AI-Powered Data Science & Machine Learning with Python
Python Language
1. Introduction to Python Programming
• Overview of Python, installation, and setup
• Introduction to Python IDEs
2. Basic Syntax and Data Types
• Variables, data types, and basic input/output
• String manipulation and formatting
3. Control Structures: Conditional Statements
• if, elif, and else structures
• Logical operations and applications
4. Loops in Python
• for and while loops
• Use cases and examples
5. Functions and Modules
• Writing functions and understanding parameters/return values
• Importing and using Python standard libraries
6. Lists
• List operations and methods
• Differences between Numeric and string Lists cases
7. Dictionaries
• Key-value pairs and dictionary methods
8. Working with Files
• Reading from and writing to text files
Introduction to Libraries for AI
Data Visualization with matplotlib
• Creating plots, histograms, and scatter plots
• Customizing plots for better visualization
Python Matplotlib
• Matplotlib Bars
• Matplotlib plot
• Matplotlib Scatter
• Matplotlib boxplot
• Matplotlib Histograms
• Matplotlib Pie Charts
Pandas Introduction
• Analyzing DataFrames
• Read CSV Files
Cleaning Data
• Removing Null Values
• Droping Rows
• Droping Columns
• Modifying Values
Python Seaborn
• Seaborn lineplot
• Seaborn barplot
• seaborn Scatterplot
• seaborn countplot
• seaborn heatmap
• seaborn catplot
Numpy Libaray
• Understand the basic structure of NumPy arrays.
• Create and manipulate arrays efficiently.
• Perform mathematical operations on arrays.
• Apply array indexing and slicing.
• Applying Sorting.
• Work with random number generation.
• Apply linear algebra operations.
Machine Learning
• Supervised learning
Supervised learning is a powerful approach to machine learning, enabling algorithms to learn from labeled data and make accurate predictions on new, unseen data.
• Types of Supervised Learning:
1. Classification: The target variable is categorical (e.g., spam vs. non-spam emails).
2. Regression: The target variable is continuous (e.g., predicting house prices).
• Unsupervised learning
Unsupervised learning is a type of machine learning algorithm that automatically identifies patterns, relationships, and groupings in data without any prior knowledge of the expected output.
Types of Unsupervised Learning:
o Clustering
o Association
• Mean - The average value
• Median - The mid point value
• Mode - The most common value
• Numpy and Random Numbers
• scikit-learn Libaray
• Decision Tree
