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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.

Skills Required

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. 





Career Opportunity

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

Regression Analysis

What is Regression Analysis

Linear Regression

Cost Function

Gradient Descent

Polynomial Regression

Logistic Regression

Cost Function for Logistic Regression


Evaluating a Machine Learning Model

Bayesian Statistics

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

Ensemble Learning

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

Deep Learning

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

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