About this Course

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Intermediate Level

ML workflow knowledge is required, as is experience with Python or similar languages. Basic knowledge of math and statistics is also recommended.

Approx. 22 hours to complete
English

What you will learn

  • Train and evaluate decision trees and random forests for regression and classification.

  • Train and evaluate support-vector machines (SVM) for regression and classification.

  • Train and evaluate multi-layer perceptron (ML) artificial neural networks (ANN) for regression and classification.

  • Train and evaluate convolutional neural networks (CNN) and recurrent neural networks (RNN) for computer vision and natural language processing tasks.

Skills you will gain

Deep LearningArtificial Neural NetworkDecision TreeSupport Vector Machine (SVM)Machine Learning (ML) Algorithms
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level

ML workflow knowledge is required, as is experience with Python or similar languages. Basic knowledge of math and statistics is also recommended.

Approx. 22 hours to complete
English

Offered by

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CertNexus

Syllabus - What you will learn from this course

Week
1

Week 1

5 hours to complete

Build Decision Trees and Random Forests

5 hours to complete
16 videos (Total 64 min), 4 readings, 1 quiz
16 videos
CAIP Specialization Introduction3m
Build Decision Trees and Random Forests Module Introduction1m
Decision Tree3m
Classification and Regression Tree (CART)3m
Gini Index Example7m
CART Hyperparameters7m
Pruning4m
C4.54m
Bin Determination3m
One-Hot Encoding3m
Decision Trees Compared to Other Algorithms2m
Ensemble Learning2m
Random Forest6m
Random Forest Hyperparameters2m
Feature Selection Benefits3m
4 readings
Overview2m
Decision Tree Algorithm Comparison3m
Guidelines for Building a Decision Tree Model5m
Guidelines for Building a Random Forest Model5m
1 practice exercise
Building Decision Trees and Random Forests30m
Week
2

Week 2

3 hours to complete

Build Support-Vector Machines (SVM)

3 hours to complete
8 videos (Total 35 min), 3 readings, 1 quiz
8 videos
Support-Vector Machines (SVMs)1m
SVMs for Linear Classification2m
Hard-Margin and Soft-Margin Classification4m
SVMs for Non-Linear Classification1m
Kernel Trick13m
Kernel Methods7m
SVMs for Regression1m
3 readings
Overview2m
Guidelines for Building SVM Models for Classification5m
Guidelines for Building SVM Models for Regression5m
1 practice exercise
Building SVMs30m
Week
3

Week 3

3 hours to complete

Build Multi-Layer Perceptrons (MLP)

3 hours to complete
8 videos (Total 29 min), 2 readings, 1 quiz
8 videos
Artificial Neural Network (ANN)1m
Perceptron5m
Perceptron Training7m
Multi-Layer Perceptron (MLP)3m
ANN Layers2m
Backpropagation2m
Activation Functions4m
2 readings
Overview2m
Guidelines for Building MLPs5m
1 practice exercise
Building MLPs30m
Week
4

Week 4

6 hours to complete

Build Convolutional and Recurrent Neural Networks (CNN/RNN)

6 hours to complete
11 videos (Total 66 min), 3 readings, 1 quiz
11 videos
Convolutional Neural Network (CNN)3m
CNN Filters7m
Padding and Stride3m
CNN Architecture10m
Generative Adversarial Network (GAN)5m
Recurrent Neural Network (RNN)6m
Memory Cell2m
RNN Training4m
Long Short-Term Memory (LSTM) Cell13m
Embedding8m
3 readings
Overview2m
Guidelines for Building CNNs10m
Guidelines for Building RNNs5m
1 practice exercise
Building CNNs and RNNs30m

About the CertNexus Certified Artificial Intelligence Practitioner Professional Certificate

CertNexus Certified Artificial Intelligence Practitioner

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