Innosoft Certified AI Professional (AI-200) Exam

Dates Timing Delivery Method
26 September 2020 03:00PM - 05:00PM In-Person, Dubai Knowledge Park
24 October 2020 03:00PM - 05:00PM Online, Time zone in Dubai (GMT+4)

Course Description

The Innosoft Certified AI Professional certification exam is performance-based, meaning that candidates must perform tasks on a live system and datasets, rather than answering multiple choice questions.  During this exam you will be provide a real-world dataset and you will be asked to perform a set of tasks pertaining to data analysis, visualization and machine learning.

You will be working with Python as the programming language and applying core Python libraries for data science including Numpy, Pandas, Matplotlib, Seaborn, and Scikit-learn.

With respect to the machine learning section of the exam, you need to be able to select and apply the appropriate machine learning algorithm based on the type of prediction required.

Candidates will be emailed exam results within three business days following the exam.

AI-200 exam candidates should master the following the topics. Moreover, they should also be able to complete data analysis, data visualization and machine learning tasks on real-world datasets without assistance.

To be well prepared for this exam you need to demonstrate good knowledge of the following:

Part I – Data Analysis

Unit 1 – Data Analysis with NumPy

  • Numpy Arrays
  • Numpy Array Indexing
  • Numpy Operations

Unit 2 – Data Analysis with Pandas

  • Series
  • DataFrames
  • Handling Missing Data
  • Groupby
  • Merging Joining and Concatenating
  • Operations
  • Data Input and Output

Part II – Data Visualization

Unit 3 – Python for Data Visualization – Matplotlib

  • Basic Matplotlib Plots

Unit 4 – Python for Data Visualization – Seaborn

  • Distribution Plots
  • Categorical Plots
  • Matrix Plots
  • Regression Plots
  • Grids
  • Style and Color

Part III – Machine Learning

Unit 5 – Linear Regression

  • Apply Linear regression in predicting a real-valued output

Unit 6 – Logistic Regression

  • Apply Logistic Regression in solving Classification problems

Unit 7 – K-Nearest Neighbors

  • Apply K-Nearest Neighbors in solving Classification problems

Unit 8 – Decision Trees and Random Forests

  • Apply Decision Trees and Random Forests in solving Classification problems

Unit 9 – Support Vector Machines

  • Apply Support Vector Machines (SVM) in solving Classification problems

Unit 10 – K-Means Clustering

  • Apply K-means Clustering in Unsupervised Learning
  1. Experienced Python, Data Science and Machine Learning professionals seeking validation of their skills
  2. Students who have attended our Python Programming, Python for Data Science and Machine Learning courses, and are on the path to earn the AI-200 Certification
  3. Experienced Data Science Professionals who require a certification by their organization
  4. AI professionals who are on the path to earn the AI-300 Certification

Candidates for this exam should have taken the following two courses, or have equivalent knowledge and experience:

Python Programming

Python for Data Science and Machine Learning