Machine Learning

Description

A typical Machine Learning Certification Syllabus would cover the foundational concepts, algorithms, and practical tools required to build and deploy machine learning models. Here’s an outline of a common syllabus, divided into multiple sections:

Duration : 240 Hrs

1. Introduction to Machine Learning

• What is Machine Learning?
  • History and evolution
  • Types of machine learning: Supervised, Unsupervised, Reinforcement Learning
  • Real-world applications of machine learning
• Machine Learning vs Artificial Intelligence vs Data Science

2. Data Preprocessing

• Data Cleaning
  • Handling missing data
  • Data normalization and standardization
• Feature Engineering
  • Feature extraction and selection
  • One-hot encoding, Label encoding
• Data Splitting
  • Training, testing, and validation sets

3. Supervised Learning

• Linear Regression
  • Simple Linear Regression
  • Multiple Linear Regression
  • Evaluation Metrics (MSE, RMSE, MAE)
• Classification Algorithms
  • Logistic Regression
  • k-Nearest Neighbors (k-NN)
  • Support Vector Machines (SVM)
  • Decision Trees and Random Forests
  • Naive Bayes
• Model Evaluation and Tuning
  • Cross-validation
  • Confusion matrix, Accuracy, Precision, Recall, F1-score
  • ROC Curve, AUC

4. Unsupervised Learning

• Clustering Algorithms
  • k-Means Clustering
  • Hierarchical Clustering
  • DBSCAN
• Dimensionality Reduction
  • Principal Component Analysis (PCA)
  • t-SNE (t-distributed Stochastic Neighbor Embedding)
• Anomaly Detection
  • Outlier detection techniques

5. Neural Networks and Deep Learning

• Introduction to Neural Networks
  • Perceptrons and Feedforward Neural Networks
  • Backpropagation algorithm
  • Activation functions (Sigmoid, ReLU, Tanh)
• Deep Learning
  • Convolutional Neural Networks (CNNs) for image recognition
  • Recurrent Neural Networks (RNNs) for sequential data
  • Long Short-Term Memory (LSTM)
  • Generative Adversarial Networks (GANs)
• Frameworks for Deep Learning
  • TensorFlow, Keras, PyTorch

6. Model Optimization and Hyperparameter Tuning

• Overfitting and Underfitting
• Bias-Variance Tradeoff
• Regularization Techniques
  • L1 and L2 regularization
• Grid Search and Random Search
• Bayesian Optimization

7. Advanced Machine Learning Techniques

• Ensemble Methods
  • Bagging (e.g., Random Forests)
  • Boosting (e.g., Gradient Boosting, AdaBoost, XGBoost)
• Reinforcement Learning
  • Introduction to Q-learning
  • Markov Decision Processes (MDPs)
  • Deep Q Networks (DQN)

8. Model Deployment and Production

• Introduction to Model Deployment
• Model Serving
  • Flask, FastAPI, Docker for API deployment
• Versioning and Monitoring Models in Production
  • Model performance tracking and retraining
• Scaling Machine Learning Models
  • Using cloud platforms (AWS, Azure, GCP)

9. Ethics in Machine Learning

• Fairness and Bias in Machine Learning
• Data Privacy
• AI for Social Good

10. Capstone Project

• End-to-End Project: Solving a real-world problem using machine learning techniques learned throughout the course.
• Model Development, Evaluation, and Deployment
• Presenting and Documenting the Solution

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Tools & Libraries Covered:

• Python Libraries: NumPy, pandas, Matplotlib, Seaborn, Scikit-learn
• Deep Learning Libraries: TensorFlow, Keras, PyTorch
• Cloud Platforms: AWS, Google Cloud, Microsoft Azure (for deploying models)

Would you like more specific information on any of these sections, or are you looking for a specific certification course?