Artificial Intelligence Professional Certificate – CAIPC™

150€

  • Registered Students:

    42

  • Duration:

    --

  • Sections:

    12

  • Difficulty Level:

    Intermediate

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

  • Machine Learning Fundamentals

    Machine Learning Fundamentals

  • 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

  • I.6 Cross Validation

    Concept
    Holdout Validation
    K-Fold Cross Validation

  • I.7 Guided Project: Predicting Car Prices

    Guided Project: Predicting Car Prices

  • 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 Prices

    Linear 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

How will the courses be conducted?

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.

How do I pay the tuition fees of the Seminar?

The Seminar Tuition fee is € 150 and you can pay through PayPal, Credit/Debit card or Bank deposit.

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