Data science & its importance
Key Elements of Data Science
Artificial Intelligence & Machine Learning Introduction
Who uses AI?
AI for Banking & Finance, Manufacturing, Healthcare, Retail and Supply Chain
What makes a Machine Learning Expert?
What to learn to become a Machine Learning Developer?
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
Deep Learning: A revolution in Artificial Intelligence
What is Deep Learning?
Advantage of Deep Learning over Machine learning
3 Reasons to go for Deep Learning
Real-Life use cases of Deep Learning
Neural Networks Basics
How Neural Networks Work?
Various activation functions – Sigmoid, Relu, Tanh
Perceptron and Multi-layer Perceptron
What is TensorFlow?
Constants, Placeholders, Variables
Creating a Model
Step by Step - Use-Case Implementation
Introduction to Keras
Understand Neural Networks in Detail
Illustrate Multi-Layer Perceptron
Backpropagation – Learning Algorithm
Understand Backpropagation – Using Neural Network Example
MLP Digit-Classifier using TensorFlow
Deep Neural Networks
Why Deep Networks
Why Deep Networks give better accuracy?
Understand How Deep Network Works?
How Backpropagation Works?
Illustrate Forward pass, Backward pass
Different variants of Gradient Descent
Types of Deep Networks
Activation and Loss functions
Hyper parameter tuning
Training challenges and techniques
Optimizers, learning rate, momentum, etc.
Convolutional Neural Networks
Introduction to CNNs
Architecture of a CNN
Forward propagation & Backpropagation for CNNs
Convolution, Pooling, Padding & its mechanisms
Understanding and Visualizing a CNN
An overview of pre-trained models (AlexNet, VGGNet, InceptionNet & ResNet) and Transfer Learning
Image classification using CNN
Advanced Computer Vision
Object & face recognition using techniques above
Natural Language Processing
Nltk, Gensim, vader, etc.
Bag of Words and Tf-IDF
Cosine Similarity of terms, documents concepts
Text Cleaning and Preprocessing using Regex
Tokenization, Stemming and Lemmatization
RNN And LSTM
Introduction to Sequential data
Word embeddings and lang translation
RNNs and its mechanisms
Vanishing & Exploding gradients in RNNs
Time series analysis
LSTMs with attention mechanism
Visualization Using Tensorboard
What is Tensor board?
Test vs Train set accuracy
CAM, Saliency and Activation maps
Reinforcement Learning And Gans
How GANs work?
Applications of GANs (Generative adversarial networks)