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Python With Machine Learning Course
Unleash the potential of Python and machine learning with our detailed course, designed specifically for tech professionals in India. Delve into feature engineering, mastering techniques like scaling, normalisation, and handling categorical variables. Boost your abilities with model optimisation methods such as random search and hyperparameter tuning. Work with data from sources like UCI and Kaggle, and become proficient in algorithms including random forests and decision trees. Enhance your expertise with practical, top-notch training crafted for real-world application in the Indian context.
- Become proficient in feature engineering: Scale, normalise, and create time-based features.
- Optimise models: Learn hyperparameter tuning and grid search methods.
- Source data efficiently: Make use of UCI and Kaggle for relevant datasets.
- Implement algorithms: Explore Random Forests, Decision Trees, and Linear Regression.
- Evaluate models: Use MAE, RMSE, and data splitting for performance analysis.

flexible workload of 4 to 360h
certificate recognized by MEC
What will I learn?
Unleash the potential of Python and machine learning with our detailed course, designed specifically for tech professionals in India. Delve into feature engineering, mastering techniques like scaling, normalisation, and handling categorical variables. Boost your abilities with model optimisation methods such as random search and hyperparameter tuning. Work with data from sources like UCI and Kaggle, and become proficient in algorithms including random forests and decision trees. Enhance your expertise with practical, top-notch training crafted for real-world application in the Indian context.
Elevify advantages
Develop skills
- Become proficient in feature engineering: Scale, normalise, and create time-based features.
- Optimise models: Learn hyperparameter tuning and grid search methods.
- Source data efficiently: Make use of UCI and Kaggle for relevant datasets.
- Implement algorithms: Explore Random Forests, Decision Trees, and Linear Regression.
- Evaluate models: Use MAE, RMSE, and data splitting for performance analysis.
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Before starting, you can change the chapters and the workload. Choose which chapter to start with. Add or remove chapters. Increase or decrease the course workload.What our students say
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