Course description
Machine learning (ML) has become indispensable in various industries and applications, transforming how businesses operate and improving decision-making processes. Here are key reasons why machine learning is essential in the real world:
- Automation and Efficiency
- Predictive Analytics
- Personalization and Customer Insights
- Healthcare and Medical Diagnostics
- Natural Language Processing (NLP)
- Image and Video Analysis
- Financial Services and Fraud Detection
- Machine learning (ML) is a subfield of artificial intelligence (AI) that focuses on developing algorithms and statistical models capable of learning from and making predictions or decisions based on data.
- Here’s a comprehensive overview of machine learning, its types, applications, and impact:
- Machine learning continues to revolutionize industries and everyday life, driving innovations in automation, decision-making, and personalized experiences.
- As advancements in algorithms, computing power, and data availability accelerate, the potential for ML to tackle complex problems and create value across diverse domains grows exponentially.
- At Stellaraa, we offer specialized courses to equip individuals with the skills and knowledge needed to thrive in the dynamic field of machine learning.
- Introduction to Machine Learning
- Overview of Machine Learning
- Types: Supervised, Unsupervised, Reinforcement Learning
- Applications in various industries
- Setting Up the Development Environment
- Installing Python, Jupyter Notebooks, Anaconda
- Essential libraries: NumPy, Pandas, Matplotlib, Seaborn, scikit-learn
- Data Preprocessing
- Understanding data types and structures
- Data cleaning and handling missing values
- EDA and visualization techniques
- Feature engineering and selection
- Normalization and standardization
- Regression Algorithms
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Evaluation Metrics: MSE, RMSE, R² Score
- Hands-On Practice
- Implementing Linear Regression with scikit-learn
- Interpreting regression results
- Classification Algorithms
- Logistic Regression
- K-Nearest Neighbors (KNN)
- Decision Trees
- Evaluation Metrics: Accuracy, Precision, Recall, F1 Score, ROC-AUC
- Hands-On Practice
- Building a classifier using scikit-learn
- Evaluating performance with confusion matrices
- Advanced Classification Algorithms
- Support Vector Machines (SVM)
- Random Forests
- Naive Bayes Classifier
- Hands-On Practice
- Implementing Random Forests
- Feature importance analysis
- Model Selection and Hyperparameter Tuning
- Cross-Validation Techniques
- Overfitting and Underfitting concepts
- Hyperparameter Tuning: Grid Search and Random Search
- Hands-On Practice
- Applying cross-validation
- Performing hyperparameter tuning using scikit-learn
- Unsupervised Learning
- Clustering Algorithms: K-Means, Hierarchical Clustering
- Dimensionality Reduction: Principal Component Analysis (PCA)
- Evaluation Metrics: Silhouette Score, Inertia
- Hands-On Practice
- Clustering datasets and interpreting clusters
- Visualizing high-dimensional data using PCA
- Introduction to Projects
- Guidance on selecting datasets and defining project scope
- Discussion on best practices for project execution
- Q&A Session for student queries
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Keras
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XGBoost
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PyTorch
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Scikit-learn
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TensorFlow
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