Artificial Intelligence and Machine Learning Fundamentals Practice Exam
Artificial Intelligence and Machine Learning Fundamentals
Artificial Intelligence and Machine Learning Fundamentals show you machine learning and neural networks from the beginning using genuine examples. Machine learning and neural networks are pillars on which you can construct intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begin by introducing you to Python and discussing AI search algorithms.
• Applied Mathematics.
• Software engineering Fundamentals and Programming.
• Information Modeling and Evaluation.
• Neural Networks.
• Regular Language Processing.
• Relational abilities.
• Programming languages.
• Information engineering.
• Exploratory information analysis.
• Machine Learning Engineer
• ML Ops Engineer
• Artificial Intelligence Engineer
• Senior Machine Learning Engineer
• Software Engineer
Table of Content
Machine Learning Foundations
• What is Machine Learning
• Types of Machine Learning
• How does a Machine Learning Algorithm Works
• Parametric and Non-Parametric Algorithms
• Regression and Classification
• Preparing Your Data
• Problem of Under fitting and Over fitting
• The Bias-Variance trade-off
• Intro to jupyter notebooks
• Data Science packages
• What is Regression Analysis
• Linear Regression
• Cost Function
• Gradient Descent
• Polynomial Regression
• Logistic Regression
• Cost Function for Logistic Regression
• Evaluating a Machine Learning Model
• Introduction to Conditional Probability
• Bayes Rule
• Bayesian Learning
• Naïve Bayes Algorithm
• Test your understanding of Bayes Theorem
• Solution – Test Your Understanding of Bayes Theorem
• Bayes Net
• Markov Chains
Tree Based Learning
• Decision Trees
• Gini Index
• ID3 Algorithm – Entropy
• ID3 Algorithm – Information Gain
• Practice Example – Information Gain
Project – 1
House Price Predictions
Project – 2
SMS Spam Classifier
• What is Ensemble Learning
• Random Forest Algorithm
Support Vector Machines
• Introduction to Support Vector Machines
• Support Vectors
• Hyperparameters in SVMs
Instance Based Learning & Feature Engineering
• What is Instance Based Learning
• K-Nearest Neighbours Algorithm
• Dimensionality Reduction
• Principle Component Analysis
• Feature Scaling
• K-Means Algorithm
Project – 3
• Introduction to Deep Learning
• Perceptron Exercise
• Solution – Perceptron Exercise
• Deep Neural Networks
• Deep Neural Networks – 2
• Activation Functions - 1
• Activation Functions - 2
• Backpropagation Algorithm
• Convolutional Neural Nets
Project – 4
What do we offer?
- Full-Length Mock Test with unique questions in each test set
- Practice objective questions with section-wise scores
- In-depth and exhaustive explanation for every question
- Reliable exam reports to evaluate strengths and weaknesses
- Latest Questions with an updated version
- Tips & Tricks to crack the test
- Unlimited access
What are our Practice Exams?
- Practice exams have been designed by professionals and domain experts that simulate real time exam scenario.
- Practice exam questions have been created on the basis of content outlined in the official documentation.
- Each set in the practice exam contains unique questions built with the intent to provide real-time experience to the candidates as well as gain more confidence during exam preparation.
- Practice exams help to self-evaluate against the exam content and work towards building strength to clear the exam.
- You can also create your own practice exam based on your choice and preference