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25% Off The Complete Visual Guide to Machine Learning & Data Science | Udemy Review & Coupon

25% Off The Complete Visual Guide to Machine Learning & Data Science | Udemy Review & Coupon

Learn about Data Science and Machine Learning topics through easy-to-follow demonstrations and user-friendly Excel models without the need for coding.

This course covers:

The Complete Visual Guide to Machine Learning & Data Science is an online course that offers 9 hours of on-demand video, 3 articles, and 3 downloadable resources. Additionally, the course is accessible on both mobile and TV devices, so you can learn anytime, anywhere. Upon completion of the course, you will receive a certificate of completion.

What you'll learn

In today’s digital economy, machine learning, and data science skills are essential to staying ahead. But many people avoid getting into these fields because they think that it requires a lot of coding experience or skill. Fortunately, this isn’t true. With the right resources and guidance, you can learn foundational machine learning & data science skills without writing complex code.

Experience interactive and user-friendly Excel models to gain a practical understanding of machine learning techniques.

Feature engineering techniques such as one-hot encoding, scaling, and discretization can be utilized to enhance datasets.

Classification models such as K-nearest neighbors, naïve bayes, and decision trees can be utilized to predict categorical outcomes.

Accurate forecasts and projections can be constructed through the use of linear and non-linear regression models.

Utilize effective techniques such as clustering, association mining, outlier detection, and dimensionality reduction.

Gain knowledge and skills in model selection and tuning to enhance performance, mitigate bias, and decrease drift.

The program provides opportunities to participate in practical case studies that demonstrate the application of machine learning in real-life scenarios.

About Instructor

Maven Analytics

Maven Analytics is an online platform designed for data analysts to improve their skills, exhibit their work, and network with colleagues and potential employers.

Maven's Guided Learning model has been recognized as one of the top 10 education companies revolutionizing the industry. With this model, users can create personalized learning plans, build public portfolios, connect with expert instructors and career coaches, and join a community of analytics talent that is world-class.

Our organization has assisted over one million students in developing skills that are essential for their careers, including proficiency in Excel, SQL, Power BI, Tableau, and Python.

Maven Analytics provides individuals with valuable data skills that can positively impact their lives.

Chris Dutton

Chris Dutton is an entrepreneur in the field of Education Technology and a successful instructor of Data Analytics.

As the Founder and Chief Product Officer of Maven Analytics, his work has been featured in various publications, including USA Today, Business Insider, Entrepreneur, and the New York Times, and has been accessed by over 1,000,000 students worldwide.

Maven Analytics has been recognized as one of the top 10 education companies that are transforming the industry. It is a purpose-built, all-in-one platform that helps data professionals launch or advance their careers.

Acquire valuable skills, develop professional portfolios to exhibit your work, and network with renowned analysts globally.

The mission of Maven Analytics is to provide individuals with valuable data skills that can positively impact their lives.

Joshua MacCarty

Josh possesses over a decade of experience in implementing machine learning and data science to complex business issues such as marketing mix and pricing optimization, forecasting, clustering, natural language processing, and predictive modeling. He has a strong interest in simplifying complex machine-learning concepts and presenting them in a business context. He holds the belief that machine learning should be made accessible to all.


This course is designed with the beginner in mind. It requires no prior knowledge or skills in mathematics or statistics and covers all the necessary topics to become proficient with data analysis. Throughout the course, we will be using Microsoft Excel (Office 365) for some of our demos to help illustrate points and give a real-world example of data analysis techniques, but participation is completely optional.


This course from Maven Analytics is ideal for those looking to gain a fundamental understanding of Machine Learning and Data Science. By combining four of their best-selling courses, this masterclass covers a variety of topics such as linear and logistic regression, decision trees, KNN, naïve bayes, hierarchical clustering, sentiment analysis – all without you having to write any code! Throughout each step-by-step demonstration and interactive exercise using Excel models; you will gain confidence in the world of machine learning.

This course is a combination of four popular courses from Maven Analytics, presented as a single masterclass.

PART 1: Univariate & Multivariate Profiling

In the first part, the machine learning workflow and standard methods for cleaning and preparing raw data for analysis will be presented. The course will cover univariate analysis using frequency tables, histograms, kernel densities, and profiling metrics. It will also cover multivariate profiling tools such as heat maps, violin and box plots, scatter plots, and correlation.

PART 2: Classification Modeling

Part 2 will cover the supervised learning landscape, the classification workflow, and important topics such as dependent vs. independent variables. The concepts of independent variables, feature engineering, data splitting, and overfitting are important in data analysis. The common classification models that will be reviewed include K-Nearest Neighbors (KNN), Naïve Bayes, Decision Trees, Random Forests, Logistic Regression, and Sentiment Analysis. Tips will also be shared for model scoring, selection, and optimization.

PART 3: Regression & Forecasting

Part 3 of the course will cover fundamental concepts such as linear relationships, least squared error, and their application to regression models including univariate, multivariate, and non-linear. We will analyze diagnostic metrics such as R-squared, mean error, F-significance, and P-Values, and apply time-series forecasting methods to identify seasonality, forecast nonlinear trends, and measure the influence of significant business decisions by utilizing intervention analysis.

PART 4: Unsupervised Learning

Part 4 examines the distinctions between supervised and unsupervised machine learning, and introduces some common unsupervised techniques such as cluster analysis, association mining, outlier detection, and dimensionality reduction. We will explain each model in a straightforward manner and assist you in comprehending how they operate, ranging from K-means and apriori to outlier detection, principal component analysis, and other techniques. Additionally, you will learn about how K-means can be utilized to recognize customer segments.

Rather than struggling to memorize difficult math or learning various coding languages; with this course, you’ll be able to understand just how exactly why these methods work. With the help of user-friendly tutorials that illustrate each concept visually; coupled with ample opportunities to practice your learned skills; you will most certainly come away from this course feeling comfortable and well-versed in the fundamentals of Machine Learning and Data Science.

This course is intended for:

This course is designed for a range of individuals with a background in data science and analytics. For those just getting started, this course provides a comprehensive overview to understand the foundations of machine learning through interactive and beginner-friendly demos. For existing data analysts or BI experts looking to transition into data science, these lessons provide an opportunity to build a fundamental understanding of machine learning algorithms. Even experienced R or Python users who want to dive deeper into the models and algorithms behind their code can learn a great deal from this course. Finally, it’s beneficial to Excel users who are looking to expand their toolkit by applying powerful tools for predictive analytics.


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