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Python For Machine Learning Course
Unlock the potential of Python for machine learning with our detailed course, specifically tailored for technology professionals in India. Delve into regression algorithms like Random Forests and Decision Trees, and get a strong grip on model evaluation metrics such as RMSE and MAE. Learn crucial data pre-processing techniques, including feature scaling and encoding, which are essential for real-world datasets. Develop practical skills in feature selection methods, project documentation, and widely-used Python libraries such as NumPy and Pandas. Further enhance model performance with hyperparameter tuning and ensemble methods. Enrol now to boost your expertise in machine learning within the Indian context.
- Master regression techniques: Implement Random Forests, Decision Trees, and Linear Regression, understanding their nuances for Indian datasets.
- Evaluate model performance: Use RMSE, MAE, and cross-validation to rigorously assess model accuracy and reliability.
- Pre-process data effectively: Scale features appropriately, handle missing data points, and encode categorical variables commonly found in Indian data.
- Optimize models efficiently: Apply hyperparameter tuning techniques, explore ensemble methods, and employ effective search strategies to fine-tune model performance.
- Analyse data comprehensively: Utilize NumPy, Pandas, Matplotlib, and Seaborn to extract meaningful insights from data, crucial for informed decision-making.
flexible workload of 4 to 360h
certificate recognized by MEC
What will I learn?
Unlock the potential of Python for machine learning with our detailed course, specifically tailored for technology professionals in India. Delve into regression algorithms like Random Forests and Decision Trees, and get a strong grip on model evaluation metrics such as RMSE and MAE. Learn crucial data pre-processing techniques, including feature scaling and encoding, which are essential for real-world datasets. Develop practical skills in feature selection methods, project documentation, and widely-used Python libraries such as NumPy and Pandas. Further enhance model performance with hyperparameter tuning and ensemble methods. Enrol now to boost your expertise in machine learning within the Indian context.
Elevify advantages
Develop skills
- Master regression techniques: Implement Random Forests, Decision Trees, and Linear Regression, understanding their nuances for Indian datasets.
- Evaluate model performance: Use RMSE, MAE, and cross-validation to rigorously assess model accuracy and reliability.
- Pre-process data effectively: Scale features appropriately, handle missing data points, and encode categorical variables commonly found in Indian data.
- Optimize models efficiently: Apply hyperparameter tuning techniques, explore ensemble methods, and employ effective search strategies to fine-tune model performance.
- Analyse data comprehensively: Utilize NumPy, Pandas, Matplotlib, and Seaborn to extract meaningful insights from data, crucial for informed decision-making.
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