Introduction to Gradient of a Function Role in Optimization Mathematical Definition Partial Derivatives and Multivariable Functions Gradient Descent Algorithm Variants of Gradient Descent Applications in Machine Learning Visualization of Gradients Gradient vs Directional Derivative Challenges in Gradient Computation Summary...

Introduction to Recommendation Systems in ML Collaborative Filtering Content Based Filtering Hybrid Methods Similarity Metrics (Cosine, Pearson) Cold Start Problem Real-Time Recommendation Engines Tools and Libraries (Surprise, LightFM) Business Applications (Netflix, Amazon) Conclusion Introduction to Recommendation...

What is Time Series Data? Components of Time Series (Trend, Seasonality) Time Series Decomposition Moving Averages and Smoothing Autocorrelation and ACF/PACF ARIMA Model LSTM for Time Series Forecasting Evaluation Metrics for Time Series Stationarity and Differencing Case Studies in Finance and Weather Conclusion ...

Introduction to Evaluation Metrics Precision and Recall Explained Definition of F1 Score F1 Score Formula F1 Score in Imbalanced Datasets Macro vs Micro Averaging F1 Score vs Accuracy When to Use F1 Score ROC-AUC vs F1 Case Studies and Use Cases Conclusion Introduction to Evaluation Metrics In the world of machine...

What is Deep Learning? Neural Networks Overview Convolutional Neural Networks (CNNs) Recurrent Neural Networks (RNNs) Long Short-Term Memory (LSTM) Generative Adversarial Networks (GANs) Transformers and Attention Mechanism Autoencoders Conclusion What is Deep Learning? Deep Learning is a subfield of machine learning that uses neural...

Importance of Feature Engineering Feature Extraction vs Selection Handling Missing Data Encoding Categorical Variables Normalization and Scaling Creating New Features Feature Selection Techniques Dimensionality Reduction (PCA, LDA) Interaction and Polynomial Features Automated Feature Engineering Tools Conclusion ...

What is Transfer Learning? Types of Transfer Learning Pre-trained Models Fine-Tuning vs Feature Extraction Popular Architectures (ResNet, BERT, etc.) Transfer Learning in NLP Transfer Learning in Computer Vision Domain Adaptation Cross lingual Transfer Learning Advantages and Challenges Case Studies and Applications ...

Basics of Image Processing Digital Image Representation Filtering and Convolution Edge Detection Techniques Color Spaces and Histograms Morphological Operations Feature Extraction from Images Image Preprocessing for ML Applications In Medical, Surveillance, Industrial Conclusion Basics of Image Processing Image...

What is a Cost Function? Role in Supervised Learning Mean Squared Error (MSE) Cross-Entropy Loss Hinge Loss Custom Loss Functions Gradient Descent and Optimization Cost vs Loss vs Objective Functions Conclusion What is a Cost Function? ACost Function in Machine Learning , also called a loss function or objective...

Introduction to Boosting Concept of AdaBoost in Machine Learning Weak Learners and Decision Stumps AdaBoost Algorithm Step-by-Step Weight Adjustment Mechanism Mathematical Formulation Application in Classification Overfitting and Regularization Real-World Use Cases Conclusion Introduction to Boosting Boosting is an...