Looking to get into machine learning and unsure where to start? In this guide, we’ve given you the rundown of some of the best machine learning courses & certificates & machine learning books to help you get started.
Machine Learning has become one of the most compelling computer science disciplines in recent years. There has been an endless number of businesses that utilize machine learning to deliver more effective outcomes.
Search engines and spam filtering exist as examples whereby machine learning innovations play an integral role in daily living. Machine learning is the driving force behind what allows us to formulate mathematical principles that perform feats of ingenuity.
Below is a list of machine learning courses that concentrate on machine learning algorithms, emphasizing how they work mathematically, then using that knowledge as a baseline that transitions into programming.
Best Machine Learning Courses
1. Machine Learning from Standford (Coursera)
Enrolled: 2.3 Million
Coursera delivers an intuitive machine learning course out of Stanford, considered by some as the standard whereby other courses get compared. The founder and subsequence teacher of the course is Andrew Ng, a long-standing Standford professor, and the founder of Coursera. The course intends to equip you with fundamentally extensive knowledge of this new engineering discipline.
The course focuses primarily on using Python or R, the two open-source programming languages used for the majority of the given assignments. If you’re a novice, it might seem a little intimidating at first, because Python and R are more complicated when compared to Octave. However, the hands-on nature of the course will make it easier to learn the nuances of the subject.
- Provides a concise introduction beneficial to those of us who are beginners.
- You can easily follow the course online via the given schedule.
- As the course progresses, it takes you through interactive topics such as building intelligent robots and support vector machines.
- Upon initial sign up you’ll notice that an array of languages are supported: English, Japanese, Spanish, Hebrew, Hindi, and Chinese.
- An extremely hands-on approach is taken to communicate more advanced topics and disciplines.
2. Professional Certificate in Data Science from Harvard (edX)
Length: 8-16 Months
No Rating Available
Harvard University brings you a professionally certified course specializing in Data Science. The course employs the aid of real-life examples that present detailed questions to give you practical experience at analyzing large sectors of data. During a bulk of the classes, you will have the task of mastering the R programming language, bulk data analysis, and numerous statistical concepts that play an integral role in extrapolating meaningful information from a given cluster of data.
As you will come to find out, majority of the case studies implemented in course arise from the numerous trends experienced in society, specifically those documented by the World Health Organization, financial sectors, and elections. Supplementing the real-life cases are programs like tidyverse and ggplot2 used for data visualizations and subsequent presentations throughout the class.
Professor Rafael Irizarry, a renowned Biostatician at Harvard, teaches the class. Professor Irizarry is known for his work on the prominent Bioconductor project.
- Employ the use of real-world examples as a means to acquire in-depth knowledge through implementing machine learning algorithms to get the job done.
- Gain familiarity with the tools that Data Scientists use on a daily bases, such as Unix/Linux, RStudio, GitHub, and git.
- Develop a working understanding of the R programming language.
- Gain valuable experience with practical tools such as tidyverse, ggplot2, and data wrangler.
- Explore various statistical concepts, such as statistics, probability, and inference.
3. Deep Learning Specialization from deeplearning.ai (Coursera)
In collaboration with current Standford Professors and nVidia deep learning institute, Professor Andrew Ng delivers us a unique, in-depth learning course brought to you by coursera. Andrew Ng is a well-known instructor who co-founded Coursera and is the current head of the Google Brain Project. Ng brings you a deep insight into various techniques that are used to build models that solve real-life problems.
Divided into five courses that that last close to a month, Ng will teach you the rudiments of Deep Learning. Delving deep into subjects such as neural networks and machine learning, you’ll learn how to integrate these topics to solve real-world challenges. Ng, seamless incorporates real case studies that draw upon topics, such as language processing and music. The popularity of Andrew Ngs contributions to the programming industry has made this course immensely popular, and it has already garnered praise as one of the best Deep Learning Courses available.
- Gain a deeper understanding of various convolutional networks.
- Get hands-on experience analyzing real-life cases in healthcare, music generation, and sign language reading.
- Industry leaders and experts provide real-world insights easily translatable into the modern workplace.
- Obtain a special machine learning certificate if you complete all assignments on time.
- Riffle through Deep Learning techniques that you’ll use to construct real-life models to tackle the everyday problems.
4.Deep Learning from IBM (edX)
Length: 8-16 Months
No Rating Available
Deep Learning brought to you by IBM focuses on the production level design of deep learning systems while succinctly intertwining a detailed introduction to various frameworks like Apache Spark, IBM Bluemix platform, and systemml. Using advanced structures and real-time data makes exploring deep learning more beneficial throughout IBMs course, which covers a total of four weeks.
IBM’s machine learning certification delves into the inner workings of deep learning, providing you a clear pathway to jump directly into a promising career field. The course walks you through various concepts and utilization of in-depth knowledge, incorporating neural networks within both supervised and unsupervised learning. Distinctive libraries such as TensorFlow, Keras, and PyTorch are used to construct models and mathematical algorithms to build models used to solve real-world problems.
By working on assignments, labs, and projects with real-world applications, you acquire valuable experience that culminates with a final capstone project designed. The IBM course also includes natural language processing at a remarkably large scale by leveraging expedited GPU tools for increased image and video processing and object recognition.
- You’ll have the ability to train, deploy, and build Deep Learning programs that incorporate convolutional, autoencoders, and recurrent networks.
- The course uses quizzes and assignments that serve as a tool to gauge how well you’re performing in the course and grasping the information.
- You’ll be equipped with a certification that dictates the skills that you’ve acquired to become successful in the AI industry.
- Consists of 5 courses that last 5 to 6 weeks per course.
5. Machine Learning A-Z: Hands-On Python & R In Data Science (Udemy)
Kirill Eremenko an established Data Scientist and Forex specialist, joined forces with Hadelin de Ponteves, a Data Scientist to create one of the most popular Machine Learning programs to date. With more than 411,000 students in the course, Machine Learning A-Z does an exceptional job at relaying complex subjects in a simple yet effective way. The course provides the learner with a thorough understanding of Python and R programming languages.
As the program continues, you’ll gain the necessary skills to make precise predictions, build effective machine learning models, and traverse NLP and Deep learning tools. Alongside the frequent use of specific software, you’ll acquire essential skills to choose the right models that fit every problem you’ll face.
- The course is fitted with practical exercises and quizzes that gauge progression.
- You’ll receive bonus training in Python and R code template that can be used for creating other projects.
Best Machine Learning Books
If you’re an absolute beginner to programming, one book you might want to consider picking up is Machine Learning for Absolute Beginners: A Plain English Introduction by Oliver Theobald.
This step-by-step guide will introduce you to machine learning, take you through the basics, and teach you how to build your first machine learning model. This is just book one in Theobald’s seven-part series, too.
Another excellent book for machine learning beginners is Aurélien Géron’s Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow.
The book covers basic machine learning concepts, focuses on TensorFlow, and takes you through projects from start-to-end. You’ll learn about neural net architectures and gain a better understanding of training deep neural nets.
If you’re looking for a machine learning book that cuts through all the fluff and gives you a good first-look at machine learning, The Hundred-Page Machine Learning Book by Andriy Burkov is also worth checking out.
As its name suggests, the book is just 100-pages long. However, it still comes packed with information and is an excellent place to start for beginners. This books doesn’t deal with actual code, but it does serve well in explaining machine learning concepts.
Whether you’re looking for the best machine learning course to take, or you want a book on machine learning that will introduce you to the key concepts of the new emerging field, any of the five courses and the three books listed above will give you a good place to start.