Learn Machine Learning | Data Preprocessing - The Machine Learning process
Understand the steps involved in Machine Learning: Data Pre-Processing (Import the data, Clean the data, Split into training & test sets, Feature Scaling), Modelling (Build the model, Train the model, Make predictions), and Evaluation (Calculate performance metrics, Make a verdict).
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What you'll learn :
Master Machine Learning on Python & R
Have a great intuition of many Machine Learning models
Make accurate predictions
Make powerful analysis
Make robust Machine Learning models
Create strong added value to your business
Use Machine Learning for personal purpose
Handle specific topics like Reinforcement Learning, NLP and Deep Learning
Handle advanced techniques like Dimensionality Reduction
Know which Machine Learning model to choose for each type of problem
Build an army of powerful Machine Learning models and know how to combine
them to solve any problem
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Binary Classification, Multiclass Classification, Imbalanced Classification, Overfitting, Underfitting, Hyperparameter Tuning, Ensemble Learning, Gradient Boosting, Neural Networks, Deep Learning, Convolutional Neural Networks, Recurrent Neural Networks, Transfer Learning, Dimensionality Reduction, Principal Component Analysis, Clustering, K-Nearest Neighbors, Evaluation Metrics, Confusion Matrix, Precision, Recall, F1 Score, Receiver Operating Characteristic Curve (ROC Curve), Area Under the Curve (AUC), Bias-Variance Tradeoff,Machine Learning, Data Science, Supervised Learning, Unsupervised Learning, Classification Algorithms, Decision Trees, Random Forests, Support Vector Machines, Naive Bayes, Logistic Regression, Artificial Intelligence, Pattern Recognition, Predictive Modeling, Feature Engineering, Model Evaluation, Training Set, Testing Set, Cross-validation,Scikit-learn, Pandas, NumPy, Matplotlib, Data Preprocessing, Feature Scaling, One-Hot Encoding, Handling Missing Data, Handling Categorical Data, Train-Test Split, Grid Search, Cross-Validation, Regularization, L1 Regularization, L2 Regularization, Decision Boundary, Bias Term, Weight Initialization, Learning Rate, Batch Size, Epochs, Stochastic Gradient Descent, Batch Gradient Descent, Mini-batch Gradient Descent, Early Stopping, Model Selection, Pipeline, Feature Selection,Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), Logistic Regression, Decision Trees, Random Forests, Naive Bayes, AdaBoost, Gradient Boosting, XGBoost, LightGBM, CatBoost, Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Dropout, Batch Normalization, Data Augmentation, Transfer Learning, Hyperparameter Optimization, Model Evaluation Metrics, Accuracy, Precision, Recall, F1 Score, ROC-AUC Score, Confusion Matrix, Receiver Operating Characteristic (ROC) Curve,Feature Importance, Gradient Descent, Learning Rate Decay, Overfitting/Underfitting Prevention, Regularization Techniques, Early Stopping, Model Stacking, Bagging, Boosting, Hyperparameter Tuning Techniques, Bayesian Optimization, Randomized Search, Pipeline, Grid Search, Model Selection, Learning Curve Analysis, Precision-Recall Curve, ROC Curve Analysis, Confusion Matrix Analysis, AUC-ROC Score, F-Beta Score, Confusion Matrix Visualization, Stratified Sampling, Imbalanced Data Handling Techniques, Sampling Techniques (Oversampling and Undersampling), SMOTE, Ensemble Techniques, Model Interpretability, SHAP Values, LIME,Class Weight Balancing, One-vs-One Classification, One-vs-All Classification, Multilabel Classification, Multiclass Classification, Error Analysis, Feature Engineering, Recursive Feature Elimination, Principal Component Analysis (PCA), Independent Component Analysis (ICA), t-Distributed Stochastic Neighbor Embedding (t-SNE), Lasso Regression, Ridge Regression, Elastic Net, Naive Bayes Classifier Variations (e.g. Gaussian, Multinomial, Bernoulli), Perceptron, Support Vector Classifier (SVC), Kernel Trick, Decision Tree Pruning, Adaboost Classifier, Gradient Boosting Classifier, XGBoost Classifier, LightGBM Classifier, CatBoost Classifier, Random Forest Classifier, Confusion Matrix Heatmap, Precision-Recall Tradeoff, Receiver Operating Characteristic (ROC) Analysis,data mining, data visualization, decision trees, random forests, support vector machines, logistic regression, naive bayes, k-nearest neighbors, clustering, anomaly detection, feature engineering, model selection,hyperparameter tuning, ensemble learning, bagging
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