Machine Learning is the study of computer algorithms that improve automatically through experience.
For starting ML from right from basics, setting everything up and getting an idea about various things, you can follow this crash course video by Academind.
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For learning deep, advanced concepts of ML and become an expert, you can go ahead with this paid course on Udemy.
* Pandas and Numpy for Data Pre-Processing and Analysis.
* Matplotlib and Seaborn for Data Visualization.
* Sklearn to implement Machine Learning Models.
* Error Measurement and Scaling techniques.
* All methods and techniques mentioned are listed below:
- Pandas 🐼
- Numpy
- Matplotlib
- Seaborn
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Models
- LinearRegression
- LogisticRegression
- DecisionTreeRegressor
- RandomForestRegressor
- KNeighborsClassifier
- SVM
- IsolationForest
- LocalOutlierFactor
- AdaBoost
- XGBoost
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Prediction Measurements
- mean_absolute_error
- accuracy_score
- classification_report
- Scaling
- StandardScaler
- RobustScaler
- Difference In Types of Scalers
- Encoding Categorical Data
- Differenent types of Encoding Techniques