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Python With Machine Learning Course
Unlock the full power of Python and Machine Learning with our well-detailed course wey tailor-make am for tech professionals like you. Dive deep into feature engineering; sabi how to scale data, normalize am, and handle categorical variables like a pro. Sharpen your skills with correct model optimization techniques like random search and hyperparameter tuning wey go burst your brain. Explore data wey dey come from UCI and Kaggle, and become correct guru for algorithms like Random Forests and Decision Trees. Raise your game with practical, high-quality training wey dem design so you fit use am for real life projects sharp sharp.
- Master feature engineering: Learn how to scale data, normalize am, and create time-based features wey go make sense.
- Optimize models: Sabi hyperparameter tuning and grid search techniques like the back of your hand.
- Source data effectively: Use UCI and Kaggle to find the correct datasets wey you need.
- Implement algorithms: Explore Random Forests, Decision Trees, and Linear Regression and become correct master.
- Evaluate models: Use MAE, RMSE, and data splitting to check how your model dey perform.
from 4 to 360h flexible workload
certificate recognized by MEC
What will I learn?
Unlock the full power of Python and Machine Learning with our well-detailed course wey tailor-make am for tech professionals like you. Dive deep into feature engineering; sabi how to scale data, normalize am, and handle categorical variables like a pro. Sharpen your skills with correct model optimization techniques like random search and hyperparameter tuning wey go burst your brain. Explore data wey dey come from UCI and Kaggle, and become correct guru for algorithms like Random Forests and Decision Trees. Raise your game with practical, high-quality training wey dem design so you fit use am for real life projects sharp sharp.
Elevify advantages
Develop skills
- Master feature engineering: Learn how to scale data, normalize am, and create time-based features wey go make sense.
- Optimize models: Sabi hyperparameter tuning and grid search techniques like the back of your hand.
- Source data effectively: Use UCI and Kaggle to find the correct datasets wey you need.
- Implement algorithms: Explore Random Forests, Decision Trees, and Linear Regression and become correct master.
- Evaluate models: Use MAE, RMSE, and data splitting to check how your model dey perform.
Suggested summary
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