Best Artificial Intelligence Training

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