10 Courses recommended for beginners in machine learning

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The following is a list of, mostly free, machine learning online courses for beginners.

If Video lectures aren ' t your thing, and books better suit your learning style, then bes sure to check out our list of free Machine learning Books.

1. Machine Learning (free)

Andrew Ng

First, and arguably the most popular course on this list, machine Learning provides a broad introduction to Machi NE Learning, data mining, and statistical pattern recognition.

Topics include:

• Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
• Unsupervised Learning (clustering, dimensionality reduction, recommender systems, deep learning).
• Best Practices on machine learning (bias/variance theory; innovation process in machine learning and AI).

The course would also draw from numerous case studies and applications, so that you'll also learn how to apply learning ALG Orithms to building smart robots (perception, control), text understanding (Web search, anti-spam), computer vision, Medic Al Informatics, audio, database mining, and other areas.

The course is one weeks long and averages a 4.9/5 user rating, currently. It is the free-to-take and can pay $79 for a certificate upon course completion.

2. Machine learning foundations:a Case STUDY approach (free)

Carlos Guestrin, Emily Fox

In Machine Learning foundations:a case Study approach, you'll get hands-on experience with machine learning fr Om a series of practical case-studies. At the end of it you'll have studied how to predict house prices based on House-level features, analyze sentiment from u Ser reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you'll be able to apply machine learning methods in a wide range of DOMA Ins.

By the end of this course, you'll be able to:

• 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 your 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, uses machine learning in its core.
• Implement these techniques in Python.

The course is 6 weeks long and requires about 5-8 hours of commitment per week. It currently averages a 4.6/5 user rating and is free to take, but can pay $59 for a certificate upon completion.

3. Learning from DATA (free)

Yaser S. Abu-mostafa

Learning from Data is a introductory course in machine learning that would cover basic theory, algorithms, and AP Plications.

It balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures follow each other in a story-like fashion:

• What is learning?
• Can a machine learn?
• How to do it?
• How to does it well?
• Take-home lessons.

You ' ll learn how to:

• Identify Basic theoretical principles, algorithms, and applications of machine learning
• Elaborate on the connections between theory and practice in machine learning
• Master the mathematical and heuristic aspects of machine learning and their applications to real world situations

The course is ten weeks long and requires about 10–20 hours per week of commitment. It is free to take, but can add a verified certificate of completion for $49.

4. Statistical learning (FREE)

Trevor Hastie, Rob Tibshirani.

A introductory-level course in supervised learning with a focus on regression and classification methods.

The syllabus includes:

• Linear and polynomial regression, logistic regression and Linear discriminant analysis
• Cross-validation and the bootstrap, model selection and regularization methods (ridge and Lasso)
• Nonlinear models, splines and generalized additive models
• tree-based methods, random forests and boosting; Support-vector Machines

Also, some unsupervised learning methods is discussed like principal components and clustering (K-means and hierarchical) .

This isn't a Math-heavy class and all computing are do in R. If you were not a familiar with R, it is OK. There is lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that IMP Lement the techniques in each chapter.

The class is free to take and is expected of your to commits hours per week to work through the course material. If you complete the course, and achieve a passing grade of 50% on the quizzes. If you get 90% or higher, your statement'll be ' with distinction '.

5. Machine Learning:regression (free)

Carlos Guestrin, Emily Fox

In machine learning:regression, you'll explore regularized linear Regression models for the task of prediction and feature selection. You'll be able to handle very large sets of features and select between models of various complexity. You'll also analyze the impact of aspects of your Data-such as Outliers-on your selected models and predictions. To fit these models, you'll implement optimization algorithms that scale to large datasets.

By the end of this course, you'll be able to:

• 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

The course requires 6 weeks of your time and approximately 5–8 hours per week to study the material. It ' s Current user rating averages a 4.8/5. The course is free-to-take and can pay $59 to receive a certificate of completion at the end.

6. Machine Learning:classification (free)

Carlos Guestrin, Emily Fox

In machine learning:classification, you'll create classifiers that provide state-of-the-art performance on a V Ariety of tasks. You'll become familiar with the most successful techniques, which is most widely used in practice, including logistic R Egression, decision trees and boosting. In addition, you'll be able to design and implement the underlying algorithms so can learn these models at scale, usin G Stochastic gradient ascent. You'll implement these technique on Real-world and large-scale machine learning tasks. You'll also address significant tasks you'll face in real-world applications of ML, including handling missing data an D measuring precision and recall to evaluate a classifier. This course was hands-on, action-packed, and full of visualizations and illustrations of how these techniques would behave o n Real data.

By the end of this course, you'll be able to:

• Describe The input and output of a classification model
• tackle both binary and Multiclass Classificati On problems
• Implement a logistic regression model for large-scale classification
• Create a non-linear m Odel using decision Trees
• Improve The performance of any model using boosting
• scale your 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 TEC Hniques for handling missing data
• Evaluate your models using Precision-recall metrics
• Implement These Techniques in Python (or in the language of your choice, though Python is highly recommended)

The course is 7 weeks long and currently averages a 4.6/5 user rating. While the course materials is provided for free, you'll need to pay $59 to earn a course completion certificate.

7. Learning:clustering & Retrieval (free)

Carlos Guestrin, Emily Fox

In Machine learning:clustering & retrieval You'll examine similarity-based algorithms for retrieval. You'll also examine structured representations for describing the documents in the corpus, including clustering and Mixe D membership models, such as latent Dirichlet allocation (LDA). You'll implement expectation maximization (EM) to learn the document Clusterings, and see how to scale the methods using Mapreduce.

By the end of this course, you'll be able to:

• Create A document retrieval system using K-nearest neighbors
• Identify Various similarity metrics for t Ext data
• Reduce computations in k-nearest neighbor search by using kd-trees
• produce approximate neares T 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 it output to draw inferences
• Compare and contrast initialization techniques for Non-convex optimization objectives
• Implement These techniques in Python

The course is 6 weeks in length and currently averages a 4.9/5 user rating. The course materials is free and you'll need to pay $59 if you want a course completion certificate.

8. Unsupervised machine learning HIDDEN MARKOV MODELS in PYTHON ($)

Justin C

While the current fad in deep learning are to use recurrent neural networks to model sequences, this course would introduce Learning algorithm that have been around for several decades now–the Hidden Markov Model.

In unsupervised machine learning Hidden Markov Models in Python, you'll learn to measure the probability Distribu tion of a sequence of random variables.

In this course you ' ll learn:

• How to use gradient descent to solve for the optimal parameters of a HMM, as an alternative to the popular EXPE Ctation-maximization algorithm.
• How to work with sequences in Theano, a popular library for deep learning
• How to look at the a model of sickness and health, and calculate how to predict how long you'll stay sick, if you get sick
• How Markov models can is used to analyze what people interact with your website, and fix problem areas Bounce rate, which could be affecting your SEO
• practical applications of Markov models, including generating IM Ages, smartphone autosuggestions, and using HMMs to answer one of the most fundamental questions in Biology–how are DNA, The Code of Life, translated to physical or behavioral attributes of an organism?

The course is comprised of videos and runs a total time of 4 hours. It currently averages a 4.7/5 user rating. However, the course isn't free, it costs $.

9. DATA Science and machine learning with Python–hands on! ($)

Frank Kane

If you ' ve got some programming or scripting experience, Data Science and Machine learning with Python–hands on! Would teach you the techniques used by real data scientists in the tech Industry–and prepare-a move into this ho T career path. This comprehensive course includes-lectures spanning almost 9 hours of video, and most topics include hands-on Python C Ode examples you can use for reference and for practice.

The topics in this course come from a analysis of real requirements in data scientist job listings from the biggest tech Employers. It covers the machine learning and data mining techniques real employers is looking for, including:

• Regression Analysis
• K-means Clustering
• Principal Component Analysis
• Train/test and Cross validation
• Bayesian Methods
• Decision Trees and Random forests
• Multivariate Regression
• Multi-level Models
• Support Vector Machines
• Reinforcement learning
• Collaborative Filtering
• K-nearest Neighbor
• Bias/variance Tradeoff
• Ensemble Learning
• Term frequency/inverse Document Frequency
• Experimental Design and A/b Tests

The course costs and currently have an average user rating of 4.6/5.

Learning for DATA Science and ANALYTICS

Ansaf Salleb-aouissi, Cliff Stein, David Blei, Itsik Peer, Mihalis Yannakakis, Peter Orbanz

Machine Learning for Data Science and Analytics are an introduction to machine learning and algorithms. You'll develop a basic understanding of the principles of machine learning and derive practical solutions using PREDICTI ve Analytics. You'll also examine why algorithms play a essential role in Big Data analysis.

In this course, you ' ll learn:

• What machine learning are and how it's related to statistics and data analysis
• How machine learning uses computer algorithms to search for patterns in data
• How to use data patterns to make decisions and predictions with real-world examples from healthcare involving genomics and Preterm birth
• How to uncover hidden themes in large collections of documents using topic modeling
• How to prepare data, deal with missing data and create custom data analysis solutions for different industries
• Basic and frequently used algorithmic techniques including sorting, searching, greedy algorithms and dynamic programming

The course is 5 weeks and requires a commitment of 7-10 hours per week. It is free and you have the option of paying $ for a verified certificate of completion.

10 Courses recommended for beginners in machine learning