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Machine Learning Specialty

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DURATION: 5 Day (40 hours).
Time Division (Break: 15 + 45 + 15 mines).
Course Outcomes:

  • Understand the fundamentals of Data Science and Machine Learning.
  • Analyse and pre-process data proficiently using Python.
  • Apply Supervised Machine Learning techniques for regression and classification.
  • Apply Unsupervised Machine Learning for clustering and natural language.
  • Introduction to Deep learning concepts.

Important Note:

  • Courseware – Reference material/ppt along with lab files/exercises will be provided.

Module 1: Introduction to Data Science & Machine Learning:

  • Need for Data Science and Machine Learning.
  • Types of Analytics.
  • Lifecycle of a Data Science project.
  • Skills for a Data Scientist role.
  • Types of Machine Learning.

Module 2: Python for Data Analysis & Pre-processing:

Introduction to Python

  • Python Libraries – NumPy, Pandas, matplotlib, Seaborn scikit-learn, Tensor Flow, Keras, Pytorch.
  • Exploratory Data Analysis (EDA).
  • Data Cleaning Techniques, Handling Missing Data, Handling Categorical Data.
  • Introduction to EDA, 2D Scatter-plot, 3D Scatter-plot, Pair plots.
  • Univariate, Bivariate, and Multivariate Analysis, Box-plot.

Data Pre-Processing

  • Need for Data Pre-Processing.
  • Handling Missing Values.
  • Label-Encoding for Categorical Data.
  • Hot-Encoding for Categorical Data Explained.

Data Transformation

  • Need for Data Transformation.
  • Concept of Data Normalization.
  • Data Normalization Techniques – Standard Scalar & Minmax.
  • Train, Test & Validation of Data.

Module 3: Supersized Machine Learning – Regression

Simple Linear Regression

  • Concept of Linear Regression.
  • Ordinary Least Square and Regression Errors.
  • Data Processing & Train and Test of Model.
  • Model Evaluation Parameters like R-squared, Score, RMSE and their Interpretations.
  • Prediction Plot & its Interpretation.
  • Hands-on Problem.

Multiple Linear Regression

  • Concept of Multiple Linear Regression.
  • Degrees of Freedom.
  • Adjusted R-Squared.
  • Assumptions of Multiple Linear Regression – Linearity, Multicollinearity, Autocorrelation,.
  • Indigeneity, Normality of Residuals, Homoscedasticity, etc..
  • Concept of time-lag data in Autocorrelation.
  • Concept of Dummy variable trap.
  • Hands-on Problem.

Module 4: Supervised Machine Learning – Classification

Logistic Regression

  • Concept of Logistic Regression.
  • Concept of Stratification.
  • Concept of Confusion Matrix.
  • Hands-on Problem.

Support Vector Machine (SVM)

  • Common Sensical Intuition of SVM.
  • Mathematical Intuition of SVM.
  • Different types of SVM Kernel Functions.
  • Hands-on Problem (Preferred: IRIS Classification Problem).

Decision Tree Classifier

  • Decision Tree Classifier.
  • Optimal Model Selection Criterion in Decision Tree.
  • Hands-on Problem.

Random Forest Classifier

  • Ensemble Learning and Random Forests.
  • Bagging and Boosting.
  • Hands-on Problem.

Evaluation Metrics for Classification Models

  • Need for Evaluation and Accuracy Paradox.
  • Different Measures for Classification Models – Accuracy, Precision, Recall, F1 Score, etc.
  • Threshold and Adjusting Thresholds.
  • AUC ROC Curve.
  • Hands-on Problem.

Module 5: Feature Selection and Dimensionality Reduction
Univariate Feature Selection

  • Feature Selection Importance.
  • Concept of Univariate Feature Selection.
  • F-Test for Regression and Classification.
  • Hands on F-test (p value analysis).
  • Chi-Squared for Classification.
  • Feature Selection Techniques – Select Best, Select Percentile & Generic Univariate Select.
  • Hands-on Chi-squared (p value analysis).

Recursive Feature Elimination (RFE)

  • Concept of Recursive Feature Elimination (RFE).
  • Feature Importance Score/Feature Ranking.
  • Hands-on RFE.

Principle Component Analysis (PCA)

  • Need to reduce dimensions and Importance of PCA.
  • Mathematical Intuition of PCA & Steps to calculate PCA.
  • Hands-on PCA (Model Comparisons with PCA & without PCA recommended).

Module 6: Cross validation & Hyper parameter Tuning

  • Cross Validation.
  • Importance of Cross Validation.
  • Parameter & Implementation of Cross Validation.
  • Hands-on Problem (Drawing inference from results).

Hyper parameter Tuning

  • Concept of Hyper parameter Tuning.
  • Grid Search & Randomized Search.

  • Hands-on GridSearchCV (analyse results).

Module 7: Supervized Machine Learning – Natural Language Processing

  • Introduction to NLP.
  • Basic Concepts of NLP: Tokenization, stop words, Stemming, Lemmatization, etc.

  • Tfidf Vector and its mathematical intuition.

  • Recommendation system example.

Module 8: Supervized Machine Learning – Clustering

  • Introduction to Clustering.
  • Mathematical intuition behind cluster formation.

  • Elbow method & its mathematical intuition.

  • K-means Clustering Implementation (numerical).

  • K-means Clustering Implementation (natural language processing).

  • Introduction to Clustering.

Module 9: Introduction to Deep Learning

  • Need & Applications of Deep Learning.
  • Working of Artificial Neural Network.
  • Backend (Tensor Flow) & Frontend (Keras).
  • Concept of Tensor.
  • Keras Model Building Overview – Construct, Compile & Evaluate.
  • Activation Function.
  • Loss Functions.
  • Optimization Techniques.
  • Evaluation metrics for Deep Learning.
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Machine Learning Specialty

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