Apresentar e enfatizar as possiblidades de trabalho pedagógico do professor para desenvolver a linguagem oral nas crianças.
Carga Horária: 40 hs
Livre
Curso na Modalidade 100% à Distância – EAD
- Concepção
- Desenvolvimento da Linguagem das crianças
- O desenvolvimento da oralidade na perspectiva Vigotskiana vs. Piagetiana
- Possibilidades de trabalho com a oralidade na Educação Infantil
Avaliação realizada através de questionário avaliativo no Ambiente Virtual de Aprendizagem
Welcome to the course!
1
Applications of Machine Learning
2
Why Machine Learning is the Future
3
Important notes, tips & tricks for this course
4
This PDF resource will help you a lot
5
Updates on Udemy Reviews
6
Installing Python and Anaconda (Mac, Linux & Windows)
7
Update: Recommended Anaconda Version
8
Installing R and R Studio (Mac, Linux & Windows)
9
BONUS: Meet your instructors
-------------------- Part 1: Data Preprocessing --------------------
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Welcome to Part 1 - Data Preprocessing
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Get the dataset
3
Importing the Libraries
4
Importing the Dataset
5
For Python learners, summary of Object-oriented programming: classes & objects
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Missing Data
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Categorical Data
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WARNING - Update
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Splitting the Dataset into the Training set and Test set
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Feature Scaling
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And here is our Data Preprocessing Template!
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Data Preprocessing
-------------------- Part 2: Regression --------------------
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Welcome to Part 2 - Regression
Simple Linear Regression
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How to get the dataset
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Dataset + Business Problem Description
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Simple Linear Regression Intuition - Step 1
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Simple Linear Regression Intuition - Step 2
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Simple Linear Regression in Python - Step 1
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Simple Linear Regression in Python - Step 2
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Simple Linear Regression in Python - Step 3
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Simple Linear Regression in Python - Step 4
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Simple Linear Regression in R - Step 1
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Simple Linear Regression in R - Step 2
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Simple Linear Regression in R - Step 3
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Simple Linear Regression in R - Step 4
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Simple Linear Regression
Multiple Linear Regression
1
How to get the dataset
2
Dataset + Business Problem Description
3
Multiple Linear Regression Intuition - Step 1
4
Multiple Linear Regression Intuition - Step 2
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Multiple Linear Regression Intuition - Step 3
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Multiple Linear Regression Intuition - Step 4
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Prerequisites: What is the P-Value?
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Multiple Linear Regression Intuition - Step 5
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Multiple Linear Regression in Python - Step 1
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Multiple Linear Regression in Python - Step 2
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Multiple Linear Regression in Python - Step 3
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Multiple Linear Regression in Python - Backward Elimination - Preparation
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Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !
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Multiple Linear Regression in Python - Backward Elimination - Homework Solution
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Multiple Linear Regression in Python - Automatic Backward Elimination
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Multiple Linear Regression in R - Step 1
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Multiple Linear Regression in R - Step 2
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Multiple Linear Regression in R - Step 3
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Multiple Linear Regression in R - Backward Elimination - HOMEWORK !
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Multiple Linear Regression in R - Backward Elimination - Homework Solution
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Multiple Linear Regression in R - Automatic Backward Elimination
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Multiple Linear Regression
Polynomial Regression
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Polynomial Regression Intuition
2
How to get the dataset
3
Polynomial Regression in Python - Step 1
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Polynomial Regression in Python - Step 2
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Polynomial Regression in Python - Step 3
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Polynomial Regression in Python - Step 4
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Python Regression Template
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Polynomial Regression in R - Step 1
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Polynomial Regression in R - Step 2
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Polynomial Regression in R - Step 3
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Polynomial Regression in R - Step 4
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R Regression Template
Support Vector Regression (SVR)
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How to get the dataset
2
SVR Intuition
3
SVR in Python
4
SVR in R
Decision Tree Regression
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Decision Tree Regression Intuition
2
How to get the dataset
3
Decision Tree Regression in Python
4
Decision Tree Regression in R
Random Forest Regression
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Random Forest Regression Intuition
2
How to get the dataset
3
Random Forest Regression in Python
4
Random Forest Regression in R
Evaluating Regression Models Performance
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R-Squared Intuition
2
Adjusted R-Squared Intuition
3
Evaluating Regression Models Performance - Homework's Final Part
4
Interpreting Linear Regression Coefficients
5
Conclusion of Part 2 - Regression
-------------------- Part 3: Classification --------------------
1
Welcome to Part 3 - Classification
Logistic Regression
1
Logistic Regression Intuition
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