Maskininlärning med Python
Den här kursen ger en fördjupad förståelse för maskininlärning med Python. I den här kursen lär du dig om artificiella datamängder med Scikit-learn, neurala nätverk, att separera klasser med delningslinjer, regressionsträd i Python, slumpmässiga skogar i Python, att förstärka algoritmer i Python och mycket mer. Utbildningen passar både nybörjare och de med avancerad nivå av Python- och maskininlärningskompetens.
Intro to Machine Learning with Python
Machine Learning with Python
Machine Learning Terminology
Evaluation Metrics
Data Representation and Visualization of Data
Available Data Sets in Sklearn
Artificial Datasets with Scikit-Learn
Train and Test Sets by Splitting Learn and Test Data
k-Nearest Neighbor Classifier in Python
k-Nearest-Neighbor Classifier with sklearn
Neural Networks Introduction
Separating Classes with Dividing Lines
A Simple Neural Network from Scratch in Python
Pereceptron class in sklearn
Neural Networks, Structure, Weights and Matrices
Running a Neural Network with Python
Backpropagation in Neural Networks
Training a Neural Network with Python
Softmax as Activation Function
Confusion Matrix in Machine Learning
Training and Testing with MNIST
Dropout Neural Networks in Python
Neural Networks with Scikit
A Neural Network for the Digits Dataset
Naive Bayes Classification with Python
Naive Bayes Classifier with Scikit
Introduction to Text Classification
Text Classification in Python
Natural Language Processing with Python
Natural Language Processing: Classification
Introduction to Regression with Python
Decision Trees in Python
Regression Trees in Python
Random Forests in Python
Boosting Algorithm in Python
Principal Component Analysis (PCA) in Python
Linear Discriminant Analysis in Python
Expectation Maximization and Gaussian Mixture Models (GMM)
Introduction to TensorFlow