Machine Learning Fundamentals Course

Comprehensive machine learning course designed for beginners to intermediate level. Course Structure: - 12 modules, 40+ hours of content - 50+ hands-on exercises - 8 real-world projects - Interactive coding environments - Certification upon completion Modules: 1. Introduction to Machine Learning 2. Data Preprocessing and Cleaning 3. Supervised Learning Algorithms 4. Unsupervised Learning Techniques 5. Model Evaluation and Validation 6. Feature Engineering 7. Deep Learning Fundamentals 8. Neural Networks and Backpropagation 9. Computer Vision with CNNs 10. Natural Language Processing 11. Time Series Analysis 12. MLOps and Model Deployment Projects: - House price prediction - Customer churn analysis - Image classification - Sentiment analysis - Recommendation system - Stock price forecasting - Fraud detection - Chatbot development Tools Used: - Python and Jupyter notebooks - Scikit-learn, TensorFlow, PyTorch - Pandas, NumPy, Matplotlib - Docker for deployment - Git for version control Certification: - Hands-on project portfolio - Proctored final exam - Industry-recognized certificate - LinkedIn skill verification