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Start your learning journey today! Enroll now in our online course.
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What You Will Learn & Build:
Kickstart your coding journey with a beginner-friendly course. We guide you from installation to building a real Python application using the tools the pros use.
Write programs using variables, data types, conditions, and loops.
Apply logical thinking to create clean, modular code with functions.
Get hands-on with NumPy and Pandas, the core libraries for data manipulation.
Process text, handle files, and manage errors like a professional developer.
Why Choose ByteSkill's Bootcamp?
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This course teaches you how to use Python for real-world Artificial Intelligence and Data Science applications. You’ll learn programming basics, data analysis, machine learning, and how to build AI models using libraries like NumPy, Pandas, Matplotlib, Scikit-Learn, and TensorFlow.
You only need a laptop with internet access. Tools like Jupyter Notebook, Anaconda, and Python libraries will be guided inside the course.
Yes. Concepts are taught step-by-step with real examples, making it perfect for beginners in coding or Data Science.
Yes, you will receive a recognized Edurva completion certificate, which you can add to your resume, LinkedIn profile, or portfolio.
Yes. You will work on industry-oriented projects, including data analysis, machine learning models, and AI applications.
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FAQ
Most Frequently Asked Questions
This course teaches you how to use Python for real-world Artificial Intelligence and Data Science applications. You’ll learn programming basics, data analysis, machine learning, and how to build AI models using libraries like NumPy, Pandas, Matplotlib, Scikit-Learn, and TensorFlow.
You only need a laptop with internet access. Tools like Jupyter Notebook, Anaconda, and Python libraries will be guided inside the course.
Yes. Concepts are taught step-by-step with real examples, making it perfect for beginners in coding or Data Science.
Yes, you will receive a recognized Edurva completion certificate, which you can add to your resume, LinkedIn profile, or portfolio.
Yes. You will work on industry-oriented projects, including data analysis, machine learning models, and AI applications.
FAQ
Most Frequently Asked Questions
This course teaches you how to use Python for real-world Artificial Intelligence and Data Science applications. You’ll learn programming basics, data analysis, machine learning, and how to build AI models using libraries like NumPy, Pandas, Matplotlib, Scikit-Learn, and TensorFlow.
You only need a laptop with internet access. Tools like Jupyter Notebook, Anaconda, and Python libraries will be guided inside the course.
Yes. Concepts are taught step-by-step with real examples, making it perfect for beginners in coding or Data Science.
Yes, you will receive a recognized Edurva completion certificate, which you can add to your resume, LinkedIn profile, or portfolio.
Yes. You will work on industry-oriented projects, including data analysis, machine learning models, and AI applications.
Common Questions
Most Popular Questions
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What is the Python for AI & Data Science course about?
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