machine learning training program course list:
Introduction to Machine Learning
Overview of machine learning concepts and applications
Types of machine learning algorithms (supervised, unsupervised, reinforcement learning)
Machine learning workflow and model development process
Tools and libraries for machine learning (Python, scikit-learn, TensorFlow, PyTorch)
Data Preprocessing and Exploratory Data Analysis (EDA)
Data cleaning and handling missing values
Data transformation techniques (scaling, encoding)
Exploratory data analysis for understanding data patterns and distributions
Feature selection and extraction methods
Supervised Learning Algorithms
Linear regression for regression tasks
Logistic regression for binary classification
Decision trees and ensemble methods (Random Forest, Gradient Boosting)
Support Vector Machines (SVM) for classification and regression
Evaluation metrics for supervised learning models (accuracy, precision, recall, F1-score, ROC-AUC)
Unsupervised Learning Algorithms
K-means clustering for clustering analysis
Hierarchical clustering techniques
Principal Component Analysis (PCA) for dimensionality reduction
Association rule mining (Apriori algorithm) for market basket analysis
Evaluation metrics for unsupervised learning models (silhouette score, inertia)
Dimensionality Reduction and Feature Engineering
Techniques for reducing dimensionality (PCA, t-SNE)
Feature engineering for improving model performance
Handling categorical variables (one-hot encoding, label encoding)
Feature scaling and normalization methods
Model Evaluation and Hyperparameter Tuning
Cross-validation techniques (k-fold cross-validation, stratified cross-validation)
Grid search and random search for hyperparameter tuning
Model selection criteria (bias-variance tradeoff, overfitting, underfitting)
Fine-tuning machine learning models for optimal performance
Introduction to Deep Learning
Neural network architecture and components (input layer, hidden layers, output layer)
Activation functions (ReLU, Sigmoid, Tanh)
Backpropagation algorithm for model training
Deep learning frameworks (TensorFlow, Keras, PyTorch)
Convolutional Neural Networks (CNNs) for image recognition tasks
Recurrent Neural Networks (RNNs) and Natural Language Processing (NLP)
Introduction to RNN architecture and applications
Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)
Sequence modeling for time series forecasting and text generation
Word embeddings (Word2Vec, GloVe) for text representation
Sentiment analysis and text classification using RNNs
Transfer Learning and Model Deployment
Transfer learning techniques for leveraging pre-trained models
Fine-tuning pre-trained models for specific tasks
Model deployment strategies (cloud deployment, edge deployment)
Monitoring model performance and updating deployed models
Advanced Topics in Machine Learning
Ensemble learning techniques (Bagging, Boosting, Stacking)
Anomaly detection algorithms (Isolation Forest, One-Class SVM)
Recommender systems and collaborative filtering
Time series analysis and forecasting models (ARIMA, Prophet)
Explainable AI (XAI) and model interpretability
Machine Learning Pipelines and Workflow
Designing end-to-end machine learning pipelines
Data versioning and management in ML projects
Automated machine learning (AutoML) tools and platforms
DevOps practices for ML model deployment and maintenance
Case Studies and Real-World Projects
Hands-on machine learning projects and applications
Analyzing real-world datasets and solving business problems
Presenting and communicating machine learning results effectively
Certification and Assessment
Preparation for machine learning certification exams (e.g., Google Certified Professional Data Engineer, AWS Certified Machine Learning Specialty)
Assessments and quizzes to evaluate learning progress
Certificate of completion for the Machine Learning course
Course Title: Machine Learning Fundamentals and Applications
Course Overview: The Machine Learning Fundamentals and Applications course is designed to provide participants with a comprehensive understanding of machine learning concepts, algorithms, and practical applications. The course covers foundational topics such as supervised learning, unsupervised learning, and deep learning, as well as advanced techniques like model evaluation, hyperparameter tuning, and model deployment. Participants will gain hands-on experience through coding exercises, projects, and real-world case studies.
Course Duration: 10 weeks (2 hours per session, 2 sessions per week)
Prerequisites:
Basic programming knowledge (Python preferred but not mandatory)
Familiarity with linear algebra and calculus concepts
Understanding of data structures and algorithms
Course Outline:
Introduction to Machine Learning
Overview of machine learning concepts and applications
Types of machine learning (supervised, unsupervised, reinforcement learning)
Machine learning workflow and project lifecycle
Python for Machine Learning
Introduction to Python programming language
Python libraries for machine learning (NumPy, Pandas, Matplotlib)
Data manipulation and visualization in Python
Supervised Learning Algorithms
Linear regression for regression tasks
Logistic regression for binary classification
Support Vector Machines (SVM) for classification
Model evaluation metrics (accuracy, precision, recall, F1-score)
Unsupervised Learning Algorithms
K-means clustering for clustering analysis
Hierarchical clustering techniques
Principal Component Analysis (PCA) for dimensionality reduction
Evaluation metrics for unsupervised learning models
Deep Learning Fundamentals
Neural network architecture and components
Activation functions (ReLU, Sigmoid, Tanh)
Backpropagation algorithm for model training
Introduction to deep learning frameworks (TensorFlow, Keras)
Convolutional Neural Networks (CNNs)
CNN architecture for image recognition tasks
Convolutional layers, pooling layers, and fully connected layers
Transfer learning with pre-trained CNN models
Recurrent Neural Networks (RNNs)
RNN architecture and sequence modeling
Long Short-Term Memory (LSTM) networks for sequential data
Natural Language Processing (NLP) tasks with RNNs
Model Evaluation and Hyperparameter Tuning
Cross-validation techniques (k-fold cross-validation, grid search)
Hyperparameter tuning for optimizing model performance
Bias-variance tradeoff and overfitting/underfitting
Model Deployment and Productionization
Model deployment strategies (local deployment, cloud deployment)
Docker containers for packaging machine learning models
RESTful APIs for model serving
Monitoring and maintaining deployed models
Real-World Projects and Case Studies
Hands-on machine learning projects
Analyzing real-world datasets and solving business problems
Presenting and communicating machine learning results effectively
Assessment and Certification:
Weekly quizzes and coding assignments
Final project demonstrating machine learning skills
Certificate of completion for the Machine Learning Fundamentals and Applications course
Courses
Course Type
Full Stack Development In Java
150 Hrs
30 Students
