Top Online Courses on Machine Learning with R and Python [2022 Jul] (2024)

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google’s AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Google famously announced that they are now “machine learning first”, meaning that machine learning is going to get a lot more attention now, and this is what’s going to drive innovation in the coming years. It’s embedded into all sorts of different products.

Machine learning is used in many industries, like finance, online advertising, medicine, and robotics.

It is a widely applicable tool that will benefit you no matter what industry you’re in, and it will also open up a ton of career opportunities once you get good.

Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?

Follow the best machine learning courses series to see the most updated and top rated tutorials and courses on machine learning.

Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Code templates included.

It will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time you dive deep into Machine Learning. It is structured the following way:

• Part 1 — Data Preprocessing
• Part 2 — Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression
• Part 3 — Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification
• Part 4 — Clustering: K-Means, Hierarchical Clustering
• Part 5 — Association Rule Learning: Apriori, Eclat
• Part 6 — Reinforcement Learning: Upper Confidence Bound, Thompson Sampling
• Part 7 — Natural Language Processing: Bag-of-words model and algorithms for NLP
• Part 8 — Deep Learning: Artificial Neural Networks, Convolutional Neural Networks
• Part 9 — Dimensionality Reduction: PCA, LDA, Kernel PCA
• Part 10 — Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

Moreover, the course is packed with practical exercises which are based on real-life examples. So not only you will learn the theory, but you will also get some hands-on practice building your own models.

And as a bonus, this course includes both Python and R code templates which you can download and use on your own projects.

Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more.

This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms.

This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science.

This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science and machine learning.

You will learn how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python. Here a just a few of the topics you will be learning:

• Programming with Python
• NumPy with Python
• Using pandas Data Frames to solve complex tasks
• Use pandas to handle Excel Files
• Web scraping with python
• Connect Python to SQL
• Use matplotlib and seaborn for data visualizations
• Use plotly for interactive visualizations
• Machine Learning with SciKit Learn, including:
• Linear Regression
• K Nearest Neighbors
• K Means Clustering
• Decision Trees
• Random Forests
• Natural Language Processing
• Neural Nets and Deep Learning
• Support Vector Machines
• and much, much more!

Go hands-on with the neural network, artificial intelligence, and machine learning techniques employers are seeking.

If you’ve got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry — and prepare you for a move into this hot career path. This comprehensive course includes over 80 lectures spanning 12 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice.

Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won’t find academic, deeply mathematical coverage of these algorithms in this course — the focus is on practical understanding and application of them. At the end, you’ll be given a final project to apply what you’ve learned!

The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers. It’ll cover the machine learning and data mining techniques real employers are looking for, including:

• Deep Learning / Neural Networks (MLP’s, CNN’s, RNN’s)
• 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

…and much more. There’s also an entire section on machine learning with Apache Spark, which lets you scale up these techniques to “big data” analyzed on a computing cluster. And you’ll also get access to this course’s Facebook Group, where you can stay in touch with your classmates.

Learn how to use the R programming language for data science and machine learning and data visualization.

This course is designed for both complete beginners with no programming experience or experienced developers looking to make the jump to Data Science.

It’ll teach you how to program with R, how to create amazing data visualizations, and how to use Machine Learning with R.

A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist.

In this introductory course, the “Backyard Data Scientist” will guide you through wilderness of Machine Learning for Data Science. Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the “techno sphere around us”, why it’s important now, and how it will dramatically change our world today and for days to come.

Exotic journey will include the core concepts of:

• The train wreck definition of computer science and one that will actually instead make sense.
• An explanation of data that will have you seeing data everywhere that you look!
• One of the “greatest lies” ever sold about the future computer science.
• A genuine explanation of Big Data, and how to avoid falling into the marketing hype.
• What is Artificial intelligence? Can a computer actually think? How do computers do things like navigate like a GPS or play games anyway?
• What is Machine Learning? And if a computer can think — can it learn?
• What is Data Science, and how it relates to magical unicorns!
• How Computer Science, Artificial Intelligence, Machine Learning, Big Data and Data Science interrelate to one another.

You’ll then explore the past and the future while touching on the importance, impacts and examples of Machine Learning for Data Science:

• How a perfect storm of data, computer and Machine Learning algorithms have combined together to make this important right now.
• It’ll actually make sense of how computer technology has changed over time while covering off a journey from 1956 to 2014. Do you have a super computer in your home? You might be surprised to learn the truth.
• It’ll discuss the kinds of problems Machine Learning solves, and visually explain regression, clustering and classification in a way that will intuitively make sense.

To make sense of the Machine part of Machine Learning, we’ll explore the Machine Learning process:

• How do you solve problems with Machine Learning and what are five things you must do to be successful?
• How to ask the right question, to be solved by Machine Learning.
• Identifying, obtaining and preparing the right data … and dealing with dirty data!
• How every mess is “unique” but that tidy data is like families!
• How to identify and apply Machine Learning algorithms, with exotic names like “Decision Trees”, “Neural Networks” “K’s Nearest Neighbors” and “Naive Bayesian Classifiers”
• And the biggest pitfalls to avoid and how to tune your Machine Learning models to help ensure a successful result for Data Science.

Our final section of the course will prepare you to begin your future journey into Machine Learning for Data Science after the course is complete. You’ll explore:

• How to start applying Machine Learning without losing your mind.
• What equipment Data Scientists use, (the answer might surprise you!)
• The top five tools Used for data science, including some surprising ones.
• And for each of the top five tools — we’ll explain what they are, and how to get started using them.
• And you’ll close off with some cautionary tales, so you can be the most successful you can be in applying Machine Learning to Data Science problems.

A down-to-earth, shy but confident take on machine learning techniques that you can put to work today.

Machine Learning:

Supervised/Unsupervised learning, Classification, Clustering, Association Detection, Anomaly Detection, Dimensionality Reduction, Regression.

Naive Bayes, K-nearest neighbours, Support Vector Machines, Artificial Neural Networks, K-means, Hierarchical clustering, Principal Components Analysis, Linear regression, Logistics regression, Random variables, Bayes theorem, Bias-variance tradeoff.

Natural Language Processing with Python:

Corpora, stopwords, sentence and word parsing, auto-summarization, sentiment analysis (as a special case of classification), TF-IDF, Document Distance, Text summarization, Text classification with Naive Bayes and K-Nearest Neighbours and Clustering with K-Means.

Sentiment Analysis:

Why it’s useful, Approaches to solving — Rule-Based , ML-Based , Training , Feature Extraction, Sentiment Lexicons, Regular Expressions, Twitter API, Sentiment Analysis of Tweets with Python.

Mitigating Overfitting with Ensemble Learning:

Decision trees and decision tree learning, Overfitting in decision trees, Techniques to mitigate overfitting (cross validation, regularization), Ensemble learning and Random forests.

Recommendations: Content based filtering, Collaborative filtering and Association Rules learning.

Get started with Deep learning: Apply Multi-layer perceptrons to the MNIST Digit recognition problem.

Data Science, Machine Learning, and Data Analytics Techniques for Marketing, Digital Media, Online Advertising, and More.

A/B testing is used everywhere. Marketing, retail, newsfeeds, online advertising, and more. A/B testing is all about comparing things.

If you’re a data scientist, and you want to tell the rest of the company, “logo A is better than logo B”, well you can’t just say that without proving it using numbers and statistics.

Traditional A/B testing has been around for a long time, and it’s full of approximations and confusing definitions.

First, you’ll see if you can improve on traditional A/B testing with adaptive methods. These all help you solve the explore-exploit dilemma.

You’ll learn about the epsilon-greedy algorithm, which you may have heard about in the context of reinforcement learning.

You’ll improve upon the epsilon-greedy algorithm with a similar algorithm called UCB1.

Finally, you’ll improve on both of those by using a fully Bayesian approach. Why is the Bayesian method interesting to us in machine learning? It’s an entirely different way of thinking about probability. It’s a paradigm shift. You’ll probably need to come back to this course several times before it fully sinks in. It’s also powerful, and many machine learning experts often make statements about how they “subscribe to the Bayesian school of thought”.

In sum — it’s going to give us a lot of powerful new tools that we can use in machine learning.

The things you’ll learn in this course are not only applicable to A/B testing, but rather, you’re using A/B testing as a concrete example of how Bayesian techniques can be applied. You’ll learn these fundamental tools of the Bayesian method — through the example of A/B testing — and then you’ll be able to carry those Bayesian techniques to more advanced machine learning models in the future.

Full Guide to Implementing Classic Machine Learning Algorithms in Python and with Sci-Kit Learn.

In this course, you are first going to discuss the K-Nearest Neighbor algorithm. It’s extremely simple and intuitive, and it’s a great first classification algorithm to learn. After we discuss the concepts and implement it in code, we’ll look at some ways in which KNN can fail. It’s important to know both the advantages and disadvantages of each algorithm we look at.

Next you’ll look at the Naive Bayes Classifier and the General Bayes Classifier. This is a very interesting algorithm to look at because it is grounded in probability. It’ll see how we can transform the Bayes Classifier into a linear and quadratic classifier to speed up our calculations.

Next you’ll look at the famous Decision Tree algorithm. This is the most complex of the algorithms we’ll study, and most courses you’ll look at won’t implement them. We will, since I believe implementation is good practice.

The last algorithm we’ll look at is the Perceptron algorithm. Perceptrons are the ancestor of neural networks and deep learning, so they are important to study in the context of machine learning.

One you’ve studied these algorithms, you’ll move to more practical machine learning topics. Hyperparameters, cross-validation, feature extraction, feature selection, and multiclass classification. You’ll do a comparison with deep learning so you understand the pros and cons of each approach.

We’ll discuss the Sci-Kit Learn library, because even though implementing your own algorithms is fun and educational, you should use optimized and well-tested code in your actual work.

We’ll cap things off with a very practical, real-world example by writing a web service that runs a machine learning model and makes predictions. This is something that real companies do and make money from.

Build a Portfolio of 12 Machine Learning Projects with Python, SVM, Regression, Unsupervised Machine Learning & More.

You’ll go from beginner to extremely high-level and your instructor will build each algorithm with you step by step on screen.

By the end of the course, you will have trained machine learning algorithms to classify flowers, predict house price, identify hand writings or digits, identify staff that is most likely to leave prematurely, detect cancer cells and much more.

Inside the course, you’ll learn how to:

• Set up a Python development environment correctly
• Gain complete machine learning tool sets to tackle most real world problems
• Understand the various regression, classification and other ml algorithms performance metrics such as R-squared, MSE, accuracy, confusion matrix, prevision, recall, etc. and when to use them.
• Combine multiple models with by bagging, boosting or stacking
• Make use to unsupervised Machine Learning (ML) algorithms such as Hierarchical clustering, k-means clustering etc. to understand your data
• Develop in Jupyter (IPython) notebook, Spyder and various IDE
• Communicate visually and effectively with Matplotlib and Seaborn
• Engineer new features to improve algorithm predictions
• Make use of train/test, K-fold and Stratified K-fold cross validation to select correct model and predict model perform with unseen data
• Use SVM for handwriting recognition, and classification problems in general
• Use decision trees to predict staff attrition
• Apply the association rule to retail shopping datasets

Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python.

In this course you’ll study ways to combine models like decision trees and logistic regression to build models that can reach much higher ac-curacies than the base models they are made of.

In particular, we will study the Random Forest and AdaBoost algorithms in detail.

To motivate our discussion, you will learn about an important topic in statistical learning, the bias-variance trade-off. You will then study the bootstrap technique and bagging as methods for reducing both bias and variance simultaneously.

You’ll do plenty of experiments and use these algorithms on real datasets so you can see first-hand how powerful they are.

Since deep learning is so popular these days, you will study some interesting commonalities between random forests, AdaBoost, and deep learning neural networks.

Leverage Machine Learning and TensorFlow in Python to improve your business! Build deep learning algorithms from scratch.

It starts with the very basics and covers everything you need to know. One hour into the course, you will have created your first machine learning algorithm! Isn’t that exciting! And it only gets better from there. We are not simply scratching the surface. The course digs deep into machine learning theory and practice, focusing on deep neural networks and Google’s state-of-the-art TensorFlow framework.

All sophisticated concepts are explained intuitively, with beautifully animated videos and our step-by-step approach, which makes this course an engaging and fun experience.

Here are some steps of this journey:

• Cover the minimum to create your first algorithm
• Get acquainted with Google’s TensorFlow with Python
• Apply all you see with the appropriate TensorFlow structure
• Explore layers, their building blocks, their activations (sigmoid, tanh, ReLu, softmax, …)
• Understand the backpropagation process, intuitively and mathematically
• Spot and prevent overfitting
• Get to know the state-of-the-art initialization methods
• Implement cutting-edge optimizations, such as SGD, batching, learning rate schedules
• Tackle the ‘Hello, world’ of machine learning

All these steps will lead us to the practical example, which will require you to build your first machine learning algorithm based on a real-life business problem. You will tackle it on your own, completely from scratch.

Using 10 different projects, the course focuses on breaking down the important concepts, algorithms, and functions of Machine Learning. The course starts at the very beginning with the building blocks of Machine Learning and then progresses onto more complicated concepts. Each project adds to the complexity of the concepts covered in the project before it.

• Project 1 — Stock Market Clustering Project
• Project 2 — Breast Cancer Detection
• Project 3 — Board Game Review
• Project 4 — Credit Card Fraud Detection
• Project 5 — Diabetes Onset Detection
• Project 6 — Markov Models and K-Nearest Neighbor Approaches to Classifying DNA Sequences
• Project 7 — Getting Started with Natural Language Processing In Python
• Project 8 — Obtaining Near State-of-the-Art Performance on Object Recognition Tasks Using Deep Learning
• Project 9 — Image Super Resolution with the SRCNN
• Project 10 — Natural Language Processing: Text Classification
• Project 11 — K-Means Clustering For Image Analysis
• Project 12 — Data Compression & Visualization Using Principle Component Analysis

The course covers a variety of different machine learning concepts such as supervised learning, unsupervised learning, reinforced learning and even neural networks. But that’s not all. In addition to understanding the theory behind machine learning, you will then actually use these concepts and implement them into actual projects to see how they work in action!

The course also comes with quizzes at the end of each section to help solidify your understanding for the subject. It will also help you valuate your learning of the subject.

At the end of this interactive and hands-on course, you will have everything you need to actually get started with understanding machine learning algorithms and even start writing your own algorithms that you can use for your own projects.

Find more FREE courses for Machine Learning and other programming languages on Quick Code