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What you'll get

  • Job Credibility
  • Certification Valid for Life
  • On-demand video*
  • E-Book
  • Self-Paced Learning
  • Certificate of Completion

Exam details

  • Mode of Exam : Online
  • Duration : 1 Hour
  • Multiple Choice Questions are asked
  • No. of Questions are asked : 50
  • Passing Marks : 25 (50%)
  • There is no negative marking

If you are interested in learning Certified Artificial Intelligence and Machine Learning Basics then you came to the right place. This course is for you. Here you will study these topics in a very comprehensive and concise manner. So let's get started. 

Here you will study about the course, introduction about Artificial Intelligence and machine learning technology which consists of all supervised learning ,their terminology, evaluating performances. You will also understand the fundamentals of machine learning, how they work, their representations from data. You will also know which covers the history of machine learning, deep learning, Bias Variance Trade and all the information about gathering data. You will also be taught RNN and LTSM. You will also learn many things. So go throughout the course. 

In the end of this course, you will see yourself learning everything from Artificial Intelligence in the beginning till learning Machine Learning  and a tint of python too. 

You will require nothing as here you are going to study from the very beginning. You just need to bring up business and managerial experience which you don't have to worry about, this course will help you with the rest.

This course is for Students who are interested in learning Machine Learning or anyone needing a complete guide on how to start this topic.Students Interested in Beginning Data Science Applications in Python Environment, or someone wanting to Specialize in Anaconda can also take this course. People who want to learn machine learning, deep learning, python, artificial intelligence with machine learning can 

You will create a great foundation in this topic with an understanding in depth and clear concepts. Your job opportunities will open and be clear without any obstacles you can get a job Or you can also involve yourself in business and eventually your certificate from this course will help you in your interviews. You are provided with lifetime access to this course. So don't worry about anything and grab this course now.

Course Content

Total: 67 lectures
  • History of Artificial Intelligence
  • What Is Artificial Intelligence?
  • Emergence of AI
  • Cognitive Science and AI
  • Logical Approach to AI
  • Knowledge-Based Systems
  • Probabilistic Approach to AI
  • Evolutionary Computation and Swarm Intelligence
  • Neural Networks & Deep Learning
  • A Return to Creating HAL
  • Propositional Logic
  • Resolution
  • Artificial Intelligence Applications
  • Artificial Intelligence Applications
  • Taxonomic Knowledge
  • Frames
  • Nonmonotonic Logic
  • Supervised Learning
  • Regression
  • Parameter Estimation
  • Learning a Decision Tree
  • Random Variables
  • Meaning of Probability
  • Random Variables in Applications
  • Probability in the Wumpus World
  • Intuitive Introduction to Bayesian Networks
  • Properties of Bayesian Networks
  • Causal Networks as Bayesian Networks
  • Inference in Bayesian Networks
  • Networks with Continuous Variables
  • Obtaining the Probabilities
  • Large-Scale Application: Promedas
  • Entailed Conditional Independencies
  • Faithfulness
  • Markov Equivalence
  • Markov Blankets and Boundaries
  • Decision Trees
  • Influence Diagrams
  • Modeling Risk Preferences
  • Analyzing Risk Directly
  • Good Decision versus Good Outcome
  • Sensitivity Analysis
  • Value of Information
  • Learning a Single Parameter
  • Learning Parameters in a Bayesian Network
  • Learning Parameters with Missing Data
  • Structure Learning Problem
  • Constraint-Based Structure Learning
  • Application: MENTOR
  • Software Packages for Learning
  • Causal Learning
  • Class Probability Trees
  • Unsupervised Learning
  • Reinforcement Learning
  • Genetics Review
  • Genetic Algorithms
  • Genetic Programming
  • Ant System
  • Flocks
  • The Perceptron
  • Feedforward Neural Networks
  • Activation Functions
  • Application to Image Recognition
  • Parsing
  • Semantic Interpretation
  • Concept/Knowledge Interpretation
  • Information Extraction


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