Artificial Intelligence and Machine Learning (AIML) Projects

Topics Covered - Artificial Intelligence, Machine Learning, Data Science

Contents:

• Markdown Language: Syntax and Examples
• Python Programming Language: Syntax and Examples
• NumPy for Data Science
• Pandas for Data Science
• Matplotlib
• Basic Statistics
• Applied Statistics
• Supervised Learning
• Unsupervised Learning
• Ensemble Techniques
• Feature Engineering, Model Selection
• Recommendation Systems
• Deep Learning
• Natural Language Processing-1
• Natural Language Processing-2
• Computer Vision-1
• Computer Vision-2
• Computer Vision-3

Markdown Language: Syntax and Examples

1. Basic Syntax: Headings, Paragraphs, Line Breaks, Emphasis, Blockquotes, Lists, Code, Horizontal Rules, Links, Images, Escaping Characters, HTML

2. Extended Syntax: Tables, Fenced Code Blocks, Footnotes, Heading IDs, Definition Lists, Strikethrough, Task Lists, Emoji, Highlight, Subscript, Superscript, Automatic URL Linking

3. Hacks: Underline, Indent, Center, Color, Comments, Admonitions, Image Size, Image Captions, Link Targets, Symbols, Table Formatting, Table of Contents, Videos

Reference and Details: Markdown Language: Syntax and Examples.

Python Programming Language: Syntax and Examples

1. Overview/Basics
2. Lists
3. Dictionaries
4. If Statements; While Loops
5. Functions
6. Classes
7. Files and Exceptions
8. Testing your Code
9. Matplotlib
10. Plotly

Reference and Details: Python Programming Language: Syntax and Examples.

NumPy for Data Science

1. Introduction to NumPy
2. Key Features of NumPy
   1. Arrays and Data Structures
   2. Universal Functions (ufunc)
   3. Broadcasting
   4. Indexing and Slicing
   5. Array Manipulation
   6. Mathematical Functions
   7. Random Number Generation
   8. File I/O
   9. Integration with Other Libraries
3. Performance and Efficiency
4. Applications of NumPy
   1. Data Analysis
   2. Machine Learning
   3. Scientific Computing
5. Best Practices with NumPy
   1. Efficient Memory Management
   2. Vectorization
   3. Code Optimization
   4. Error Handling and Debugging
6. Conclusion
7. Further Resources

Reference and Details: NumPy for Data Science: A Comprehensive Guide.

Pandas for Data Science

1. Introduction
2. Key Features of Pandas
   • Data Structures
     • Series
     • DataFrame
     • Panel (deprecated)
   • Data Alignment
   • Handling Missing Data
     • isna() and notna() functions
     • fillna() method
     • dropna() method
   • Data Manipulation
     • Indexing and Selection
     • Data Transformation
   • Grouping and Aggregation
     • Grouping
     • Aggregation
     • Transformation
   • Merging and Joining
     • Concatenation
     • Merging
     • Joining
   • Input and Output
     • Reading Data
     • Writing Data
   • Time Series Analysis
     • Date Range Generation
     • Frequency Conversion
     • Resampling
     • Time Shifting
   • Visualization
     • Basic Plotting
     • Integration with Matplotlib
   • Data Cleaning
     • Removing Duplicates
     • Replacing Values
     • Renaming Columns
   • Advanced Indexing
     • MultiIndex
     • Cross-section Selection
   • Performance Optimization
     • Memory Usage
     • Efficient Computation
   • Integration with Other Libraries
     • NumPy Integration
     • Scikit-learn Integration
   • Data Visualization Integration
     • Seaborn Integration
     • Plotly Integration
3. Conclusion

Reference and Details: Pandas for Data Science: A Comprehensive Guide.

Matplotlib

Pass

Basic Statistics

Pass

Applied Statistics

Pass

Supervised Learning

Pass

Unsupervised Learning

Pass

Ensemble Techniques

Pass

Feature Engineering, Model Selection

Pass

Recommendation Systems

Pass

Deep Learning

Pass

Natural Language Processing-1

Pass

Natural Language Processing-2

Pass

Computer Vision-1

Pass

Computer Vision-2

Pass

Computer Vision-3

Pass

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Published: 2020-01-01; Updated: 2024-05-01

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