83% Off Machine Learning A-Z™: AI, Python & R + ChatGPT Bonus [2023] | Udemy Review & Coupon

Discover how to develop Machine Learning Algorithms in both Python and R with the guidance of two Data Science experts. The code templates are provided.

This course covers:

This course, Machine Learning A-Z™: AI, Python & R + ChatGPT Bonus is an online learning program that helps you to learn the foundations of AI, Python, and R programming. This comprehensive program consists of 42 hours of on-demand video, 39 articles, and 9 downloadable resources and also allows you access on mobile and TV. Upon completion of this course, you will receive a certificate of completion.

What you'll learn

This topic will enable students to understand the powerful potential of machine learning within the Python and R programming languages. By developing a great intuition of many machine learning models, students learn how to apply these predictive models in order to make accurate predictions from data. Machine learning enables businesses to undertake powerful analysis, making informed decisions based on this data. Not only that but you can also create strong added value for your business by constructing robust machine learning models with appropriate datasets.

This is a powerful set of skills - once you perfect them you’ll be able to better predict variables such as future demand and customer behaviour. It also enables more efficient forecasting, allowing businesses to remain competitive in their respective markets. Understanding how to utilise machine learning tools can become incredibly useful when seeking out correlations and patterns in massive datasets – these insights will give any business a clear strategic advantage against competitors. Once understood, businesses can deploy automatic process control improvements that were previously unimaginable prior to embracing this technology.

About Instructor

Kirill Eremenko

Kirill is a Data Scientist. He has professional experience in the Data Science consulting field, with expertise in various industries including finance, retail, and transportation. After receiving training from top analytics mentors at Deloitte Australia, he has shared his knowledge with thousands of aspiring data scientists through Udemy.

Through his courses, you will observe the integration of his experience in Physics and Mathematics with his academic background to provide comprehensive guidance in the field of Data Science. One of the strengths of his teaching style is his focus on intuitive explanations, ensuring comprehension of even the most complex topics.

In conclusion, he has a strong enthusiasm for Data Science and is eager to impart his expertise and passion to you.

Hadelin de Ponteves

Hadelin has developed over 30 educational e-courses on various technology topics, including Artificial Intelligence, Machine Learning, Deep Learning, Blockchain, and Cryptocurrencies, which have received high ratings from users worldwide. He is dedicated to sharing this knowledge with others and assisting as many individuals as possible. The number of students subscribed to his courses has exceeded 1.6 million.

Requirements

Requirements are a fundamental element of any academic endeavor. High school math is no exception and in fact can help to set the foundation for learning more complex material in the future. Requirements must be established to understand the level of knowledge a student should possess in order to make progress through a course in mathematics.

Description

This course is ideal for anyone who wants to get a comprehensive overview of the field of Machine Learning. With over 900,000 students enrolled, this course design by Data Scientists and Machine Learning experts has been successfully helping people around the world develop their knowledge and skills in this rapidly-expanding field. The course is designed to help those understand the underlying theory behind algorithms, coding libraries and other related concepts in an easy-to-understand manner.

Each tutorial within this course will provide learners with step-by-step instructions to help them gain new insights into complex topics such as supervised learning, unsupervised learning, deep learning and natural language processing. At the end of it all, learners will have acquired valuable insight into how Machine Learning can be used to solve real-world problems efficiently. In conclusion, this course helps everyone seeking entry into the world of Machine Learning learn everything they need to know!

This course combines both entertainment and education, as it delves extensively into Machine Learning. The structure is as follows:

• Part 1 - Data preprocessing is a crucial step in data analysis.
• Part 2 - of the topic covers various regression techniques such as Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, and Random Forest Regression.
• Part 3 - Classification: The following methods can be used for classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, and Random Forest Classification.
• Part 4 - The two main types of clustering are K-Means and Hierarchical Clustering.
• Part 5 - Two common methods of Association Rule Learning are Apriori and Eclat.
• Part 6 - The topics covered in Reinforcement Learning are Upper Confidence Bound and Thompson Sampling.
• Part 7 - The field of Natural Language Processing involves the use of bag-of-words models and algorithms.
• Part 8 - Deep Learning involves the use of Artificial Neural Networks and Convolutional Neural Networks.
• Part 9 - The methods for dimensionality reduction include PCA, LDA, and Kernel PCA.
• Part 10 - The methods involved in model selection and boosting include k-fold cross validation, parameter tuning, grid search, and XGBoost.

The sections within each part operate independently. The course offers the flexibility to either complete it in its entirety or choose specific sections according to your current career needs.

In addition, the course includes practical exercises that are grounded in real-life case studies. In addition to theoretical knowledge, there is also ample opportunity for practical experience in constructing models.

The course provides Python and R code templates that can be downloaded and utilized for personal projects.

This course is intended for:

This course is designed for anyone who has an interest in Machine Learning, from people with just basic knowledge of math and a desire to learn more about it, to those who are already comfortable working with machine learning algorithms but want a deeper understanding of everything this field encompasses. It is ideal for students in high school or college who have a yearning to enter the data science field and progress further in their career path. Data analysts looking for the next step up can also benefit greatly from this course.

The course covers all the basics right through to more advanced concepts, leaving behind no stone unturned in machine learning theory as well as its applications. Students will gain a comprehensive overview that brings together both practical and theoretical knowledge with exercises and projects which they can use to apply their newfound skills. This course is perfect for any learner wanting expert guidance on how to get started using machine learning or brushing up on their skills so they can stay competitive in an ever-evolving world of technology.