A-Warded
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Machine Learning
Machine Learning
35 lessons across 6 topics
1. Foundations
1
Computational Tools
2
Linear Algebra Review
3
Optimization Intro
4
Probability Basics
5
Probability Models
6
Statistics Review
2. Supervised Learning
7
Decision Trees
8
Ensemble Methods
9
Linear Regression
10
Logistic Regression
11
Model Evaluation
12
Support Vector Machines
3. Unsupervised Learning
13
Anomaly Detection
14
Clustering Basics
15
Gaussian Mixtures
16
Manifold Learning
17
Pca
18
Representation Learning
4. Probabilistic Models
19
Bayesian Inference
20
Graphical Models
21
Hidden Markov Models
22
Inference Algorithms
23
Probabilistic Programming
5. Deep Learning
24
Convolutional Networks
25
Deep Generative Models
26
Neural Networks
27
Optimization Deep
28
Recurrent Networks
29
Regularization Dl
6. Deployment
30
Case Studies
31
Ethics
32
Mlops
33
Model Selection
34
Monitoring
35
Scalable Systems
