Python Basics
  • Python and It’s Module
  • Python Basics
  • Python Functions and Packages
  • Working with Data Structures,
  • Arrays, Vectors & Data Frames
  • Jupyter Notebook – Installation & Function
  • Pandas, NumPy, Matplotlib, Seaborn
Supervised learning
  • Linear Regression
  • Multiple Variable Linear Regression
  • Logistic Regression
  • Naive Bayes Classifiers
  • k-NN Classification
  • Support Vector Machine
MODULE 2
  • Ensemble Techniques
  • Decision Trees
  • Random Forests
MODULE 3
  • Unsupervised learning
  • K-means Clustering
  • Hierarchical Clustering
  • Dimension Reduction – PCA
MODULE 4
  • Featurization, Model Selection & Tuning
  • Feature engineering
  • Model selection and tuning
  • Model performance measures
  • Regularizing Linear models
  • ML pipeline
  • Bootstrap sampling
  • Grid search CV
  • Randomized search CV
  • K fold cross-validation
MODULE 5
  • Customer churn out rate
  • Financial habit analysis
  • Breast cancer prediction
  • Travel insurance prediction
ARTIFICIAL INTELLIGENCE
  • Introduction to Neural Networks and Deep
  • Learning Introduction to Perceptron &
  • Neural Networks
  • Activation and Loss
  • functions Gradient
  • Descent
  • Batch Normalization
  • TensorFlow & Keras for Neural
  • Networks Hyper Parameter Tuning
MODULE 2
  • Computer Vision
  • Introduction to Convolutional Neural Networks
  • Introduction to Images
  • Convolution, Pooling,
  • Padding & its Mechanisms
  • Forward Propagation & Backpropagation for CNNs
  • CNN architectures like AlexNet, VGGNet, InceptionNet & ResNet
  • Transfer Learning
  • Object Detection
  • YOLO, R-CNN, SSD
  • Semantic Segmentation
  • U-Net
  • Face Recognition using Siamese Networks
  • Instance Segmentation
MODULE 3
  • NLP (Natural Language Processing)
  • Introduction to NLP
  • Stop Words
  • Tokenization
  • Stemming and Lemmatization
  • Bag of Words Model
  • Word Vectorizer
  • TF-IDF
  • POS Tagging
  • Named Entity Recognition
  • Introduction to Sequential data
  • RNNs and its Mechanisms
  • Vanishing & Exploding gradients in RNNs
  • LSTMs - Long short-term memory
  • GRUs - Gated Recurrent Unit
  • LSTMs Applications
  • Time Series Analysis
  • LSTMs with Attention Mechanism
  • Neural Machine Translation
  • Advanced Language Models:
  • Transformers, BERT, XLNet
MODULE 4
  • Introduction to
  • Reinforcement Learning (RL)
  • RL Framework
  • Component of RL Framework
  • Examples of RL Systems
  • Types of RL Systems
  • Q-learning
MODULE 5
  • Dynamic SQL
  • Introduction to Cursors
  • Types of Cursors
  • Advantages of cursors
FUNCTIONS
  • Projects based on the previous modules so as to have a better understanding of the concepts
  • To create an automation using computer vision to impute dynamic bounding boxes to locate cars or vehicles on the road.
  • To build a NLP classifier which can use input text parameters to determine the label/s of the blog.
  • To build a semi-rule-based text chat bot which can give static responses to the user depending on the inputs for industrial safety and incidents
Best Artificial Intelligence Training in Hyderabad

Preview this course

  • Duration 25 Hours
  • Students 21
  • Days 40 Days
  • Resume Preparation Yes
  • Interview Guidance Yes