← back to curriculum
github ↗
phase 2
Ml Fundamentals
18 lessons · click any to read the docs
phase progress
0
/ 18 lessons · 0%
01
What Is Machine Learning
01-what-is-machine-learning
read →
02
Linear Regression
02-linear-regression
read →
03
Logistic Regression
03-logistic-regression
read →
04
Decision Trees
04-decision-trees
read →
05
Support Vector Machines
05-support-vector-machines
read →
06
Knn And Distances
06-knn-and-distances
read →
07
Unsupervised Learning
07-unsupervised-learning
read →
08
Feature Engineering
08-feature-engineering
read →
09
Model Evaluation
09-model-evaluation
read →
10
Bias Variance
10-bias-variance
read →
11
Ensemble Methods
11-ensemble-methods
read →
12
Hyperparameter Tuning
12-hyperparameter-tuning
read →
13
Ml Pipelines
13-ml-pipelines
read →
14
Naive Bayes
14-naive-bayes
read →
15
Time Series
15-time-series
read →
16
Anomaly Detection
16-anomaly-detection
read →
17
Imbalanced Data
17-imbalanced-data
read →
18
Feature Selection
18-feature-selection
read →