Machine Learning Training Course in Jalandhar

Course Content: Machine Learning Training in Jalandhar

Module 1: Introduction to Machine Learning

• What is Machine Learning?
• Types of Machine Learning (Supervised, Unsupervised, Reinforcement Learning)
• Machine Learning Workflow
• Tools and Software Setup (Python, Jupyter, scikit-learn)

Module 2: Data Preprocessing

• Data Cleaning and Handling Missing Values
• Data Transformation (Scaling, Encoding)
• Feature Engineering
• Data Splitting (Training, Validation, Testing)

Module 3: Supervised Learning

• Linear Regression
• Logistic Regression
• Decision Trees and Random Forest
• Support Vector Machines (SVM)
• k-Nearest Neighbors (k-NN)
• Model Evaluation Metrics (Accuracy, Precision, Recall, F1-score)

Module 4: Unsupervised Learning

• Clustering Algorithms (K-Means, Hierarchical)
• Dimensionality Reduction (Principal Component Analysis, t-SNE)
• Association Rule Mining

Module 5: Model Selection and Evaluation

• Cross-Validation Techniques (k-fold, Stratified, Leave-One-Out)
• Bias-Variance Tradeoff
• Hyperparameter Tuning (Grid Search, Random Search)
• Model Selection and Comparison

Module 6: Neural Networks and Deep Learning

• Introduction to Artificial Neural Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)
• Transfer Learning with Pretrained Models
• Practical Deep Learning Tips and Tricks

Module 7: Natural Language Processing (NLP)

• Text Preprocessing
• Word Embeddings (Word2Vec, GloVe)
• Sentiment Analysis
• Text Classification
• Named Entity Recognition (NER)
• Seq2Seq Models and Attention Mechanisms

Module 8: Reinforcement Learning (Optional)

• Introduction to Reinforcement Learning
• Markov Decision Processes
• Q-Learning and Deep Q Networks (DQN)
• Policy Gradient Methods

Module 9: Time Series Analysis

• Time Series Data
• ARIMA and Seasonal Decomposition
• Exponential Smoothing
• Forecasting Techniques

Module 10: Model Deployment and Productionization

• Model Deployment Strategies
• RESTful APIs for Model Deployment
• Containerization with Docker
• Orchestrating Containers with Kubernetes

Module 11: Ethics and Bias in Machine Learning

• Data Privacy and Security
• Bias and Fairness in Machine Learning
• Ethical Considerations
• Regulatory Compliance (e.g., GDPR)

Module 12: Real-world Projects and Case Studies

• Application of Machine Learning in Various Industries
• Hands-on Projects
• Case Studies and Practical Examples

Module 13: Advanced Topics (Optional)

• Bayesian Machine Learning
• Anomaly Detection
• Recommender Systems
• Advanced Deep Learning Architectures (e.g., GANs, Transformers)

Module 14: Capstone Project

• Real-world Machine Learning Project
• End-to-end Project Lifecycle
• Presentation and Documentation