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