Machine Learning & Algorithms
Learn core machine learning concepts and algorithms with Python for real-world applications.
Course Syllabus
What is AI & ML? Real-life examples
Introduction to artificial intelligence and machine learning
Topics Covered:
- AI vs ML
- Real-world applications
- Future of AI
AI vs Machine Learning
Understanding the differences and relationships
Topics Covered:
- AI definitions
- ML types
- Deep learning basics
Python refresher for ML
Essential Python concepts for machine learning
Topics Covered:
- Python basics
- Data types
- Control structures
Advanced Python for ML
Python libraries and tools for data science
Topics Covered:
- Object-oriented Python
- Error handling
- File operations
NumPy basics
Introduction to numerical computing
Topics Covered:
- NumPy arrays
- Array operations
- Mathematical functions
NumPy & Pandas basics
Working with numerical and data analysis libraries
Topics Covered:
- Pandas DataFrames
- Data manipulation
- Data selection
Advanced Pandas
Advanced data manipulation techniques
Topics Covered:
- Groupby operations
- Data aggregation
- Time series data
Data cleaning basics
Preparing data for analysis
Topics Covered:
- Missing data
- Data types
- Data validation
Data cleaning & visualization
Preparing and visualizing data for analysis
Topics Covered:
- Data cleaning
- Matplotlib
- Seaborn visualization
Advanced Data Visualization
Creating compelling data visualizations
Topics Covered:
- Chart customization
- Interactive plots
- Dashboard creation
Supervised learning basics
Introduction to supervised learning
Topics Covered:
- Training data
- Labels
- Model types
Supervised learning (Linear Regression)
Understanding supervised learning with linear regression
Topics Covered:
- Linear regression
- Model training
- Prediction
Advanced Linear Regression
Building robust regression models
Topics Covered:
- Feature engineering
- Model evaluation
- Regularization
Classification basics (KNN)
Introduction to classification algorithms
Topics Covered:
- K-Nearest Neighbors
- Classification metrics
- Model evaluation
Advanced Classification
Building classification systems
Topics Covered:
- Feature scaling
- Cross-validation
- Hyperparameter tuning
Train/Test split
Proper data splitting for model validation
Topics Covered:
- Data splitting
- Cross-validation
- Overfitting prevention
Model Validation Techniques
Ensuring model reliability
Topics Covered:
- K-fold cross-validation
- Stratified sampling
- Validation metrics
Intro to Neural Networks
Basics of artificial neural networks
Topics Covered:
- Neural network architecture
- Activation functions
- Training process
Neural Network Implementation
Building neural networks from scratch
Topics Covered:
- Forward propagation
- Backpropagation
- Gradient descent
Algorithms: Sorting & Searching
Fundamental algorithms for data processing
Topics Covered:
- Bubble sort
- Quick sort
- Binary search
- Linear search
Algorithm Complexity
Understanding algorithm efficiency
Topics Covered:
- Big O notation
- Time complexity
- Space complexity
Recursion & problem-solving
Solving complex problems using recursion
Topics Covered:
- Recursive thinking
- Base cases
- Problem decomposition
Model deployment basics
Deploying machine learning models
Topics Covered:
- Model saving
- API creation
- Web deployment
Final Project: ML-powered Application
Complete machine learning application
Topics Covered:
- End-to-end ML pipeline
- Model optimization
- Real-world deployment
Q&A Session 1
Interactive Q&A session to clarify doubts and review ML concepts
Topics Covered:
- Algorithm review
- Model optimization
- Best practices
Q&A Session 2
Final Q&A session and course wrap-up
Topics Covered:
- Advanced questions
- Next steps
- Course completion
What You'll Learn
Course Summary
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