Machine Learning for BI, PART 1: Data Profiling Free Download Paid Course From google drive. You will learn to Demystify the world of machine learning & build foundational data science skills, without writing a line of code in this complete course.
- Build foundational machine learning, without composing complex code
- Use intuitive, easy to understand apparatuses like Microsoft Excel to present and demystify AI instruments and procedures
- Prepare raw data for investigation utilizing QA devices like variable sorts, range estimations, and table structures
- Analyze datasets using normal univariate and multivariate profiling measurements
- Portray and picture appropriations with histograms, bit densities, heat guides, and violin plots
- Describe & visualize associations with scatterplots and relationship
Machine Learning for BI, PART 1: Data Profiling Course Requirement
This is a beginner-friendly course (no earlier information or math/details foundation required)
We’ll utilize Microsoft Excel (Office 365) for some course demos, however interest is discretionary
Machine Learning for BI, PART 1: Data Profiling Course Description
In case you’re eager to investigate information science and AI yet on edge about learning complex programming dialects or threatened by terms like “guileless Bayes”, “calculated relapse”, “KNN” and “choice trees”, you’re in the correct spot.
This course is PART 1 of a 4-PART SERIES intended to help you construct a solid, fundamental comprehension of AI:
- Section 1: QA and Data Profiling
- Section 2: Classification
- Section 3: Regression and Forecasting
- Section 4: Unsupervised Learning
This course makes information science congenial to ordinary individuals, and is intended to demystify amazing AI instruments and methods without attempting to show you a coding language simultaneously.
All things considered, we’ll utilize recognizable, easy to use devices like Microsoft Excel to separate complex themes and assist you with seeing HOW and WHY AI functions before you plunge into programming dialects like Python or R. In contrast to most information science and AI courses, you won’t compose a SINGLE LINE of code.
In this Part 1 course, we’ll present the AI scene and work process, and survey basic QA tips for cleaning and planning crude information for the investigation, including variable sorts, void qualities, range and check figurings, table structures, and the sky is the limit from there.
We’ll cover univariate examination with recurrence tables, histograms, portion densities, and profiling measurements, at that point plunge into multivariate profiling apparatuses like warmth maps, violin and box plots, disperse plots, and connection:
- Area 1: Machine Learning Intro and Landscape
- AI cycle, definition, and scene
- Segment 2: Preliminary Data QA
- Variable sorts, void qualities, range and tally estimations, left/right editing, and so forth
- Segment 3: Univariate Profiling
- Histograms, recurrence tables, mean, middle, mode, fluctuation, skewness, and so forth
- Segment 4: Multivariate Profiling
- Violin and box plots, bit densities, heat maps, connection, and so forth
All through the course we’ll acquaint certifiable situations planned with assistance harden key ideas and tie them back to real business knowledge contextual analyses. You’ll go through profiling measurements to clean item stock information for a nearby staple, investigate Olympic competitor socioeconomics with histograms and bit densities, envision auto collision recurrence with heat guides, and significantly more.
In case you’re prepared to construct the establishment for a fruitful profession in information science, this is the course for you.
Join today and get prompt, lifetime admittance to the accompanying:
- High-caliber, on-request video
- AI: Data Profiling digital book
- Downloadable Excel project record
- Master Q&A gathering
- 30-day unconditional promise
- Glad learning!
- Josh M. (Lead Machine Learning Instructor, Maven Analytics)
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Recommended Course: Machine Learning for BI, PART 1: Data Profiling
Who this course is for:
Anybody hoping to become familiar with the nuts and bolts of AI through genuine demos and instinctive, perfectly clear clarifications
Information Analysts or BI specialists hoping to progress into information science or construct a major comprehension of AI
R or Python clients looking for a more profound comprehension of the models and calculations behind their code.
Note: This is Old Course Link Updated course Link Provided soon