Master both foundational and advanced concepts in Machine Learning and NLP, applying theoretical and practical knowledge to real-world projects with Machine Learning, NLP, and MLOps. Gain a deep understanding of the mathematical principles behind ML algorithms, and learn to develop and optimize models using industry-standard tools and techniques. Build a strong grasp of core Deep Learning concepts, including optimizers, loss functions, neural networks, and convolutional neural networks (CNNs).
₹7,000.00
Machine Learning
Master the theory, practice, and mathematics behind Data Science, Machine Learning, Deep Learning, and NLP through comprehensive end-to-end projects.
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About the course
What you’ll learn
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Course content
Curriculum
- 7 Sections
- 41 Lessons
- 20 Weeks
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- Introduction to Machine Learning4
- Machine Learning Lifecycle5
- Linear Regression (Supervised Regression)9
- 3.1Business Use Cases (Sales Forecasting, Price Prediction, etc.)
- 3.2Model Assumptions
- 3.3Model Formula (Y = β0 + β1X + … + ε)
- 3.4Coefficients Interpretation
- 3.5Evaluation Metrics (R², MAE, MSE, RMSE)
- 3.6Overfitting & Underfitting
- 3.7Regularization (L1, L2) (Short Overview)
- 3.8Full Python Code Walkthrough (sklearn and Statsmodels)
- 3.9End to End Production Ready application building
- Logistic Regression (Supervised Classification)10
- 4.1Business Use Cases (Churn, Fraud, Lead Conversion)
- 4.2Sigmoid Function & Probability Output
- 4.3Odds Ratio & Log-Odds Interpretation
- 4.4Evaluation Metrics (Accuracy, Precision, Recall, F1, ROC-AUC)
- 4.5Confusion Matrix
- 4.6Threshold Tuning
- 4.7Multicollinearity (VIF)
- 4.8Full Python Code Walkthrough (sklearn and Statsmodels)
- 4.9End to End Production Ready application building
- 4.10Business Use Cases (Customer Segmentation, Market Segmentation)
- K-Means Clustering (Unsupervised Learning)7
- Model Comparison & Selection3
- Common Machine Learning Mistakes and Best Practices3
Instructor
₹7,000.00
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