AI ML Training

Content

• Learn the Essential Mathematical Foundations for Machine Learning & Artificial Intelligence, Implement Mathematical Concepts using Real-World Data, De -rive PCA (Principal Component Analysis) from a Projection Perspective.
• Learn Foundations of Python for ML: Significant Functions, Packages and Routines, Statistics & Probability-Descriptive and Inferential Stats, Probability, Conditional Probabilities Visualization Principles & Techniques, Linear Algebra, Calculus
• Machine Learning: Supervised Learning Regression-Linear, Multiple, Logistic, Classification (K-Nn, Naïve Bayes, Svm) Techniques, Decision Trees
• Machine Learning: Unsupervised Learning Clustering (K-Means, Hierarchical, High-Dimensional), Expectation Maximized -ion
• Machine Learning: Ensemble Method Boosting And Bagging, Random Forests
• Machine Learning: Associative Learning Aprior, Eclat, Natural Language Processing Statistical Nlp & Text Similarity, Syntax & Parsing Techniques, Text Summarization Techniques, Semantics and Generation, Topic Modelling (Lda, Tf-ldf)