Course content

Basic Concept
  • Train,Test & Validation Distribution
  • ML Strategy
  • Computation Graph
  • Evaluation Metric
  • Human Level Performance
Supervised
  • Linear Regression
  • Logistic Regression
  • Gradient Descent
  • Decision Tree
  • Random Forest
  • Bagging & Boosting
  • KNN
Unsupervised
  • K-Means
  • Hierarichal Clustering
Python
  • Basic Programming
  • NLP Libraries
  • OpenCV
Basic Statistics
  • Sampling & Sampling Statistics
  • Hypothesis Testing
Calculus
  • Derivatives
  • Optimization
Linear Algebra
  • Function
  • Scalar-Vector-Matrix
  • Vector Operation
Probability
  • Space
  • Probability
  • Distribution
Introduction
  • Intro
  • Deep Learning Importance [Strength & Limiltation]
  • SP | MLP
Feed Forward & Backward Propagation
  • Neural Network Overview 
  • Neural Network Representation
  • Activation Function
  • Loss Function
  • Importance of Non-linear Activation Function
  • Gradient Descent for Neural Network
Practical Aspect
  • Train, Test & Validation Set
  • Vanishing & Exploding Gradient
  • Dropout
  • Regularization
Optimization
  • Bias Correction
  • RMS Prop
  • Adam,Ada,AdaBoost
  • Learning Rate
  • Tuning 
  • Softmax
Environment
  • Scikit Learn
  • NLTK
  • Spacy & Gensim
  • OpenCV
  • Tensorflow
  • Keras
Text Processing
  • Representation
  • Data Cleaning
  • Data Preprocessing
  • Similarity
Image Processing
  • Image
  • Image Transformation
  • Filters 
  • Noise Removal
  • Correlation & Convolution
  • Edge Detection
  • Non Maximum Suppression & Hysterisis
  • Fourier Domain
  • Video Processing
Speech Data Analytics Feature Extraction
  • Image Feature
  • Descriptors
Object Detection
  • Detection  & Classification
CNN
  • Computer Vision
  • Padding
  • Convolution
  • Pooling
  • Why Convolution
Deep Convolution Model
  • Case Studies
  • Classic Networks
  • Inception
  • Open Source Implementation
  • Transfer Learning
Detection Algorithm
  • Object Localization
  • Landmark Detection
  • Object Detection
  • Bounding Box Prediction
  • Yolo
Face Recognition
  • What is Face Recognition
  • One Shot Learning
  • Siamese Network
  • Triplet Loss
  • Face Verification
  • Neural Style Transfer
  • Deep Conv Net Learning
  • Why Sequence Model
  • RNN Model
  • Backpropogation through time
  • Different Type of RNNs
  • GRU
  • LSTM
  • Biderectional LSTM
  • Deep RNN
  • Word Embedding
  • Debiasing
  • Negative Sampling
  • Elmo & Bert
  • Beam Search
  • Attention Model
  • Autoencoders & Decoders
  • Adversial Network
  • Active Learning
  • Q Learning
  • Exploration & Exploitation
Introduction to Machine Learning
  • Business Case evaluation
  • Data requirements and collection
  • Evaluation metrics
Machine Learning
  • Profit of 50_startups data prediction
  • Extra marital affair prediction
  • Fraud data analytics
  • Fabric sales analysis
  • Classification of animals data
  • Crime data analysis using clustering method and airlines data to obtain optimum number of clusters.
Python Programming
  • Resource Information Analysis
  • Text Cleaning of Customer reviews using NLP
  • Image Manipulation (Loading, Rotation etc.)
Mathematics Foundation
  • Sampling & Sampling Statistics
  • Hypothesis Testing
  • Calculus Problems
  • Linear Algebra Problems
  • Probability Problems
Intro to Neural Network & Deep Learning Parameter & Hyperparameter
  • Risk Evaluation
  • Prediction of claim amount
  • Emotor temp prediction
  • User Behavioural Pattern
(2 ANN assignments+ 2 Parameter and hyperparameters) Data Processing
  • User review data load and familiriaty with data and environment
  • E commerce Product Similarity
  • Sentiment classification of movie reviews
  • Emotion Mining of user reviews”
  • Vehicle edge detection
  • Cleaning of hand-written digits data
  • Image data Augumentation
  • Facial feature detection
  • Image data wrangling for classification
  • Video Analysis of a short film
  • Speech data Analysis w.r.t emotion
CNN
  • Ecommerce product image classification
  • Disease prediction based on images
(2 CNN algorithms)
  • Vehicle identification(Object Detection)
  • Animal Classification(Object Classification)
  • Spatial Image classification (Image segmentation)
  • Face detection
  • Face recognition (Attendance using facial recognition)
RNN
  • Next word prediction (Vanilla RNN)
  • Twitter data analysis using Named Entity Recognition(NER)
  • Retail data – Word2vec
  • NER and Forecasting of Oil price prediction
  • Auto text composer (NER language model)
  • Auto text composer (NER language model)
  • Q and A Chatbot
  • Real life voice Recognition
Generative
  • Machine Translation
  • New Image generation based on existing images
Reinforcement Learning
  • Game Intelligence

Mode of Training

Online

Total duration of the course

5 to 7 weeks

Training duration per day

50 mins - 90 mins

Communication Mode

Go to meeting, WEB-EX

Software access:

Software will be installed/Server access will be provided, whichever is possible

Material

Soft copy of the material will be provided during the training.

Training

Both weekdays and weekends

Training Fee

 $400