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Registered Students:
42
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Duration:
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Sections:
12
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Difficulty Level:
Intermediate
- DESCRIPTION
- CURRICULUM
- FAQ
- ANNOUNCEMENT
Audience Profile
– Anyone interested in expanding their knowledge in Artificial Intelligence and Machine Learning
– Engineers, Analysts, Marketing Managers
– Data Analysts, Data Scientists, Data Stewards
– Anyone interested in Data Mining and Machine Learning techniques
In 1959, Arthur Samuel, a computer scientist who pioneered the study of Artificial Intelligence, described machine learning as “the study that gives computers the ability to learn without being explicitly programmed.” Alan Turing’s seminal paper (Turing, 1950) introduced a benchmark standard for demonstrating machine intelligence. A machine has to be intelligent and responsive in a manner that cannot be differentiated from that of a human being.
Machine Learning is an application of Artificial Intelligence where a computer/machine learns from past experiences (input data) and makes future predictions. The performance of such a system should be at least at human level.
This certification focuses on clustering problems for unsupervised machine learning with K-Means algorithm. For Supervised machine learning, we will describe the classification problem with a demonstration of the design trees algorithm and the regression one with an example of linear regression.
Learning Objectives
– Understand the fundamentals of Artificial Intelligence and Machine Learning
– Describe the methods of Machine Learning: supervised and unsupervised
– Use the data analysis for Decision-Making
– Understand the limits of algorithms
– Understand and grasp Python programming, essential mathematics knowledge in AI, and basic programming methods
Exam Details
– Format: Multiple choice question
– Questions: 40
– Pass Score: 32/40 or 80 %
– Language: Spanish/English/Portuguese
– Duration: 60 minutes
– Open book: No
– Delivery: This examination is available online
– Supervised: It will be at the Partner’s discretion
Certification Details
– Certification Type: Professional.
– Certification Code: CAIPC™
Prerequisites
There are no formal prerequisites for this certification
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Machine Learning Fundamentals
Machine Learning Fundamentals
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I.1 Key Points
Supervised Machine Learning
Unsupervised Machine Learning
Reinforcement Machine Learning -
I.2 Introduction to K-Nearest Neighbors
Introduction
Introduction to the Data
K-nearest Neighbors
Euclidean Distance
Calculate Distance for All Observations
Randomizing and Sorting
Average Price
Functions for Prediction -
I.3 Evaluating Model Performance
Testing Quality of Predictions
Error Metrics
Mean Squared Error
Training Another Model
Root Mean Squared Error
Comparing MAE and RMSE -
I.4 Multivariate K-Nearest Neighbors
Recap
Removing Features
Handling Missing Values
Normalize Columns
Euclidean Distance for Multivariate Case
Introduction to Scikit-learn
Fitting a Model and Making Predictions
Calculating MSE using Scikit-Learn
Using More Features
Using All Features -
I.5 Hyperparameter Optimization
Recap
Hyperparameter Optimization
Expanding Grid Search
Visualizing Hyperparameter Values
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I.6 Cross Validation
Concept
Holdout Validation
K-Fold Cross Validation -
I.7 Guided Project: Predicting Car Prices
Guided Project: Predicting Car Prices
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II Calculus For Machine Learning
Calculus For Machine Learning
Understanding Linear and Nonlinear Functions
Understanding Limits
Finding Extreme Points -
III Linear Algebra For Machine Learning
Linear Algebra For Machine Learning
Linear Systems
Vectors
Matrix Algebra
Solution Sets -
IV Linear Regression For Machine Learning
Linear Regression For Machine Learning
The Linear Regression Model
Feature Selection
Gradient Descent
Ordinary Least Squares
Processing And Transforming Features
Guided Project: Predicting House Sale PricesLinear Algebra For Machine Learning
Linear Systems
Vectors
Matrix Algebra
Solution Sets -
V Machine Learning in Python
Logistic Regression
Introduction to Evaluating Binary Classifiers
Multiclass Classification
Overfitting
Clustering Basics
K-means Clustering
Guided Project: Predicting the Stock Market -
VI Decision Tree
Decision Tree
Why use Decision Trees?
Decision Tree Terminologies
How Does the Decision Tree Algorithm Work
Pruning: Getting an Optimal Decision Tree
Advantages of the Decision Tree
Disadvantages of the Decision Tree
Python Implementation of Decision Tree
Guided Project: Predicting Bike Rentals
References and Bibliography
The course is self paced. This means that you can learn at your own time and schedule, while completing the program you receive both the attendance certificate and certification through online exams.
The Seminar Tuition fee is € 150 and you can pay through PayPal, Credit/Debit card or Bank deposit.
Who is CertiProf®?
CertiProf® is an Examination Institute founded in 2015, in the USA. Located in Sunrise, Florida.
Our philosophy is based on community knowledge, and for that purpose its collaborative network is
made up of:
• CKA’s (CertiProf Knowledge Ambassadors), are influential people in their fields of expertise or
mastery, coaches, trainers, consultants, bloggers, community builders, organizers and evangelists,
who are willing to contribute in the improvement of content
• CLL’s (CertiProf Lifelong Learners), Certification candidates are identified as Continuing Learner
proven their unwavering commitment to lifelong learning, which is vitally important in today’s
ever-changing and expanding digitalized world. Regardless of whether they win or fail the exam
• ATP’s (Accredited Trainer Partners), Universities, training centers and facilitators around the
world that make up the partner network
• Authors (co-creators), Industry experts or practitioners who, with their knowledge, develop
content for the creation of new certifications that respond to the needs of the industry
• Internal Staff, our distributed team with operations in India, Brazil, Colombia and the United
States that support day by day the execution of the purpose of CertiProf®