Data Science

• What are Decision Trees?
• Gini, Entropy criterions
• Decision trees in Classification
• Decision trees in Regression
• Ensembles
• Random Forest
• Boosting (Ada, Gradient, Extreme Gradient)
• SVM

• K-fold
• Repeated Hold-out Data
• Bootstrap aggregation sampling.

• Entropy
• Gini Index
• Information Gain
• Tree Pruning.

• What is Classification?
• Finding Patterns/Fixed Patterns
• Problems with Fixed Patterns
• Machine learning approach over fixed patter
• Decision Tree based classification.
• Ensemble Based Classification
• Logistic Regression (SGD Classifier)
• Accuracy measurements
• Confusion Matrix
• ROC Curve
• AUC Score
• Multi-class Classification
• Softmax Regression Classifier
• Multi-label Classification
• Multi-output Classification.

• Random Forest
• Bagging
• Boosting
• Adaptive Boosting
• Gradient Boosting
• Extreme Gradient Boosting
• Heterogeneous Ensemble Models
• Stacking
•  

• Multiple Linear Regressions (SGD Regressor)
• Gradient Descent (Calculus way of solving linear equation)
• Feature Scaling (Min-Max vs Mean Normalization)
• Feature Transformation
• Polynomial Regression
• Matrix addition, subtraction, multiplication and transpose
• Optimization theory for data scientist.

• Modelling ML problems with optimization requirements
• Solving unconstrained optimization problems
• Solving optimization problems with linear constraints
• Gradient descent ideas
• Gradient descent
• Batch gradient descent.
• Stochastic gradient descent.

• Train/Validation/Test split
• K-Fold Cross Validation
• The Problem of Over-fitting (Bias-Variance tread-off)
• Learning Curve
• Regularization (Ridge, Lasso and Elastic-Net)
• Hyper Parameter Tuning (Grid Search CV)

• What is Recommendation System?
• Top-N Recommender
• Rating Prediction
• Content based Recommenders.
• Limitations of Content based recommenders.
• Machine Learning Approaches for Recommenders.
• User-User KNN model, Item-Item KNN model
• Factorization or latent factor model
• Hybrid Recommenders
• Evaluation Metrics for Recommendation Algorithms
• Top-N Recommnder : Accuracy, Error Rate
• Rating Prediction: RMSE.

• Finding pattern and Fixed Pattern Approach
• Limitations of Fixed Pattern Approach
• Machine Learning Approaches for Clustering
• Iterative based K-Means Approaches
• Density based DB-SCAN Approach
• Evaluation Metrics for Clustering
• Cohesion, Coupling Metrics
• Correlation Metric.

• SVM Classifier (Soft/Hard – Margin)
• Linear SVM
• Non-Linear SVM
• Kernel SVM
• SVM Regression.

• Dimensionality Reduction
• Choosing Number of Dimensions or Principal Components
• Incremental PCA
• Kernel PCA
• When to apply PCA?
• Eigen vectors
• Eigen values.

• Pickle (Pkl file)
• Model load from Pkl file and prediction.

• A priori Algorithm
• Collaborative Filtering (User-Item based)

• Introduction to Deep Learning
• Tensor flow
• Keras
• Setting up new environment for Deep Learning
• Perceptron model for classification and regression
• Perceptron Learning
• Limitations of Perceptron model
• Multi-layer Feed Forward NN model for classification and regression
• ML-FF-NN Learning with backpropagation.
• Applying ML-FF-NN and parameter tuning
• Pros and Cons of the Model
• Introduction to CNN with examples.
  
  

• Bag of words
• Glove Dictionary
• Text Data Preparation
• Normalizing Text
• Stop word and Whitespace Removal
• Stemming
• Building Document Term Matrix

NLP (Natural Language Processing).