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Machine Learning with Python

Welcome to the course!

1.Applications of Machine Learning

2. Why Machine Learning is the Future

3. Installing R and R Studio (MAC & Windows)

4. Update: Recommended Anaconda Version

5. Installing Python and Anaconda (MAC & Windows)

6. BONUS: Meet your instructors

 Part 2: Regression

1. Welcome to Part 2 – Regression

Simple Linear Regression

2. How to get the dataset

3. Dataset + Business Problem Description

4. Simple Linear Regression Intuition

Simple Linear Regression in Python

Multiple Linear Regression

1. How to get the dataset

2. Dataset + Business Problem Description

3. Multiple Linear Regression Intuition

 Multiple Linear Regression in Python

Polynomial Regression

1. Polynomial Regression Intuition

2. How to get the dataset

Polynomial Regression in Python

Support Vector Regression (SVR)

1. How to get the dataset

2. SVR in Python

Decision Tree Regression

1. Decision Tree Regression Intuition

2. How to get the dataset

 Decision Tree Regression in Python

Random Forest Regression

1. Random Forest Regression Intuition

2. How to get the dataset

 Random Forest Regression in Python

Evaluating Regression Models Performance

1. R-Squared Intuition

2. Adjusted R-Squared Intuition

3. Evaluating Regression Models Performance

4. Interpreting Linear Regression Coefficients

Part 4: Clustering

1. Welcome to Part 4 – Clustering

K-Means Clustering

1. K-Means Clustering Intuition

2. K-Means Random Initialization Trap

3. K-Means Selecting The Number Of Clusters

4. How to get the dataset

5. K-Means Clustering in Python

Hierarchical Clustering

1. Hierarchical Clustering Intuition

2. Hierarchical Clustering How Dendrograms Work

3. Hierarchical Clustering Using Dendrograms

4. How to get the dataset

5. HC in Python

 Part 6: Reinforcement Learning

1. Welcome to Part 6 – Reinforcement Learning

Upper Confidence Bound (UCB)

1. The Multi-Armed Bandit Problem

2. Upper Confidence Bound (UCB) Intuition

3. How to get the dataset

4. Upper Confidence Bound in Python

Thompson Sampling

1. Thompson Sampling Intuition

2. Algorithm Comparison: UCB vs Thompson Sampling

3. How to get the dataset

4. Thompson Sampling in Python

 Part 9: Dimensionality Reduction

Principal Component Analysis (PCA)

1. How to get the datase

2. PCA in Python

Linear Discriminant Analysis (LDA)

1. How to get the datase

2. LDA in Python

Kernel PCA

1. How to get the dataset

2. Kernel PCA in Python

Model Selection

1. How to get the dataset

2. k-Fold Cross Validation Python

3. k-Fold Cross Validation

4. Grid Search in Python

5. Grid Search in Python

XGBoost

1. How to get the dataset

2. XGBoost in Python

3. XGBoost in Python

 Part 1: Data Preprocessing

1. Welcome to Part 1 – Data Preprocessing

2. Get the dataset

3. Importing the Libraries

4. Importing the Dataset

5. For Python learners, summary of Object-oriented programming: classes & objects

6. Missing Data

7. Categorical Data

8. Splitting the Dataset into the Training set and Test set

9. Feature Scaling

10. And here is our Data Preprocessing Template

 Part 3: Classification

Logistic Regression

1. Logistic Regression Intuition

2. How to get the dataset

3. Logistic Regression in Python

K-Nearest Neighbors (K-NN)

1. K-Nearest Neighbor Intuition

2. How to get the dataset

3. K-NN in Python

Support Vector Machine (SVM)

1. SVM Intuition

2. How to get the dataset

3. SVM in Python

Kernel SVM

1. Kernel SVM Intuition

2. Mapping to a higher dimension

3. The Kernel Trick

4. Types of Kernel Functions

5. How to get the dataset

6. Kernel SVM in Python

Naive Bayes

1. Bayes Theorem

2. Naive Bayes Intuition14:03

3. Naive Bayes Intuition (Challenge Reveal)

4. Naive Bayes Intuition (Extras)

5. How to get the dataset

6. Naive Bayes in Python

Decision Tree Classification

1. Decision Tree Classification Intuition

2. How to get the dataset

3. Decision Tree Classification in Python

Random Forest Classification

1. Random Forest Classification Intuition

2. How to get the dataset

3. Random Forest Classification in Python

Evaluating Classification Models Performance

1. False Positives & False Negatives

2. Confusion Matrix

3. Accuracy Paradox

4. CAP Curve

5. CAP Curve Analysis

6. Conclusion of Part 3 – Classification

 Part 7: Natural Language Processing

1. Welcome to Part 7 – Natural Language Processing

2. How to get the dataset

3. Natural Language Processing in Python

 

 

Part 8: Deep Learning

1. Welcome to Part 8 – Deep Learning

2. What is Deep Learning?

Artificial Neural Networks

1. Plan of attack

2. The Neuron

3. The Activation Function

4. How do Neural Networks work?

5. How do Neural Networks learn?

6. Gradient Descent

7. Stochastic Gradient Descent

8. Backpropagation

9. How to get the dataset

10. Business Problem Description

11. ANN in Python

Convolutional Neural Networks

1. Plan of attack

2. What are convolutional neural networks?

3. Step 1 – Convolution Operation

4. Step 1(b) – ReLU Layer

5. Step 2 – Pooling

6. Step 3 – Flattening

7. Step 4 – Full Connection

8. Softmax & Cross-Entropy

9. How to get the dataset

10. CNN in Python

 

 

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