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
- K-Means
- Hierarichal Clustering
- Basic Programming
- NLP Libraries
- OpenCV
Basic Statistics
- Sampling & Sampling Statistics
- Hypothesis Testing
- Derivatives
- Optimization
- Function
- Scalar-Vector-Matrix
- Vector Operation
- Space
- Probability
- Distribution
Introduction
- Intro
- Deep Learning Importance [Strength & Limiltation]
- SP | MLP
- 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
- Bias Correction
- RMS Prop
- Adam,Ada,AdaBoost
- Learning Rate
- Tuning
- Softmax
Environment
- Scikit Learn
- NLTK
- Spacy & Gensim
- OpenCV
- Tensorflow
- Keras
- Representation
- Data Cleaning
- Data Preprocessing
- Similarity
- Image
- Image Transformation
- Filters
- Noise Removal
- Correlation & Convolution
- Edge Detection
- Non Maximum Suppression & Hysterisis
- Fourier Domain
- Video Processing
- Image Feature
- Descriptors
- Detection & Classification
CNN
- Computer Vision
- Padding
- Convolution
- Pooling
- Why Convolution
- Case Studies
- Classic Networks
- Inception
- Open Source Implementation
- Transfer Learning
- Object Localization
- Landmark Detection
- Object Detection
- Bounding Box Prediction
- Yolo
- 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
- 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.
- Resource Information Analysis
- Text Cleaning of Customer reviews using NLP
- Image Manipulation (Loading, Rotation etc.)
- Sampling & Sampling Statistics
- Hypothesis Testing
- Calculus Problems
- Linear Algebra Problems
- Probability Problems
- Risk Evaluation
- Prediction of claim amount
- Emotor temp prediction
- User Behavioural Pattern
- 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
- Ecommerce product image classification
- Disease prediction based on images
- Vehicle identification(Object Detection)
- Animal Classification(Object Classification)
- Spatial Image classification (Image segmentation)
- Face detection
- Face recognition (Attendance using facial recognition)
- 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
- Machine Translation
- New Image generation based on existing images
- 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