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Machine Learning Foundation is a hands-on introduction to the mathematics and algorithms used in Data Science, as well as creating the foundation and building the intuition necessary for solving complex machine learning problems.
Unlimited Duration
March 3, 2021
This “skills-centric” course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. In this course you will learn about:
· Popular machine learning algorithms, their applicability and limitations
· Practical application of these methods in a machine learning environment
· Practical use cases and limitations of algorithms
· Core machine learning mathematics and statistics
· Supervised Learning vs. Unsupervised Learning
· Classification Algorithms including Support Vector Machines, Discriminant Analysis, Naïve Bayes, and Nearest Neighbor
· Regression Algorithms including Linear and Logistic Regression, Generalized Linear Modeling, Support Vector Regression, Decision Trees, k-Nearest Neighbors (KNN)
· Clustering Algorithms including k-Means, Fuzzy clustering, Gaussian Mixture
· Neural Networks including Hidden Markov (HMM), Recurrent (RNN) and Long-Short Term Memory (LSTM)
· Dimensionality Reduction, Single Value Decomposition (SVD), Principle Component Analysis (PCA)
· How to choose an algorithm for a given problem
· How to choose parameters and activation functions
· Ensemble methods
Course Curriculum
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- Statistics Overview and Review 00:00:00
- Mean, Median, Variance, and deviation 00:00:00
- Normal / Gaussian Distribution 00:00:00
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- Probability Theory 00:00:00
- Discrete Probability Distributions 00:00:00
- Continuous Probability Distributions 00:00:00
- Measure-Theoretic Probability Theory 00:00:00
- Central Limit and Normal Distribution 00:00:00
- Probability Density Function 00:00:00
- Probability in Machine Learning 00:00:00
- Supervised Learning Explained 00:00:00
- Classification vs. Regression 00:00:00
- Examples of Supervised Learning 00:00:00
- Key supervised algorithms 00:00:00
- Linear Regression 00:00:00
- Logistic Regression 00:00:00
- Support Vector Regression 00:00:00
- Decision Trees 00:00:00
- Random Forests 00:00:00
- k-Means Clustering 00:00:00
- Fuzzy Clustering 00:00:00
- Gaussian Mixture Models 00:00:00
- Choosing between Supervised and Unsupervised algorithms 00:00:00
- Choosing between Classification Algorithms 00:00:00
- Choosing between Regressions 00:00:00
- Choosing Neural Networks 00:00:00
- Choosing Activation Functions 00:00:00
- Machine Learning in Python: NumPy, Pandas, SciKit-ML, and MatPlotLIb; NLTK, Keras 00:00:00
- Machine Learning in R 00:00:00
- Machine Learning in Java 00:00:00
- Machine Learning with Apache Madlib 00:00:00
- Hadoop, MapReduce, and Mahout 00:00:00
- Spark and MLLib 00:00:00
- TensorFlow 00:00:00
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