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159

Machine Learning Fundamentals 1 What is Machine Learning? 2 Types of Machine Learning 3 What is Supervised learning? 4 What is Unsupervised learning? 5 Machine Learning vs Artificial intelligence 6 Machine Learning Tutorial 7 History of Machine Learning 8 Reinforcement Learning 9 Machine Learning vs Deep Learning 10 Applications of Machine Learning 11 Machine Learning Workflow 12 Advantages and Limitations of Machine Learning

Structured Data Classification 1 Structured Data Classification 2 Hands on 3 Introduction to Classification 4 Binary Classification 5 Multi-Class Classification 6 Logistic Regression 7 Decision Tree Classifier 8 Random Forest Classifier 9 K-Nearest Neighbors (KNN) 10 Support Vector Machine (SVM) 11 Naive Bayes Algorithm 12 Hands-on Classification Project

Unstructured Data Classification 1 Unstructured Data Classification 2 Introduction to Unstructured Data 3 Text Classification 4 Image Classification 5 Audio Classification 6 Natural Language Processing Basics 7 Tokenization 8 Stop Words Removal 9 Stemming and Lemmatization 10 TF-IDF Vectorization 11 Sentiment Analysis 12 Hands-on NLP Project

Python for Machine Learning 1 Introduction to Python 2 Variables and Data Types 3 Operators and Expressions 4 Loops and Conditional Statements 5 Functions in Python 6 NumPy Basics 7 Pandas Basics 8 Matplotlib for Visualization 9 Seaborn for Data Visualization 10 Jupyter Notebook Introduction

Mathematics for Machine Learning 1 Linear Algebra Basics 2 Scalars, Vectors, and Matrices 3 Matrix Operations 4 Probability Basics 5 Statistics Fundamentals 6 Mean, Median, Mode 7 Variance and Standard Deviation 8 Correlation and Covariance 9 Calculus for Machine Learning 10 Gradient Descent

Data Preprocessing 1 What is Data Preprocessing? 2 Handling Missing Data 3 Feature Scaling 4 Normalization vs Standardization 5 Encoding Categorical Data 6 Train-Test Split 7 Feature Engineering 8 Handling Outliers 9 Data Cleaning Techniques 10 Dimensionality Reduction

Structured Data Regression 1 Introduction to Regression 2 Linear Regression 3 Multiple Linear Regression 4 Polynomial Regression 5 Ridge Regression 6 Lasso Regression 7 Regression Evaluation Metrics 8 Hands-on Regression Project

Clustering Algorithms 1 What is Clustering? 2 K-Means Clustering 3 Hierarchical Clustering 4 DBSCAN Clustering 5 Elbow Method 6 Silhouette Score 7 Customer Segmentation Project

Association Rule Learning 1 Introduction to Association Rules 2 Apriori Algorithm 3 Market Basket Analysis 4 FP-Growth Algorithm

Model Evaluation and Validation 1 Accuracy 2 Precision 3 Recall 4 F1 Score 5 Confusion Matrix 6 ROC Curve 7 AUC Score 8 Cross Validation 9 Bias vs Variance 10 Overfitting and Underfitting

Deep Learning Fundamentals 1 Introduction to Deep Learning 2 Artificial Neural Networks 3 Activation Functions 4 Forward Propagation 5 Backpropagation 6 TensorFlow Introduction 7 Keras Introduction 8 Building First Neural Network

Computer Vision 1 Introduction to Computer Vision 2 Image Processing Basics 3 Convolutional Neural Networks (CNN) 4 Image Recognition 5 Object Detection 6 Face Recognition Project

Natural Language Processing (Advanced) 1 NLP Overview 2 Bag of Words 3 Word Embeddings 4 RNN Basics 5 LSTM Networks 6 Transformer Models 7 Chatbot Development

Time Series Analysis 1 Introduction to Time Series 2 Time Series Components 3 ARIMA Model 4 Forecasting Techniques 5 Stock Price Prediction Project

Recommendation Systems 1 Introduction to Recommendation Systems 2 Content-Based Filtering 3 Collaborative Filtering 4 Movie Recommendation System Project

Reinforcement Learning 1 Introduction to Reinforcement Learning 2 Agent and Environment 3 Q-Learning 4 Reward Function 5 Real-World Applications

Machine Learning Projects 1 House Price Prediction 2 Spam Email Detection 3 Fake News Detection 4 Image Classification Project 5 Customer Churn Prediction 6 Sales Forecasting 7 Chatbot Project

Model Deployment 1 Introduction to Model Deployment 2 Saving and Loading Models 3 Flask for ML Deployment 4 FastAPI for ML APIs 5 Deploying ML Model on Cloud 6 Docker Basics for ML

MLOps Fundamentals 1 What is MLOps? 2 ML Pipeline 3 Model Monitoring 4 CI/CD for ML 5 Version Control for Models

Generative AI and Large Language Models 1 Introduction to Generative AI 2 What are Large Language Models? 3 Prompt Engineering 4 Fine-Tuning Basics 5 Retrieval-Augmented Generation (RAG) 6 AI Agents Introduction

Ethics and Responsible AI 1 AI Ethics 2 Bias in Machine Learning 3 Explainable AI 4 Data Privacy 5 Responsible AI Practices

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Regression Evaluation Metrics

Rumman Ansari May 25, 2026 2 views

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