Data Science in Python Course Syllabus

Machine Learning in Python (Fundamental)

• Steps of ML Algorithms
  • Data Gathering
  • Data Preparation
  • Data Exploration
  • Training
  • Testing and Evaluation
    • Evaluation Criteria
      • Error Rate
      • Precision
      • Recall
• Supervised Learning
  • Classification
    • Task Intro
    • k-Nearest Neighbors (KNN)
      • Algorithm Intro
      • Loading and Parsing Data
      • Distance Measurements
      • Normalizing Values
      • Testing the Classifier
      • Project: Handwritten Digits Recognition
    • Decision Trees
      • Algorithm Intro
      • Information Gain
      • Splitting Data
        • Finding the Best Feature to Split on
        • Tree-building
      • Plotting the Tree
      • Testing the Classifier
      • Project: Buying a Car
    • Naïve Bayes
      • Algorithm Intro
      • Probability Theory
        • Bayesian Decision Theory
        • Conditional Probability
      • Classification based on Probabilities
      • Bag of Words (BoW) Model
      • Modifying the Classifier
      • Testing the Classifier
      • Displaying the Targets
      • Project: Text (Documents) Classification
      • Project: Junk Email Classification

Machine Learning in Python (Supervised Learning I)

• Supervised Learning
  • Classification
    • Logistic Regression
      • Task Intro
        • The Sigmoid Function
      • Classification with Logistic Regression
      • Optimization
        • Finding the Best Coefficients
        • Gradient Ascent Optimization (GAO)
        • Gradient Descent Optimization (GDO)
      • Plotting the Decision Boundary
      • Missing Values
      • Project: Finding Important Academic Factors for Job Seekers
    • Support Vector Machines (SVM)
      • Algorithm Intro
      • Data Separation
        • Finding the Maximum Margines
      • Optimization
        • Platt’s SMO Algorithm
      • Kernels
        • Data Mapping
      • Testing the Algorithm
      • Project: Handwritten Digits Recognition
    • AdaBoost
      • Algorithm Intro
      • Combining Classifiers
      • Bagging
      • Boosting
      • Improving the Classifier
      • Weak Learners
      • Testing the Classifier
      • Project: Handwritten Digits Recognition
    • XGBoost
      • Algorithm Intro
      • Get Started with XGBoost
        • Boosted Trees
        • Model IO
      • Gradient Boosting
      • Parameters
      • Random Forests in XGBoost

Machine Learning in Python (Supervised Learning II)

• Supervised Learning
  • Regression
    • Linear Regression
      • Task Intro
      • Best-fit Lines
      • Standard Regression Function
      • Locally Weighted Function
      • Ridge Regression
        • Coefficients
      • Forward Stagewise Regression
      • Project: Age Prediction
    • Tree-based Regression
      • Task Intro
      • Modeling Complex Data
      • Tree Building
        • Continuous Features
        • Discrete Features
        • CART Approach
      • Tree Splitting
      • Tree Pruning
        • Prepruning
        • Postpruning
      • Model Trees
      • Project: Price Prediction
    • Time Series Regression
      • Task Intro
      • Analyzing Time Series Data
        • Serial Correlation
      • General Assumptions
      • Using Regression Models for Forecasting
      • Autoregressions
        • Models
        • Forecast Errors
      • Algorithms
      • Project: Weather Temperature Forecast
      • Project: GDP Growth Forecasting

Machine Learning in Python (Unsupervised Learning)

• Unsupervised Learning
  • Clustering
    • Task Intro
    • k-Means Clustering
      • Algorithm Intro
      • Support Functions
      • Post-processing and Performance Improvement
      • Bisecting k-means
      • Project: Clustering Geo-coordinates
    • Fuzzy Clustering
      • Algorithm Intro
      • Fuzzy Logic
        • Introduction
        • Fuzzy Sets
        • Fuzzy Operations
        • Fuzzification and Defuzzification
      • Analyzing Data Points
        • Initialization
      • Finding Out the Centroid
      • Distance Measurement
      • Project: Car Engine Performance Modeling
  • Association Rule Mining
    • Apriori Algorithm
      • Algorithm Intro
        • Principles
      • Association Mining and Analysis
      • Finding Frequent Itemsets
      • Mining Association Rules
      • Project: Voting Patterns Analysis
    • FP-growth
      • Algorithm Intro
      • Finding Common Patterns
      • FP-trees
        • Data Structure
        • Construction of Trees
        • Mining Frequent Items
        • Pattern Extraction
      • Project: Finding Co-occurring Words

Feature Engineering and Data Pre-processing

• Working on Data
  • Data Preprocessing
    • Aggregation
    • Sampling
    • Dimensionality Reduction
    • Discretization and Binarization
  • Dataset Balancing
    • Imbalanced Data Challenges
    • Choosing Proper Evaluation Metric
    • Training Set Resampling
      • Under-sampling
      • Over-sampling
      • Ration Seletction
    • K-fold Cross-Validation
  • Data Testing
  • Outlier Detection
    • What is Outlier Data
    • Overview of Outlier Detection Methods
      • Isolation Forest
      • Local Outlier Factor
      • One-Class SVM (SGD)
      • Robust Covariance
    • Novelty Detection
• Principal Component Analysis (PCA)
  • Task Intro
  • Data Simplification
  • Dimensionality Reduction
    • Goals
  • PCA Concepts
    • Moving the Coordinate Axes
    • A Usage Example in NumPy
    • Missing Values Replacement
  • Project: Dimensionality Reduction in Factories Data
• Singular Value Decomposition (SVD)
  • Task Intro
  • Matrix Factorization (MF)
  • Latent Semantic Indexing (LSI)
  • Applications in Practice
    • Recommender Systems (RecSys)
      • Collaborative Filtering
      • Measuring Similarity
        • Item-based Similarity
        • User-based Similarity
      • Rating Estimation
  • SVD in Python
  • Project: Food Recommender
  • Project: Fashion Recommender
  • Project: Image Compression
• Big Data
  • Task Intro
  • MapReduce
    • Algorithm Intro
    • Applications
  • Hadoop Streaming
    • Intro
    • Distributed Mapper/Reducer
    • Mrjob in Python
    • Running Jobs in AWS
    • Running Jobs in EMR
  • Project: Distributed SVM