Support Vector Machines: Basics & Cost Function, Wide Margin Classifier
Support Vector Regression
Support Vector Classifier
Kernel SVM: Linear Kernel
Gaussian Kernel
Naive Bayes
KNN(K Nearest Neighbours)
Naive Bayes MNIST
Advanced Naive Bayes: Working with Text data and Text based Classification
Advanced Machine Learning Concepts
KFold Cross Validation
Advanced Machine Learning
Feature Selection & Feature Engineering
Model Persistance, Evaluation, Retraining
Ensemble Learning with Multiple ML Algorithms
Bagging to Improve Accuracy
Boosting to Improve Accuracy
Gradient Boosting
AdaBoost(Ensemble Learning): Weigthts
Upper Confidence Bound(UCB) & Thomson Sampling
ML As a Service(ML Web Service)
a. Binary Classifier as a Service
Unsupervised Machine Learning
Clustering
KMeans Clustering
Hierarchical Clustering: Agglomerative & Devisive
Dendo Grams, Hierarchical Trees
Dimensionality Reduction: Projections
Principal Component Analysis(PCA)
Kernel PCA
Supervised Dimensionality Reduction: LDANoSQL
Association Rule Mining
Apriori or Market Basket Analysis
NoSQL
Structured & Semistructured Data
MongoDB for Document Store DB
NoSQL Databases Role in Machine Learning: MongoDB
Deep Learning
Advanced Machine Learning
Neural Networks Intro
Artificial Neural Networks(ANN)
Deep Neural Networks
Convolutional Neural Networks(CNN)
Recurrent Neural Networks(RNN)
Stock Price Prediction using Neural Networks: Demo
Neural Net Concepts
Neurons as Nodes: Perceptrons
Dense & Sparse Neural Networks
Neuron Based approach: Benefits
Perceptrons
Learning Weights
Gradient Descent & Back Propagation
Activation Function & Feedforward Neural Networks
Installing Prerequisite Softwares
Tensorflow
Theano
Keras
3 Layer Neural Network for Customer Churn Modeling
Online Learning(Reinforcement Learning)
Generative Adversarial Networks (GANs)
PyTorch
Image Processing Introduction
OpenCV for Image Processing in Python
Edge Detection
Eye & Nose Detection
Face Detection using Haar cascades
Optical Character Recognition using Neural Networks
Text Detection: Sliding Window
Character Segmentation
Character Classification
Synthetic Character Generation: Shearing & Scaling, Rotation
Revisiting Perceptrons
Coding a Text Classifier in Neural Networks
Advanced Neural Nets
Long Short Term Memory(LSTM) in RNN
Time Series Data(ARMA, ARIMA)
Unsupervised Learning using Hidden Markov Model(Tensorflow and Theano)
Tensorflow Deep Dive
Speech Recognition
Advanced Text Mining
Building & Deploying a Intelligent Chatbot
Data Preprocessing
Seq2Seq
Deploying the Chat Application
Computer Vision as AI
Image Recoginition and Classification
Deep Neural Networks Architecture revisited
Deep Convolutional Neural Network for Image Recognition
Convolutions
Pooling, Flattening
LeNet, Fully Connectected Feed Forward Network
Face Recognition using Convolutional Neural Network
Importing Pretrained Models
Running Convolutional Neural Networks on GPU for Image
Unsupervised Learning in Deep Neural Networks Revisited
Current Advancements:
Self Organizing Maps(SOM)
Auto Encoders
Boltzman Machines
VGG, SDD, ResNet
Future Direction: Self Driving Cars, IIoT with AI, Drone based Parcel Delivery, etc
Conclusion
Course Outline
Advanced Python Programming Language is covered in Depth
Machine Learning & Data Visualization are covered in Python using language and also using Very advanced ML packages like Tensorflow, Theano and Keras
Data Visualization is covered with Python
All Machine Learning algorithms are covered in Depth, several algorithms are covered with real world solutions. Neural Networks and Deep Neural Nets are covered with real world examples
Knowledge about data, features & distribution, model building, accuracy are clearly covered
Basics to Advanced statistics are covered
Very advanced practical applications like Recommendation Engine, Threat Detection on Python
Benefits of our Courses
Exposure to advanced Data science concepts through experienced and senior Solution Architect
Presentations with Live Examples and real project scenarios, Business use cases
Optionally the students can work in our application(Real time project) based on their interest
Internships will be offered to high performing students
