Course description
- Enhanced Efficiency and Productivity
- Data-Driven Decision Making
- Improved Customer Experience
- Innovation and Problem Solving
- Healthcare Advancements
- Enhanced Security
- Accessibility and Inclusivity
- Economic Growth
- Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems.
- These processes include learning (acquiring information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.
- AI has become an integral part of various industries, transforming how we live, work, and interact with technology.
- Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems.
- These processes include learning (acquiring information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction.
- AI has become an integral part of various industries, transforming how we live, work, and interact with technology
- Overview of AI and its evolution
- Applications across industries (healthcare, finance, etc.)
- Ethical considerations in AI
- Setting up the development environment (Python, Jupyter, Anaconda)
- Introduction to Python for AI
- Python programming basics
- Essential libraries: NumPy, Pandas, Matplotlib
- Linear Algebra for AI
- Vectors, matrices, eigenvalues/eigenvectors, matrix operations
- Calculus and Probability
- Derivatives, chain rule, gradient descent
- Probability theory and Bayesian statistics
- Supervised Learning
- Regression (Linear, Ridge, Lasso)
- Classification (Logistic Regression, Decision Trees, SVM)
- Unsupervised Learning
- Clustering (K-Means, DBSCAN)
- Dimensionality reduction (PCA, LDA)
- Model evaluation (cross-validation, hyperparameter tuning)
- Neural Networks Basics
- Activation and loss functions, optimizers
- Introduction to TensorFlow/Keras
- CNNs and RNNs
- CNN architecture and applications (image classification)
- RNNs for time series and NLP (LSTM, GRU)
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TensorFlow
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PyTorch
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Keras
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Scikit-learn
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Apache Spark MLlib
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