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Build strong portfolio by learning trending technologies and frameworks
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Advanced Analytics with AI
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AI Electives
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Machine Learning Fundamentals In Depth Elective for Data Analysts
Learn the basics of probability, statistics, and the fundamentals of AI and ML.Understand normal distribution, business moments, and an introduction to artificial intelligence.Explore supervised learning, including linear regression, one-hot encoding, and gradient descent.Dive into classification problems with logistic regression and cost function optimization.Understand decision trees, entropy, and how information gain works in classification.Learn about random forests, bootstrapping techniques, and how to evaluate classifiers.Discover support vector machines, K-nearest neighbors, and the basics of neural networks.Explore unsupervised learning with K-Means, hierarchical clustering, PCA, and deep learning techniques.Math behind Machine Learning & Artificial Intelligence using Python
Exploring Basic Mathematical Concepts: Dive into sets, subsets, power sets, Venn diagrams, trigonometric functions, straight lines, and vectors, building a strong foundation for advanced topics.Understanding Permutations and Combinations: Learn the principles of counting and explore the practical applications of permutations and combinations in problem-solving.Exploring Key Statistical Measures: Gain insight into the first four business moments, Z-scores, confidence intervals, correlation, and covariance to analyze data effectively.Deepening Knowledge in Probability: Uncover the intricacies of probability with a focus on random experiments, conditional and joint probabilities, and delve into various probability distributions like uniform, normal, binomial, and Poisson.Discovering Likelihood in Logistic Regression: Explore likelihood statistics, odds, log odds, and learn the differences between maximum likelihood and probability in logistic regression.Mastering Gradient Descent: Learn how gradient descent helps optimize cost functions in linear and logistic regression, enhancing your understanding of machine learning algorithms.Linear Algebra for Data Science: Delve into matrices, operations, eigenvalues, and eigenvectors, discovering how these concepts are critical for Principal Component Analysis (PCA) in data reduction.Introduction to Machine Learning Algorithms and their Implementation in Python
Introduction to Data Science & Tools: Learn the fundamentals of Data Science and explore key programming tools used in the field.
Programming Basics with Python: Master essential concepts like variables, operators, data structures, and control flow in Python.
Essential Python Libraries: Dive into libraries such as NumPy, Pandas, and Matplotlib for data manipulation and visualization.
Introduction to Machine Learning: Understand core machine learning concepts like classification, cross-validation, and the bias-variance tradeoff.
Model Evaluation: Explore important evaluation metrics to assess machine learning model performance.
Exploratory Data Analysis & Feature Engineering: Learn how to import data, conduct exploratory analysis, and extract meaningful features.
Regression & Clustering: Implement univariate and multivariate linear regression, logistic regression, k-nearest neighbors, and clustering techniques in Python.
Advanced Machine Learning Models: Explore decision trees, random forests, and neural networks, with hands-on implementations in Pytho