Curriculum

Artificial Intelligence and Machine Learning

Module 1: Introduction to AIML
  •  Introduction to Artificial Intelligence
  •  History of Artificial Intelligence
  •  Introduction to Machine Learning
  •  Machine Learning in Data Science
  •  Applications of Machine Learning
  •  Introduction to Deep Learning
  •  Deep Learning in Data Science
  •  Use Cases of Artificial Intelligence
  •  Scope of AI and ML
  •  AI Tools & Packages

Module 2: Python for Data Science
  • Python Basics
  • Python Packages
  • Working with NUMPY
  • Working with Pandas
  • Introduction to Data Visualization
  • Exploratory Data Analysis with Matplotlib and Seaborn
  • Exploring Tensorflow and Keras

Module 3: Data Wrangling Techniques
  • Introduction to Data Preprocessing
  • Importing the Dataset
  • Handling Missing Data (Mean, Median, Mode)
  • Working with Categorical Data (OneHotEncoding, LabelEncoding)
  • Finding Outliers
  • Handling Outliers using Quantile Method, Box Plot
  • Transformation Methods (Log, Reciprocal, Square root, Exponential, BoxCox)
  • Feature Scaling
  • Splitting the data into Train and Test set
  • Feature Selection (Forward Selection, Backward Elimination)

Module 4: Supervised Learning - Regression
  • Introduction to Regression
  • Linear Regression
  • Multi Linear Regression
  • Polynomial Regression

Module 5: Supervised Learning - Classification
  • Introduction to Classification
  • Logistic Regression
  • Decision Tree Classification
  • Random Forest Classification
  • K-nearest Neighbors
  • Naïve-Bayes
  • Support Vector Machine
  • Ensembling Techniques

Module 6: ML With TensorFlow
  •  Introduction to TensorFlow library
  •  Random Forest Model
  •  Gradient Boosted Tree Model

Module 7: Model Evaluation Metrics
  •  Regression Evaluation Metrics
  •  MAE
  •  MSE
  •  R Squared
  •  RMSE
  •  Classification metrics
  •  Confusion Metrics
  •  Accuracy
  •  Precision
  •  Recall
  •  F1 Score
  •  AUC ROC Curves

Module 8: Model Hyper-parameter Optimization
  •  Handling Imbalanced Data
  •  Oversampling
  •  Undersampling
  •  SMOTE
  •  Ensembling Techniques
  •  Hyper-parameter tuning
  •  Grid Search
  •  Randomize Search

Module 9: ML Flask Local Deployment
  •  Introduction to Flask
  •  Flask Basics
  •  Display output in web page
  •  Routing templates
  •  Fetching values from templates and performing some arithmetic calculation

Module 10: Introduction to Neural Network
  • Introduction to Neural Network
  • What is an ANN
  • Forward propagation
  • Activation function
  • Backward propagation
  • Gradient Descent

Module 11: Introduction to CNN
  • Introduction to CNN
  • Data Augmentation
  • Conv-layers
  • Pooling layers
  • Flatten layer
  • Fully connected layer
  • Evaluating CNN Model

Module 12: Transfer Learning
  • Introduction to Transfer Learning
  • VGG-16
  • Inception
  • Xception
  • ResNet 50
  • Evaluating Transfer Learning Model

Module 13: DL Flask Local Deployment
  • Working with Flask framework
  • Building an application with Flask Framework
  • Integrating Deep learning & Transfer Learning model with Web Application