CERTIFICATES

This module provides the basic introduction of the machine learning with the concepts like Reinforcement learning, reinforcement learning, decision trees, neural networks, support vector machines, clustering, regression analysis, feature engineering, cross-validation, bias-variance tradeoff, overfitting, and underfitting are used to evaluate models.

This module explains the concepts of Linear algebra like vectors and matrices, linear transformations, eigenvalues and eigenvectors, orthogonality and projection, Singular Value Decomposition (SVD), matrix operations, determinants and rank, systems of linear equations, norms and distances, vector spaces and subspaces, statistics, probability theory, inferential statistics, correlation and regression analysis, sampling distributions, experimental design and analysis, statistical models, maximum likelihood estimation (MLE), Bayesian statistics and inference.

This module explains the concepts of the data preprocessing like data cleaning, transformation, data reduction, data discretization, and data sampling.

This module explains the Regression concepts like Linear regression, Polynomial regression, Regularization , Evaluation metrics (MSE, RMSE, R-squared, MAE) and Classification topics like Logistic regression, Naive Bayes, Decision trees, Random forests and Evaluation metrics (accuracy, precision, recall, F1-score, ROC curve, AUC).

This module explains the clustering algorithms like  K-Means, Hierarchical Clustering, Dimensionality Reduction techniques, Clustering evaluation metrics, Feature scaling and normalization and Applications of clustering and dimensionality reduction in various domains.

This module makes you to learn about the  Artificial Neural Networks (ANNs), Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Learning, Transfer Learning and Generative Adversarial Networks (GANs).

This module explains about the text preprocessing, Part-of-speech (POS) tagging, Sentiment analysis, Topic modeling, Language modeling, Machine translation and Text classification.

This module provides information about the time Series Data, Stationarity, Autocorrelation and Partial Autocorrelation, ARIMA Model, Exponential Smoothing and LSTM Networks.

This module explains about the concepts like Markov Decision Processes, Policy, Value Functions, Q-Learning, Exploration vs Exploitation, Reward function and Temporal Difference Learning.

This module explains about the Train/Test Split, Cross-validation, Bias-Variance Tradeoff, Evaluation Metrics, Grid Search and Hyperparameter Tuning, Ensembling and Model Selection.

This module explains about the Grid Search, Random Search, Bayesian Optimization, Cross-Validation, Overfitting and Underfitting and Performance Metrics.

This module explains the concepts like Model serving, Model versioning, Containerization, Cloud deployment, API development, Performance monitoring, Security and privacy, Scalability and Continuous integration and deployment.