Description
Machine Learning (ML) is a branch of artificial intelligence (AI) that enables systems to learn and improve from experience without being explicitly programmed. In ML, algorithms are used to identify patterns and make decisions based on data, allowing computers to perform tasks such as prediction, classification, and optimization.
Machine Learning Course Content
Module 1: Introduction to Machine Learning
- What is Machine Learning?
- Applications of Machine Learning
- The Machine Learning Process
Module 2: Python for Machine Learning
- Setting up Python Environment
- Basic Python for ML
- Exploratory Data Analysis (EDA)
Module 3: Supervised Learning
- Overview of Supervised Learning
- Regression Algorithms
- Linear Regression
- Polynomial Regression
- Ridge and Lasso Regression
- Classification Algorithms
- Logistic Regression
- k-Nearest Neighbors (k-NN)
- Support Vector Machines (SVM)
- Decision Trees
- Random Forest
- Naive Bayes
- Model Evaluation
- Hands-on Projects
Module 4: Unsupervised Learning
- Introduction to Unsupervised Learning
- Clustering Algorithms
- k-Means Clustering
- Hierarchical Clustering
- DBSCAN
- Dimensionality Reduction
- Principal Component Analysis (PCA)
- t-SNE
- Association Rule Learning
- Apriori Algorithm
- Eclat
- Hands-on Projects
Module 5: Advanced Topics in Supervised Learning
- Ensemble Methods
- Bagging and Boosting
- Random Forest
- Gradient Boosting Machines (GBM)
- XGBoost
- Regularization
- L1 and L2 Regularization
- Support Vector Machines (Advanced)
- Hyperparameter Tuning
- Grid Search and Random Search
- Hands-on Projects
Module 6: Introduction to Neural Networks and Deep Learning
- Introduction to Neural Networks
- Deep Learning Basics
- Intro to TensorFlow and Keras
- Hands-on Projects
- Image classification using neural networks
Module 7: Model Deployment and Practical Considerations
- Saving and Loading Models
- Model Deployment
- Real-World Challenges in ML
Courses Features
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LanguageEnglish
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Lectures02
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CertificationYes
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Project2 Minor + 2 Major
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Duration35 hrs
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Max-Students20
