machine learning with r online course

Machine learning is a field that has gained tremendous popularity in recent years, and it’s no wonder why. It’s the driving force behind many innovative technologies, from recommendation systems and image recognition to natural language processing and self-driving cars. If you’re interested in machine learning, you might be wondering which programming language to learn, and R is certainly a viable option.

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.

1. Machine Learning A-Z: Hands-On Python & R In Data Science

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.

2. Python for Data Science and Machine Learning Bootcamp

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!

3. Data Science, Deep Learning, & Machine Learning with Python

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.

4. Data Science and Machine Learning Bootcamp with R

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.

5. Introduction to Machine Learning for Data Science

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.

R: A Versatile Language for Data Analysis and Machine Learning

R is a programming language and environment specifically designed for data analysis and statistics. It offers a wide array of packages and libraries that make it a powerful tool for data manipulation and visualization. These features also make R a suitable choice for machine learning.

In R, you can use packages like “caret,” “randomForest,” and “xgboost” to build predictive models, perform classification and regression tasks, and even create recommendation systems. R’s extensive graphical capabilities are also a huge advantage when it comes to visualizing data and model results.

Is R or Python Faster for Machine Learning?

One of the primary concerns when choosing a language for machine learning is performance. Both R and Python have their strengths and weaknesses in this regard.

R is known for its data manipulation capabilities and statistical packages, which can be computationally intensive. However, it might not be the fastest choice for implementing certain machine learning algorithms, especially when dealing with large datasets. Python, on the other hand, has libraries like TensorFlow and PyTorch that are optimized for deep learning tasks, making it a better choice for performance-critical applications.

So, if you prioritize speed, Python may be a better option. But keep in mind that R is still a strong contender for many machine learning tasks, especially when performance isn’t a primary concern.

Is Python or R Better for Data Science?

The choice between Python and R for data science is a common dilemma. Both languages are highly regarded in the field, and the decision often depends on the specific requirements of your project.

Python is known for its versatility. It’s not only used in machine learning but also in web development, scripting, and a wide range of other applications. This versatility makes it a preferred choice for data scientists who need to integrate their data analysis and machine learning workflows into other software projects.

R, on the other hand, is tailor-made for data analysis and statistics. It excels in areas like data visualization, hypothesis testing, and statistical modeling. If your primary focus is data analysis and exploration, R might be a more natural fit.

In summary, Python’s broader applicability makes it better suited for data science tasks that require integration with other software, while R’s specialized features make it a strong choice for data analysis and statistics.

Can Python Do Everything R Can?

Python and R each have their unique strengths, but can Python do everything that R can do? The answer is, in most cases, yes.

Python’s ecosystem is vast and continuously growing. It has libraries for almost every data science and machine learning task. Many of the libraries available in R have Python equivalents, and some Python libraries are even more widely adopted and actively maintained. For instance, libraries like NumPy, pandas, scikit-learn, and Matplotlib provide similar functionality to their R counterparts.

Python’s adoption in the machine learning and data science communities has led to a wealth of tutorials, documentation, and community support. This makes Python a great choice for those who prefer a language that can handle a wide range of tasks.

Is R Good for Machine Learning?

R is certainly a good choice for machine learning, especially for individuals and organizations with a strong background in statistics and data analysis. The vast number of packages available in R provides a rich ecosystem for machine learning, and its data visualization capabilities can be highly advantageous in the model-building process.

Furthermore, R’s user-friendly environment allows for rapid prototyping and experimentation, which is essential in the iterative process of machine learning model development. Data scientists and statisticians who already have experience with R will find it relatively easy to transition to machine learning tasks.

In summary, R can be an excellent choice for machine learning, especially if you are already familiar with the language or have specific statistical needs that R excels at addressing.

Is Learning R Worth It?

The decision to learn R ultimately depends on your specific goals and needs. If you’re interested in data analysis, statistics, and machine learning, R can be a valuable addition to your skill set. Learning R can make you proficient in tasks such as data manipulation, visualization, and statistical modeling, which are essential in the world of data science and machine learning.

Furthermore, R has a supportive and active community, which means you’ll have access to numerous resources and a wealth of expertise to help you along the way. Whether it’s through online courses, books, or forums, there are ample opportunities to learn and grow with R.

However, if you’re already proficient in Python and are primarily focused on machine learning, you might find that Python’s broader ecosystem and performance optimization libraries are more in line with your goals.

In conclusion, learning R is worth it if you are passionate about data analysis, statistics, and machine learning, and if you value a language tailored to these specific domains. Ultimately, the choice between R and Python will depend on your individual objectives and the scope of your projects.

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