AI&ML

Description

AIML can be used to create chatbots, and computer programs that mimic human conversation. AIML can also be used to create virtual assistants,

Course Content

1- Foundations for AIML

• Introduction to Python, Data Science for Predictions, EDA and data Visualization
  Preface to Data Science
  Fundamentals  to Python Data Science libraries (Pandas, numpy, matplotlib, seaborn)
  About  EDA
  Stages of EDA and EDA in data science life cycle
  Hands-on Visualization techniques
  Data visualization using Seaborn
  Case Study (Telecommunication Dataset)
  Distribution of Data
  Types of variables and missing values
  Descriptive data exploration – five-point summary
  Data Distribution – pairplot and heatmap
  EDA Summary

2- Supervised Learning

• Intro to Supervised Learning, Creating Regression Models for Prediction
  Introduction  to Machine Learning from Data
  Intro to scikit-learn python ML library
  Supervised Learning, train, test data
  Overfitting and underfitting
  Linear Regression
  Linear relation between two variables, measure of association – correlation and covariance
  A simple fit, best fit line – measure of regression fit
  Multiple regression
  R Squared.
  Introduction to Logistic Regression
  Logit function in Logistic Regression
  Confusion Matrix
  F1 Score, gini index and ROC curve
  Pros and Cons of Logistic Regression Case Study – Domain Specific – TBD

3- Ensemble Techniques and Model Tuning

• Introduction to Decision Tree and Ensemble Techniques
  Entropy
  Gini Index
  Pruning
  Ensemble Methods
  Bias-Variance error
  Bagging concept
  Boosting concept
  Random Forest
  Feature Engineering and Model Tuning
  Case Study – Domain Specific – TBD

4- Unsupervised Learning and Neural Network

• Introduction to Unsupervised Learning
  K-Means Clustering
  Hierarchical Clustering Case Study – Domain Specific – TBD Intro to NN, Tensorflow and Keras
  Building blocks and activation functions
  Computer Vision and Natural Language Processing Intro to CNN – Working with images
  CNN Architectures and Transfer Learning Intro to NLP – Working with Text Data
  RNN and LSTM – Intro and Application Case Study – Domain Specific – TBD

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