Learn Machine Learning through our comprehensive live classes. Gain the essential skills to build, train, and deploy machine learning models for real-world applications. This course introduces students to the fundamentals of ML while providing hands-on experience with industry-standard tools. You'll explore supervised and unsupervised learning, neural networks, model evaluation, and deployment strategies. Through practical projects, you'll develop the ability to solve complex problems using machine learning techniques. By course completion, you'll have a strong foundation in ML concepts and practical experience with real datasets.
What Will You Learn?
This program provides both theoretical understanding and practical skills to become proficient in machine learning.
- Understand core machine learning concepts and algorithms
- Preprocess and analyze data for ML applications
- Implement regression, classification, and clustering models
- Work with neural networks and deep learning frameworks
- Evaluate model performance and optimize hyperparameters
- Deploy ML models in production environments
Entry Requirements
Essential entry requirements for machine learning course programme
- Bachelor's degree in STEM field preferred
- Intermediate Python programming skills
- Basic understanding of linear algebra and statistics
- Analytical and problem-solving mindset
Programme Highlights
- Introduction to Machine Learning
- Python for Data Science
- Data Preprocessing and EDA
- Supervised Learning Fundamentals
- Linear and Logistic Regression
- Decision Trees and Random Forests
- Support Vector Machines
- Model Evaluation Metrics
- Unsupervised Learning Techniques
- Neural Networks Basics
- TensorFlow and Keras
- Hyperparameter Tuning
- Feature Engineering
- Model Deployment Strategies
- ML in Cloud Platforms