Certified Artificial Intelligence Developer

Certified Artificial Intelligence Developer

An AI engineer builds AI models using machine learning algorithms and deep learning neural networks to draw business insights, which can be used to make business decisions that affect the entire organization. These engineers also create weak or strong AIs, depending on what goals they want to achieve.

This 7 day certification course is specially crafted by experts in AI and covers questions from basic concepts to the very core. Certified AI Developer training is an expertly curated and excellently designed training, rendering profound knowledge on various aspects of AI.

Artificial Intelligence (AI) are the machines which are programmed and designed in such a way that they act and think like a human. It becomes an important part of our daily life and used in a wide area of day to day services. The introduction of AI brings the idea of the error-free world and slowly introduces all the sectors that reduce human effort and give an accurate and faster result.

Learning Outcome

By the end of the course, the learner shall have in-depth knowledge and understanding of the following:

• Complete understanding of the Machine Learning process
• Complete understanding of the Artificial Intelligence development flow
• Complete understanding of Deep Learning & neural networks
• Python
• Pandas
• Matplotlib
• SpaCy
• NLTK
• Statistical knowledge for data science
• Applied Mathematics and Algorithms pertaining to AI computing
• Natural Language Processing (NLP)
• Probabilistic reasoning
• Data Mining tools usage skills

Certification

• Certification Body: Global Tech Council (Australia)
• Examination Fee: USD 149.00
• Lab Cost (During training): USD 20
• There will be an online examination of multiple choice exam of 100 marks.
• You need to acquire 60+ marks to clear the exam.
• If you fail, you can retake the exam after one day.
• You can take the exam no more than 3 times.

Prerequisites

In order to be able to participate in this course, the learner needs to comply with the following conditions:

• Access to PC [either one of these OS: MS Windows / Mac / Linux (ubuntu)]
• Full admin / root access to the above mentioned PC
• Experience and understanding of filing system
• Internet connection
• Webcam
• Microphone
• Dual screens
• Very basic understanding of server architecture
• Ability to comprehend the English language (as the examination is in English)
• Minimum 99% attendance
• Basic education in mathematics (understanding of linear and non-linear mathematics at high-school level)
• Google account

Course Outline

Duration: 7 days.

Daily duration: 8 hours (inclusive of 2 x 15 minute break and 1 hour lunch break)

Difficulty: INtensive

1. Basics of Python for Data Science
   1. How to Install Python
   2. Python Variables
   3. Loops in Python
   4. Python collection Data Types
   5. OOPS concepts
   6. Exception Handling
   7. Regular Expression
   8. Python Numpy Arrays
   9. Matrix and its operation
   10. Functions in Python
   11. User Defined functions in Python
   12. Scope in Python
   13. Introduction to Methods
   14. Packages in Python and PIP
   15. Pandas and Data frames
   16. Import and Export data from CSV
2. Data Preprocessing
   1. Importing Libraries
   2. Importing Dataset
   3. Taking care of Missing Data
   4. Encoding Data: Categorical Data
   5. Splitting the dataset into the Training set and Test set
   6. Feature Scaling
3. Fundamentals of Machine Learning
   1. What is Machine Learning?
   2. Process of Machine Learning
   3. Life Cycle of Machine Learning
   4. Application working in Machine Learning
   5. Types Of Machine Learning
   6. Setting up the development environment lab
4. Introduction to AI
   1. What is Artificial Intelligence?
   2. Intelligent Agents
   3. Advantages and Disadvantages of AI
   4. Challenges of AI
5. NLP
   1. Parts of Speech and Entity Tagging
   2. Text Classification
   3. POS
   4. NER
   5. Semantics
   6. Sentiment Analysis
6. Problem Solving
   1. What is Searching?
   2. Uninformed Search Algorithm
   3. Informed Search Algorithm
   4. Adversarial Search
   5. Constraint satisfaction problems
7. Knowledge Representation And Planning
   1. Knowledge Representation
   2. Knowledge Representation Techniques
   3. Propositional Logic
   4. First Order Logic
   5. Rule-Based System
8. Probabilistic Reasoning
   1. Basic Probability Concepts
   2. Markov and Hidden Markov Model
   3. Association rules
   4. Dimensionality reduction
   5. Feature selection and Feature Extraction
9. Machine Learning
   1. What is Machine Learning?
   2. Types of Learning
   3. Clustering
   4. Classification
   5. Decision Tree
   6. Regression
   7. Support Vector Machine
   8. What is Reinforcement learning?
10. Communication and Perceiving
    1. Natural Language Processing
    2. Perception
11. Neural Network
    1. What is a Neural Network?
    2. Types of Neural Network
    3. Neural Network Components
12. Data Mining Tools
    1. Rapid Miner
    2. Weka
    3. Orange
    4. R
    5. KNIME
13. Project
    1. Installing Prerequisites
    2. Clustering using K-means in Python
    3. Collaborative Filtering Movie DB rating prediction
14. Assessment