The Best Unsupervised Learning online courses and tutorials for beginner to learn Unsupervised Learning in 2020.
Unsupervised Learning is a Machine Learning technique, in which there is no need to supervise the model. Unsupervised Learning allows model to work on its own to discover information. It deals with unlabeled data and perform quite complex tasks compared to supervised learning. The outcomes of Supervised Learning are more unpredictable than natural learning models. You are not taught but you learn from the data and past experience or information fed.
Unsupervised Machine Learning finds all kind of unknown patterns in data. It helps in finding features useful for categorization and separation based on some common grounds. Unsupervised Learning takes place in real-time and input data is analyzed in presence of learners. All said and done, Unsupervised Learning is an important Machine Learning paradigm which is very essential for aspiring Data Scientists and Software Engineers to be aware of. Thus, we present the list of courses which will take you long way in your career in Machine Learning or Data Science.
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Top Unsupervised Learning Courses, Tutorials, Certifications list
HMMs for stock price analysis, language modeling, web analytics, biology, and PageRank.
Course rating: 4.5 out of 5.0 ( 2,074 Ratings total)
In this course, you will :
- Understand and enumerate the various applications of Markov Models and Hidden Markov Models.
- Understand how Markov Models work.
- Write a Markov Model in code and apply Markov Models to any sequence of data.
- Understand the mathematics behind Markov chains.
- Apply Markov models to language.
- Apply Markov models to website analytics.
- Understand how Google's Page-Rank works.
- Understand Hidden Markov Models.
- Write a Hidden Markov Model in Code and write a Hidden Markov Model using Theano.
- Understand how gradient descent, which is normally used in deep learning, can be used for HMMs.
You can take Unsupervised Machine Learning Hidden Markov Models in Python Certificate Course on Udemy .
Learn AI Workflow: Feature Engineering and Bias Detection from IBM. This is the third course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are.
Course rating: 4.9 out of 5.0 ( 7 Ratings total)
In this course, you will :
- You will learn best practices for feature engineering, handling class imbalances and detecting bias in the data.
- These topics will be followed by sections on best practices for dimension reduction, outlier detection, and unsupervised learning techniques for finding patterns in your data. The case studies will focus on topic modeling and data visualization.
- Employ the tools that help address class and class imbalance issues
- Learn the ethical considerations regarding bias in data
- Employ AI Fairness 360 open source libraries to detect bias in models
- Employ dimension reduction techniques for both EDA and transformations stages
- Describe topic modeling techniques in natural language processing
- Use topic modeling and visualization to explore text data
- Employ outlier handling best practices in high dimension data
- Employ outlier detection algorithms as a quality assurance tool and a modeling tool
- Employ unsupervised learning techniques using pipelines as part of the AI workflow and basic clustering algorithms
You can take AI Workflow: Feature Engineering and Bias Detection Certificate Course on Coursera.
Data science techniques for pattern recognition, data mining, k-means clustering, and hierarchical clustering, and KDE.
Course rating: 4.5 out of 5.0 ( 2,854 Ratings total)
In this course, you will :
- Understand the regular K-Means algorithm.
- Understand and enumerate the disadvantages of K-Means Clustering.
- Understand the soft or fuzzy K-Means Clustering algorithm.
- Implement Soft K-Means Clustering in Code.
- Understand Hierarchical Clustering.
- Explain algorithmically how Hierarchical Agglomerative Clustering works.
- Apply Scipy's Hierarchical Clustering library to data.
- Understand how to read a dendrogram.
- Understand the different distance metrics used in clustering.
- Understand the difference between single linkage, complete linkage, Ward linkage, and UPGMA.
- Understand the Gaussian mixture model and how to use it for density estimation.
- Explain when GMM is equivalent to K-Means Clustering.
- Explain the expectation-maximization algorithm.
- Understand how GMM overcomes some disadvantages of K-Means.
- Understand the Singular Covariance problem and how to fix it.
You can take Cluster Analysis and Unsupervised Machine Learning in Python Certificate Course on Udemy .
Learn how to use cluster analysis, association rules, and anomaly detection algorithms for unsupervised learning.
Course rating: 13,488 total enrollments
In this course you will learn:
- Cluster and distance-based measures
- Hierarchical cluster analysis and K-means cluster analysis
- Visualizing and reporting cluster solutions
- Cluster methods for categorical variables and anomaly detection
- Association rules and sequence detection
This course shows how to use leading machine-learning techniques—cluster analysis, anomaly detection, and association rules—to get accurate, meaningful results from big data.All exercises are demonstrated in IBM SPSS Modeler and IBM SPSS Statistics, but the emphasis is on concepts, not the mechanics of the software.
You can take Machine Learning and AI Foundations: Clustering and Association Certificate Course on Linkedin .
Theano / Tensorflow: Autoencoders, Restricted Boltzmann Machines, Deep Neural Networks, t-SNE and PCA
Course rating: 4.6 out of 5.0 ( 1,251 Ratings total)
In this course, you will :
- Understand the theory behind principal components analysis (PCA).
- Know why PCA is useful for dimensionality reduction, visualization, de-correlation, and denoising.
- Derive the PCA algorithm by hand and write the code for PCA.
- Understand the theory behind t-SNE and use t-SNE in code.
- Understand the limitations of PCA and t-SNE.
- Understand the theory behind autoencoders.
- Write an autoencoder in Theano and Tensorflow.
- Understand how stacked autoencoders are used in deep learning.
- Write a stacked denoising autoencoder in Theano and Tensorflow.
- Understand the theory behind restricted Boltzmann machines (RBMs).
- Understand why RBMs are hard to train.
- Understand the contrastive divergence algorithm to train RBMs.
- Write your own RBM and deep belief network (DBN) in Theano and Tensorflow.
- Visualize and interpret the features learned by autoencoders and RBMs.
In this course you will start with some very basic stuff - principal components analysis (PCA), and a popular nonlinear dimensionality reduction technique known as t-SNE (t-distributed stochastic neighbor embedding).
You can take Unsupervised Deep Learning in Python Certificate Course on Udemy .
Thus, drawing a conclusion, that Unsupervised Learning is very essential as a machine learning paradigm, is no surprise. With the drastically increasing popularity of machine learning and artificial intelligence are leaving no field unaffected, making it extremely important, as a skill, every software engineer must we equipped with.
Hence, the above compiled course list will definitely take you a long way if you are just beginning to venture in the field of machine learning. You can also refer to the compiled list of courses on Reinforcement Learning as it is an indispensable part of machine learning paradigms.
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