Course Content
Introduction to AI & ML
Introduction to AI & ML
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Python for AI/ML (NumPy, Pandas, Matplotlib, and Sci-kit Learn)
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Development Environment Setup (Notebook/Anaconda)
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Mathematical Foundations for ML
Linear Algebra for ML
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Probability & Statistics
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Distance Metrics & Similarity
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Bias-Variance Trade-off
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Data Preprocessing and Exploration
Data Cleaning & Preprocessing
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Feature Engineering
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Data Analysis & Visualization
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Supervised Learning
Regression Analysis & Algorithms
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Classification Algorithms
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Model Evaluation & Performance Metrics
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Overfitting, Hyperparameter Tuning & Regularization
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Unsupervised Learning
Clustering Techniques
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Dimensionality Reduction
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Outlier Detection
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Introduction to Deep Learning
Artificial Neural Networks (ANN)
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Convolutional Neural Networks (CNN)
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Recurrent Neural Networks
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Deep Learning in Practice
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Natural Language Processing (NLP)
Text Preprocessing
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Feature Engineering for NLP
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Text Classification
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Named Entity Recognition (NER)
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AI/ML in Practice
MLOps & AutoML
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Recommender Systems
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Lessons: