Machine-Learning-Specialization

Model based techniques to extract information from data

Machine Learning, GraphLab, Numpy, Scipy

Fundamentals

Tasks:

• Identify potential applications of machine learning in practice.
• Describe the core differences in analyses enabled by regression, classification, and clustering.
• Select the appropriate machine learning task for a potential application.
• Apply regression, classification, clustering, retrieval, recommender systems, and deep learning.
• Represent data as features to serve as input to machine learning models.
• Assess the model quality in terms of relevant error metrics for each task.
• Utilize a dataset to fit a model to analyze new data.
• Build an end-to-end application that uses machine learning at its core.
• Implement these techniques in Python.

Regression

Models:

• Linear Regression
• Regularization: Ridge(L2), Lasso(L1)
• Neareat neighbor and kernel regression

Algorithms:

• Gradient descent
• Coordinate descent

Concepts:

• Loss funcitons
• bias-variance tradeoff
• cross validation
• sparsity
• overfitting
• model selection
• feature selection

Tasks:

• Describe the input and output of a regression model.
• Compare and contrast bias and variance when modeling data.
• Estimate model parameters using optimization algorithms.
• Tune parameters with cross validation.
• Analyze the performance of the model.
• Describe the notion of sparsity and how LASSO leads to sparse solutions.
• Deploy methods to select between models.
• Exploit the model to form predictions.
• Build a regression model to predict prices using a housing dataset.
• Implement these techniques in Python.

Classification

Tasks:

• Describe the input and output of a classification model.
• Tackle both binary and multiclass classification problems.
• Implement a logistic regression model for large-scale classification.
• Create a non-linear model using decision trees.
• Improve the performance of any model using boosting.
• Scale methods with stochastic gradient ascent.
• Describe the underlying decision boundaries.
• Build a classification model to predict sentiment in a product review dataset.
• Analyze financial data to predict loan defaults.
• Use techniques for handling missing data.
• Evaluate models using precision-recall metrics.

Clustering

Models:

• Nearest Neighbors
• Clustering
• Mixture of Gaussians
• Latent Dirichlet Allocation

Algorithms:

• KD-trees
• Locality sensitve hashing
• K-means
• MapReduce
• Expectation Maximization
• Gibbs sampling

Core ML:

• Distance Metrics
• Approximation algorithms
• Unsupervised learning
• Probabilistic modeling
• Data Parallel problems
• Bayesian inference

Tasks:

• Create a document retrieval system using k-nearest neighbors.
• Identify various similarity metrics for text data.
• Reduce computations in k-nearest neighbor search by using KD-trees.
• Produce approximate nearest neighbors using locality sensitive hashing.
• Compare and contrast supervised and unsupervised learning tasks.
• Cluster documents by topic using k-means.
• Describe how to parallelize k-means using MapReduce.
• Examine probabilistic clustering approaches using mixtures models.
• Fit a mixture of Gaussian model using expectation maximization (EM).
• Perform mixed membership modeling using latent Dirichlet allocation (LDA).
• Describe the steps of a Gibbs sampler and how to use its output to draw inferences.
• Compare and contrast initialization techniques for non-convex optimization objectives.
• Implement these techniques in Python.