Email: registration@kalpracademy.com
Contact: +1-281-801-0921
Location: 13111 Westheimer Rd., Suite 311, Houston, TX, 77077
Email: registration@kalpracademy.com
Contact: +1-281-801-0921
Location: 13111 Westheimer Rd., Suite 311, Houston, TX, 77077
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Probability for Data Scientist
Basic Probability and Conditional Probability Properties of Random Variables Expectations (Mean) and Variance Entropy and cross-entropy Covariance and correlation Estimating probability of Random variable Understanding standard random processes. Normal Distribution Binomial Distribution Multinomial Distribution Bernoulli Distribution Probability, Prior probability, Posterior probability Bayes Theorem Naive Bayes Naive Bayes Algorithm Normal Distribution Install python (Anaconda) Jupiter Notebook Install NumPy, Pandas, Matplotlib, Seaborn and SciKit Learn Spyder IDE Strings Lists Tuples Sets Dictionaries Control Flows Functions Formal, Positional, Keyword arguments Predefined functions (range, len, enumerates etc…) Packages required for data Science in R and Python. Packages required for data Science in R and Python. One-dimensional Array Two-dimensional Array Pre-defined functions (arrange, reshape, zeros, ones, empty) Basic Matrix operations Scalar addition, subtraction, multiplication, division Matrix addition, subtraction, multiplication, division and transpose Slicing Indexing Looping Shape Manipulation Series Data Frame Group By Crosstab Apply Structured Data Preparation Data Type Conversion Category to Numeric Numeric to Category Data Normalization: 0-1, Z-Score Skew Data handling: Box-Cox Transformation Missing Data treatment.Exploratory Data Analysis (EDA)
Statistical Data Analysis Exploring Individual Features Exploring Bi-Feature Relationships Exploring Multi-feature Relationships Feature/Dimension Reduction: Principal Component Analysis PCA advantages Interpretation of PCA output Covariance & Correlation Relating PCA to Covariance/Correlation. Combine Features Split Features. Bar Chart Histogram Box whisker plot Line plot Scatter Plot Heat Map Matplotlib, Seaboarn– Visualization.Regression (Supervised Learning)
What is regression? Use Cases: Regression Linear Regression Theory behind Linear Regression Model Evaluation and related metrics Root Mean Square Error (RMSE) R-Square, Adj R-Square Feature selection methods Linear regression – Practice Problem. Understand what overfitting is and under fitting model Visualize the overfitting and under fitting model How do you handle overfitting? 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.Classification (Supervised Learning)
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 StackingMultiple/Polynomial Regression (scikit-learn)
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.Optimization Theory (Gradient Descent Algorithm)
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.Model Evaluation and Error Analysis
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.Clustering (Unsupervised Learning)
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.Natural Language Processing Text analytics (NLP):
Bag of words Glove Dictionary Text Data Preparation Normalizing Text Stop word and Whitespace Removal Stemming Building Document Term MatrixNLP (Natural Language Processing).
