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.
• Models.
• Services.
• Deploying.
• Security.
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
• Clustering
• Preparing Your Data
• Outliers
• 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
• Regularization
• 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
• Bagging
• Random Forest Algorithm
• Boosting
Support Vector Machines
• Introduction to Support Vector Machines
• Support Vectors
• Kernel
• 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
• 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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