1 to 2 Months
Online + Offline
15 Students Only Batch Size
Anyone
Beginner to Advance Training
Definition and Core Concepts Machine Learning (ML): Machines learning from data. Artificial Intelligence (AI): Machines performing intelligent tasks. Key Concepts: Algorithms, models, and training phases. Applications and Impact Industry Impact: Revolutionizing healthcare, finance, retail, automotive, marketing, and education. Real-world Applications: Showcasing practical uses in various sectors. Differentiating ML and AI Scope and Capability: ML learns; AI performs intelligent tasks. Human Mimicry: AI mimics human intelligence; ML focuses on patterns. Learning vs. Reasoning: ML learns from data; AI includes reasoning. Illustrative Examples: Providing practical cases for better understanding.
Introduction to Learning Paradigms Supervised Learning: Learning from labeled data with input-output pairs. Unsupervised Learning: Extracting patterns from unlabeled data. Reinforcement Learning: Learning from interactions with an environment, maximizing rewards. Key ML Algorithms Regression: Predicting continuous outcomes. Classification: Categorizing data into classes. Clustering: Grouping similar data points. Evaluation Metrics in ML Understanding metrics like accuracy, precision, recall, F1-score. Data Preprocessing and Feature Engineering Handling Missing Data: Strategies for dealing with missing values. Outlier Management: Techniques for identifying and handling outliers. Feature Scaling and Normalization: Ensuring features are on a similar scale. Feature Selection Techniques: Choosing relevant features for model training.
Model Training and Evaluation Splitting data into training and testing sets Training ML models using scikit-learn Cross-validation for model evaluation Regression and Classification Understanding and implementing linear regression Logistic regression for classification Model evaluation for regression and classification Clustering and Unsupervised Learning Introduction to clustering algorithms (K-Means, Hierarchical) Applications of unsupervised learning
Overview of AI Concepts Definition: Machines mimicking human intelligence. History and Evolution: Tracing the development of AI. Narrow vs. General AI: Understanding the spectrum of AI capabilities. Neural Networks and Deep Learning Basics Neural Networks: Mimicking the human brain's structure. Deep Learning: Utilizing neural networks with multiple layers. Activation Functions: Understanding their role in neural network computations. Introduction to TensorFlow or PyTorch TensorFlow: Overview and applications. PyTorch: Introduction and use cases. Hands-on Basics: Simple exercises to get familiar with the chosen framework.
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