Importance of Python Machine Learning
Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of Computer Programs that can change when exposed to new data.
Python Machine Learning
- Category: Programming
- Project: One Academic Project
- Duration: 2 Month
Syllabus
Introduction
- History
- Features
- Setting up path
- Working with Python
- Basic Syntax
- Variable and Data Types
- Operator
Control Flow Statements
- If-else statements
- Loops (for, while)
- Break, continue, and pass
Function and Modules
Functions
- Defining a function
- Calling a function
- Types of functions
- Function Arguments
- Anonymous functions
- Global and local variables
Modules
- Importing module
- Math module
- Random module
- Packages
- Composition
Data Structures
- String
- Lists, tuples, and sets
- Dictionaries
- List comprehensions
Introduction to AI and Machine Learning
- Application of AI
- Deep learning, neutral learning, machine learning
- Introduction to Machine learning
- Supervised learning, (labelled data sets)
- Unsupervised learning, (not labelled)
- Reinforcement learning.
Machine Learning packages in python
- NumPy and Pandas (for data analysis)
- Matplotlib and Seaborn (for data visualization)
- programs using python data frames
Data Preprocessing and Cleaning
- Handling missing values and outliers
- Data encoding (One-hot, Label encoding)
- Feature scaling (Normalization, Standardization)
- Splitting datasets into training and testing sets
Supervised Learning
- Regression
- Linear Regression
- Polynomial Regression
- Ridge and Lasso Regression
- Classification
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Decision Trees and Random Forests
- Support Vector Machines (SVM)
- Naive Bayes
Unsupervised Learning
- Clustering (K-Means, Hierarchical Clustering)
- Dimensionality Reduction (PCA, t-SNE)