Artificial Intelligence Certification Training

Machine Learning: Supervised Learning

• • Introduction to Machine Learning
  • Understanding Supervised Learning.
  • Linear Regression Theory
  • Supervised Learning Regression
  • Linear Regression
  • Multiple Linear Regression
  • Bias-Variance Trade-Off
  • Logistic Regression
  • K-Nearest Neighbors (KNN)
  • Simple Vector Machine (SVM)
  • Decision Trees
  • Bagging
  • Boosting, AdaBoost & XGBoost
  • Naïve Bayes Classifier

Machine Learning: Unsupervised Learning

• • K-Means Clustering
  • Hierarchical Clustering
  • Dimensionality Reduction
  • Linear Discriminant Analysis
  • Time Series Modelling
  • Principal Component Analysis (PCA)
  • Reinforcement Learning
  • Model Comparisons
  • Analysis Considerations
  • Clustering Animals
  • Customer Segmentation
  • Optimal Number of Clusters
  • Cluster Based Incentivization
  • Image Segmentation

Deep Learning with Tensorflow, Keras

• • What is Neural Networks
  • Gradient Descent
  • Perceptron & Neural Networks
  • Batch Normalization
  • Activation and Loss functions
  • Hyper parameter tuning
  • Deep Neural Networks
  • Tensor Flow & Keras for Neural Networks & Deep Learning
  • Understand Neural Networks in Detail
  • PyTorch Tensors
  • PyTorch Autograd
  • Illustrate Multi-Layer Perceptron
  • Backpropagation – Learning Algorithm
  • Understand Backpropagation – Using Neural Network Example
  • MLP Digit-Classifier using TensorFlow
  • Building a multi-layered perceptron for classification

Neural Networks, Convolutional Neural Network,Recurrent Neural Networks

• • Artificial neural networ