Machine Learning Training
Introduction to Machine Learning
Gain an overview of machine learning, a subset of artificial intelligence that focuses on building systems that learn from data. Learn about the fundamental concepts, types of machine learning, and applications.
Data Preparation and Preprocessing
Learn how to prepare and preprocess data for machine learning. Understand techniques for cleaning data, handling missing values, and feature engineering to ensure high-quality input for models.
Supervised Learning
Explore supervised learning techniques, including regression and classification. Learn about algorithms such as linear regression, decision trees, and support vector machines, and how to apply them to real-world problems.
Unsupervised Learning
Discover unsupervised learning methods, including clustering and dimensionality reduction. Learn about algorithms such as k-means clustering, hierarchical clustering, and principal component analysis (PCA).
Model Evaluation and Selection
Understand how to evaluate and select machine learning models. Learn about metrics such as accuracy, precision, recall, and F1-score, and techniques for model validation and cross-validation.
Deep Learning
Explore deep learning, a subset of machine learning that uses neural networks to model complex patterns. Learn about architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
Machine Learning Tools and Libraries
Get familiar with popular machine learning tools and libraries such as scikit-learn, TensorFlow, and Keras. Learn how to use these tools to build and deploy machine learning models effectively.
Hands-On Labs and Practical Projects
Engage in hands-on labs and practical projects to apply your knowledge of machine learning. Work on real-world scenarios to develop practical skills in building, evaluating, and deploying machine learning models.
Machine Learning syllabus
1. Introduction to Machine Learning
- Overview of Machine Learning: History, applications, and significance
- Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
- Machine Learning Workflow: Data collection, preprocessing, model building, evaluation, deployment
2. Python Fundamentals for Machine Learning
- Introduction to Python for Data Science: Basics of Python programming
- Essential Libraries: NumPy, Pandas, Matplotlib, Scikit-learn
- Data Visualization: Plotting data for analysis and presentation
3. Data Preprocessing and Exploration
- Data Cleaning: Handling missing data, outliers, and noise
- Data Transformation: Normalization, scaling, encoding categorical variables
- Exploratory Data Analysis (EDA): Visualizing data distributions, correlations, and patterns
4. Supervised Learning Algorithms
- Linear Regression: Simple and multiple linear regression
- Logistic Regression: Binary and multiclass classification
- Decision Trees and Random Forests: Ensemble methods for classification and regression
- Support Vector Machines (SVM): Kernel methods for classification and regression
5. Model Evaluation and Validation
- Model Selection: Training and testing datasets, cross-validation
- Evaluation Metrics: Accuracy, precision, recall, F1-score, ROC curves
- Overfitting and Underfitting: Bias-variance trade-off, regularization techniques
6. Unsupervised Learning Algorithms
- Clustering Techniques: K-means, hierarchical clustering
- Dimensionality Reduction: Principal Component Analysis (PCA), t-SNE
- Association Rule Learning: Apriori algorithm for market basket analysis
7. Neural Networks and Deep Learning
- Introduction to Neural Networks: Perceptron, activation functions
- Deep Learning Fundamentals: Feedforward Neural Networks, Backpropagation
- Convolutional Neural Networks (CNNs): Image recognition and classification
- Recurrent Neural Networks (RNNs): Sequence modeling, text generation
8. Natural Language Processing (NLP)
- Text Preprocessing: Tokenization, stemming, lemmatization
- Bag-of-Words and TF-IDF Models: Representing text data
- Sentiment Analysis: Classifying sentiment from text data
- Named Entity Recognition (NER) and Text Generation
9. Reinforcement Learning
- Introduction to Reinforcement Learning: Markov Decision Processes (MDPs)
- Q-Learning and Deep Q-Learning: Learning optimal policies
- Applications of Reinforcement Learning: Game playing, robotics
10. Machine Learning Deployment and Production
- Model Deployment: Exporting models, integrating with web applications
- Containerization and Microservices: Docker, Kubernetes for scalable deployments
- Model Monitoring: Performance tracking, feedback loops, and updates
11. Advanced Topics in Machine Learning
- Transfer Learning: Using pre-trained models for new tasks
- Generative Adversarial Networks (GANs): Generating synthetic data
- Explainable AI: Interpreting model decisions, fairness in AI
12. Ethical Considerations in Machine Learning
- Bias and Fairness: Addressing biases in data and models
- Privacy and Security: Protecting sensitive information
- AI Ethics Guidelines and Regulations
13. Machine Learning Projects and Case Studies
- Real-world Machine Learning Projects: Implementation and evaluation
- Case Studies: Industry-specific applications (e.g., healthcare, finance)
- Presentation and Documentation of Machine Learning Projects
Training
Basic Level Training
Duration : 1 Month
Advanced Level Training
Duration : 1 Month
Project Level Training
Duration : 1 Month
Total Training Period
Duration : 3 Months
Course Mode :
Available Online / Offline
Course Fees :
Please contact the office for details