Introduction to Machine Learning

In the Introduction to Machine Learning training course, you'll learn:

• High Level Overview of Data Science and ML Concepts
  • Supervised versus Unsupervised Learning
  • Basic ML Vocabulary
  • Tools and Libraries in Various Languages
  • Machine Learning on Big Data / Scalable Machine Learning
• Introducing Data Science and Data Analytics in Python
  • NumPy and SciPy
  • Pandas
  • Data Exploration
• Introducing scikit-learn Library in Python
  • scikit-learn Capabilities
  • scikit-learn Structure / Base Classes (Estimators / Transformers)
  • Concepts
• Feature Extraction and Feature Engineering
  • Feature Selection
  • Dealing with Categorical Variables
  • Scaling and Normalization
  • Dimensionality Reduction / PCA
• Clustering and Unsupervised Learning
  • Clustering Intro
  • How k-Means Works
  • k-Means in scikit-learn
  • Interpreting Clusters
  • Clustering and Outlier Detection for Fraud Detection
• Supervised Learning: Regression and Classification
  • Introducing Regression and Classification
  • Survey of Regression and Classification Methods
  • Fitting Linear Models
  • Logistic Regression
  • Decision Tree Learning
  • Random Forests
  • Evaluating Models: Accuracy, Precision/Recall/F1, ROC Curve
• Recommendations
  • User/Items Matrix
  • Ratings: Implicit vs. Explicit
  • Cluster-Based Recommendation
  • k-Nearest-Neighbors (KNN) and Recommendations
  • Similarity Matrices
  • Matrix Decomposition: SVD vs. ALS
• Productizing ML
• Using Prediction.IO stack