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コース概要
Introduction to Applied Machine Learning
- Statistical learning vs. Machine learning
- Iteration and evaluation
- Bias-Variance trade-off
Machine Learning with Python
- Choice of libraries
- Add-on tools
Regression
- Linear regression
- Generalizations and Nonlinearity
- Exercises
Classification
- Bayesian refresher
- Naive Bayes
- Logistic regression
- K-Nearest neighbors
- Exercises
Cross-validation and Resampling
- Cross-validation approaches
- Bootstrap
- Exercises
Unsupervised Learning
- K-means clustering
- Examples
- Challenges of unsupervised learning and beyond K-means
要求
Knowledge of Python programming language. Basic familiarity with statistics and linear algebra is recommended.
14 時間
お客様の声 (5)
トレーナーはこの件についてよく理解していることを示した。
Marino - EQUS - The University of Queensland
コース - Machine Learning with Python – 2 Days
Machine Translated
MLへの素晴らしい入門書でした!!本当に全部気に入りました。組織は完璧でした。講義やデモ、そして私たちだけの遊びに適した時間。適切なレベルで、多くのトピックに触れられました。彼はまた、カメラがオンになっていない場合でも、私たちを非常に魅了し続けるのが非常に上手でした。
Zsolt - EQUS - The University of Queensland
コース - Machine Learning with Python – 2 Days
Machine Translated
分かりやすい説明と質問に対する知識豊富な回答。
Harish - EQUS - The University of Queensland
コース - Machine Learning with Python – 2 Days
Machine Translated
The knowledge of the trainer was very high and the material was well prepared and organised.
Otilia - TCMT
コース - Machine Learning with Python – 2 Days
I thought the trainer was very knowledgeable and answered questions with confidence to clarify understanding.
