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آموزش Udemy Machine Learning AZ: HandsOn Python & R In Data Science

آموزش یادگیری ماشینی بوسیله زبانهای R و Python - با زیرنویس انگلیسی

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ویدئو معرفی این محصول

در این مجموعه آموزش پس از آموختن مبانی یادگیری ماشینی بر پیاده سازی الگوریتم های یادگیری ماشینی به زبان R مسلط خواهید شد. مدرس مباحث را به زبانی ساده و در حین انجام پروژه های واقعی به شما عزیزان یاد میدهد.

عنوان اصلی : Machine Learning AZ: HandsOn Python & R In Data Science

این مجموعه آموزش ویدیویی محصول موسسه آموزشی Udemy است که بر روی 2 حلقه DVD به همراه فایلهای تمرینی و به مدت زمان 32 ساعت و 20 دقیقه در اختیار علاقه مندان قرار می گیرد.

در ادامه با برخی از سرفصل های درسی این مجموعه آموزش آشنا می شویم :

Welcome to the course! :
Applications of Machine Learning
Why Machine Learning is the Future
Installing R and R Studio (MAC & Windows)
Installing Python and Anaconda (MAC & Windows)
BONUS: Meet your instructors


Part 1: Data Preprocessing :
Welcome to Part 1 Data Preprocessing
Get the dataset
Importing the Libraries
Importing the Dataset
For Python learners, summary of Objectoriented programming: classes & objects
Missing Data
Categorical Data
Splitting the Dataset into the Training set and Test set
Feature Scaling
And here is our Data Preprocessing Template!
Data Preprocessing
5 questions


Part 2: Regression
Welcome to Part 2 Regression
Simple Linear Regression
How to get the dataset
Dataset + Business Problem Description
Simple Linear Regression Intuition Step 1
Simple Linear Regression Intuition Step 2
Simple Linear Regression in Python Step 1
Simple Linear Regression in Python Step 2
Simple Linear Regression in Python Step 3
Simple Linear Regression in Python Step 4
Simple Linear Regression in R Step 1
Simple Linear Regression in R Step 2
Simple Linear Regression in R Step 3
Simple Linear Regression in R Step 4
Simple Linear Regression
5 questions
Multiple Linear Regression
How to get the dataset
Dataset + Business Problem Description
Multiple Linear Regression Intuition Step 1
Multiple Linear Regression Intuition Step 2
Multiple Linear Regression Intuition Step 3
Multiple Linear Regression Intuition Step 4
Multiple Linear Regression Intuition Step 5
Multiple Linear Regression in Python Step 1
Multiple Linear Regression in Python Step 2
Multiple Linear Regression in Python Step 3
Multiple Linear Regression in Python Backward Elimination Preparation
Multiple Linear Regression in Python Backward Elimination HOMEWORK !
Multiple Linear Regression in Python Backward Elimination Homework Solution
Multiple Linear Regression in R Step 1
Multiple Linear Regression in R Step 2
Multiple Linear Regression in R Step 3
Multiple Linear Regression in R Backward Elimination HOMEWORK !
Multiple Linear Regression in R Backward Elimination Homework Solution
Multiple Linear Regression
5 questions
Polynomial Regression
Polynomial Regression Intuition
How to get the dataset
Polynomial Regression in Python Step 1
Polynomial Regression in Python Step 2
Polynomial Regression in Python Step 3
Polynomial Regression in Python Step 4
Python Regression Template
Polynomial Regression in R Step 1
Polynomial Regression in R Step 2
Polynomial Regression in R Step 3
Polynomial Regression in R Step 4
R Regression Template
Support Vector Regression (SVR)
How to get the dataset
SVR in Python
SVR in R
Decision Tree Regression
Decision Tree Regression Intuition
How to get the dataset
Decision Tree Regression in Python
Decision Tree Regression in R
Random Forest Regression
Random Forest Regression Intuition
How to get the dataset
Random Forest Regression in Python
Random Forest Regression in R
Evaluating Regression Models Performance
RSquared Intuition
Adjusted RSquared Intuition
Evaluating Regression Models Performance Homework's Final Part
Interpreting Linear Regression Coefficients
Conclusion of Part 2 Regression


Part 3: Classification
Welcome to Part 3 Classification
Logistic Regression
Logistic Regression Intuition
How to get the dataset
Logistic Regression in Python Step 1
Logistic Regression in Python Step 2
Logistic Regression in Python Step 3
Logistic Regression in Python Step 4
Logistic Regression in Python Step 5
Python Classification Template
Logistic Regression in R Step 1
Logistic Regression in R Step 2
Logistic Regression in R Step 3
Logistic Regression in R Step 4
Logistic Regression in R Step 5
R Classification Template
Logistic Regression
5 questions
KNearest Neighbors (KNN)
KNearest Neighbor Intuition
How to get the dataset
KNN in Python
KNN in R
KNearest Neighbor
5 questions
Support Vector Machine (SVM)
SVM Intuition
How to get the dataset
SVM in Python
SVM in R
Kernel SVM
Kernel SVM Intuition
Mapping to a higher dimension
The Kernel Trick
Types of Kernel Functions
How to get the dataset
Kernel SVM in Python
Kernel SVM in R
Naive Bayes
Bayes Theorem
Naive Bayes Intuition
Naive Bayes Intuition (Challenge Reveal)
Naive Bayes Intuition (Extras)
How to get the dataset
Naive Bayes in Python
Naive Bayes in R
Decision Tree Classification
Decision Tree Classification Intuition
How to get the dataset
Decision Tree Classification in Python
Decision Tree Classification in R
Random Forest Classification
Random Forest Classification Intuition
How to get the dataset
Random Forest Classification in Python
Random Forest Classification in R
Evaluating Classification Models Performance
False Positives & False Negatives
Confusion Matrix
Accuracy Paradox
CAP Curve
CAP Curve Analysis
Conclusion of Part 3 Classification


Part 4: Clustering
Welcome to Part 4 Clustering
KMeans Clustering
KMeans Clustering Intuition
KMeans Random Initialization Trap
KMeans Selecting The Number Of Clusters
How to get the dataset
KMeans Clustering in Python
KMeans Clustering in R
KMeans Clustering
5 questions
Hierarchical Clustering
Hierarchical Clustering Intuition
Hierarchical Clustering How Dendrograms Work
Hierarchical Clustering Using Dendrograms
How to get the dataset
HC in Python Step 1
HC in Python Step 2
HC in Python Step 3
HC in Python Step 4
HC in Python Step 5
HC in R Step 1
HC in R Step 2
HC in R Step 3
HC in R Step 4
HC in R Step 5
Hierarchical Clustering
5 questions
Conclusion of Part 4 Clustering


Part 5: Association Rule Learning
Welcome to Part 5 Association Rule Learning
Apriori
Apriori Intuition
How to get the dataset
Apriori in R Step 1
Apriori in R Step 2
Apriori in R Step 3
Apriori in Python Step 1
Apriori in Python Step 2
Apriori in Python Step 3
Eclat
Eclat Intuition
How to get the dataset
Eclat in R


Part 6: Reinforcement Learning
Welcome to Part 6 Reinforcement Learning
Upper Confidence Bound (UCB)
The MultiArmed Bandit Problem
Upper Confidence Bound (UCB) Intuition
How to get the dataset
Upper Confidence Bound in Python Step 1
Upper Confidence Bound in Python Step 2
Upper Confidence Bound in Python Step 3
Upper Confidence Bound in Python Step 4
Upper Confidence Bound in R Step 1
Upper Confidence Bound in R Step 2
Upper Confidence Bound in R Step 3
Upper Confidence Bound in R Step 4
Thompson Sampling
Thompson Sampling Intuition
Algorithm Comparison: UCB vs Thompson Sampling
How to get the dataset
Thompson Sampling in Python Step 1
Thompson Sampling in Python Step 2
Thompson Sampling in R Step 1
Thompson Sampling in R Step 2


Part 7: Natural Language Processing
Welcome to Part 7 Natural Language Processing
How to get the dataset
Natural Language Processing in Python Step 1
Natural Language Processing in Python Step 2
Natural Language Processing in Python Step 3
Natural Language Processing in Python Step 4
Natural Language Processing in Python Step 5
Natural Language Processing in Python Step 6
Natural Language Processing in Python Step 7
Natural Language Processing in Python Step 8
Natural Language Processing in Python Step 9
Natural Language Processing in Python Step 10
Homework Challenge
Natural Language Processing in R Step 1
Natural Language Processing in R Step 2
Natural Language Processing in R Step 3
Natural Language Processing in R Step 4
Natural Language Processing in R Step 5
Natural Language Processing in R Step 6
Natural Language Processing in R Step 7
Natural Language Processing in R Step 8
Natural Language Processing in R Step 9
Natural Language Processing in R Step 10
Homework Challenge


Part 8: Deep Learning
Welcome to Part 8 Deep Learning
What is Deep Learning?
Artificial Neural Networks
Plan of attack
The Neuron
The Activation Function
How do Neural Networks work?
How do Neural Networks learn?
Gradient Descent
Stochastic Gradient Descent
Backpropagation
How to get the dataset
Business Problem Description
ANN in Python Step 1 Installing Theano, Tensorflow and Keras
ANN in Python Step 2
ANN in Python Step 3
ANN in Python Step 4
ANN in Python Step 5
ANN in Python Step 6
ANN in Python Step 7
ANN in Python Step 8
ANN in Python Step 9
ANN in Python Step 10
ANN in R Step 1
ANN in R Step 2
ANN in R Step 3
ANN in R Step 4 (Last step)
Convolutional Neural Networks
Plan of attack
What are convolutional neural networks?
Step 1 Convolution Operation
Step 1(b) ReLU Layer
Step 2 Pooling
Step 3 Flattening
Step 4 Full Connection
Summary
Softmax & CrossEntropy
How to get the dataset
CNN in Python Step 1
CNN in Python Step 2
CNN in Python Step 3
CNN in Python Step 4
CNN in Python Step 5
CNN in Python Step 6
CNN in Python Step 7
CNN in Python Step 8
CNN in Python Step 9
CNN in Python Step 10
CNN in R


Part 9: Dimensionality Reduction
Welcome to Part 9 Dimensionality Reduction
Principal Component Analysis (PCA)
How to get the dataset
PCA in Python Step 1
PCA in Python Step 2
PCA in Python Step 3
PCA in R Step 1
PCA in R Step 2
PCA in R Step 3
Linear Discriminant Analysis (LDA)
How to get the dataset
LDA in Python
LDA in R
Kernel PCA
How to get the dataset
Kernel PCA in Python
Kernel PCA in R


Part 10: Model Selection & Boosting
Welcome to Part 10 Model Selection & Boosting
Model Selection
How to get the dataset
kFold Cross Validation in Python
kFold Cross Validation in R
Grid Search in Python Step 1
Grid Search in Python Step 2
Grid Search in R
XGBoost
How to get the dataset
XGBoost in Python Step 1
XGBoost in Python Step 2
XGBoost in R
Bonus Lectures

مشخصات این مجموعه :
زبان آموزش ها انگلیسی روان و ساده
دارای آموزشهای ویدیویی و دسته بندی شده
ارائه شده بر روی 2 حلقه DVD به همراه فایلهای تمرینی
مدت زمان آموزش 32 ساعت و 20 دقیقه !
محصول موسسه آموزشی Udemy

تولید کننده:
شناسه: UD3881
حجم: 6371 مگابایت
مدت زمان: 1940 دقیقه
تعداد دیسک: 2 عدد
زبان: انگلیسی ساده و روان
تاریخ انتشار: 9 مرداد 1396
محصولات مشابه این محصول