Fundamentals of Machine Learning

Eğitmen: Dr. H. Sait Ölmez

Eğitimin İçeriği:

  1. Introduction to Machine Learning
    1. General terminology for Machine Learning
    2. Types of Machine Learning
  2. A Primer on Exploratory Data Analysis (EDA) and Data Visualization Techniques
    1. Data types
    2. Importance of visualizing data
    3. Tools and techniques used in data visualization
  3. Data Pre-processing for Machine Learning
    1. Assessing the data quality, data cleaning and preparation
    2. Data Sampling (random, stratified, and cluster sampling)
    3. Variable transformation techniques
    4. Feature Engineering
    5. Dealing with Outliers
    6. Dealing with Missing Data
    7. Dealing with Imbalanced Data
  4. Logistic Regression
  5. Model Accuracy and Bias-Variance Tradeoff
    1. Challenges in estimating the predictive accuracy
    2. Concepts of under-fitting and over-fitting (bias vs variance)
    3. Model accuracy
    4. Model selection and validation techniques
    5. Confusion matrix and other accuracy metrics
  6. Naïve-Bayes Classifiers
  7. Instance-based Models: k Nearest Neighbors (kNN)
  8. Decision Trees
  9. Ensemble Learning Methods
    1. Why does an ensemble of weak learners work?
    2. Methods used in Ensemble Learning
    3. Bagging (Bootstrap Aggregating)
      • Random Forest Classifier
      • Extreme Trees
    4. Boosting
      • Adaboost (ADAptive BOOSTing)
      • Gradient Boosting (GB)
    5. Support Vector Machines (SVM)
    6. Unsupervised Learning: Clustering
      1. Major clustering approaches
      2. Methods of Clustering
        • k–Means Clustering
        • Hierarchical Clustering
        • Density-based clustering
      3. What to use when?

Note: Numerous examples will be conducted throughout the course using Python and its related libraries (numpy, pandas, scipy, scikit-learn)

Eğitimin Hedef Kitlesi: Kurum içinde “Veri Analisti” ve/veya “Veri Bilimci” olarak çalışanlar, zaman serileri ile tahmin çalışmaları yapan analistler (quant’lar).

Eğitimin Seviyesi: Orta-İleri

Eğitim Süresi: 12 gün

Ek Bilgiler: Katılımcıların Python programlama dilini pratik seviyede kullanabildikleri ve temel seviyede İstatistik birikimleri bulunduğu varsayılmaktadır.