AI / ML

Description

Advanced Artificial Intelligence and Machine Learning Program – Advanced AIML

Duration : 120 Hours (Including the Python)

• Bigdata
• Python for Artificial Intelligence
• Visualization Tools
• Machine Learning
• Cloud for AIML
• NLP – Natural Language Processing
• Computer Vision
• Speech Recognition
• Neural Networks
• Tensorflow 2.0 With KERAS
• Deep Learning
• Tensorflow for Mobile

 

• Bigdata
  1. Bigdata Spark Ecosystem
  2. Bigdata Pipeline and Components

• Data Ingestion

1. Bigdata Automation
2. Bigdata Data workflow to Artificial Intelligence
3. Use Case – Apache Flume, Apache ZooKeeper, Apache Spark, Apache Kafka

• Machine Learning
  1. Statistical Methodologies
  2. Mathematical Mythologies

• Supervised and Unsupervised Learning
  1. Supervised Learning Algorithms
     1. Linear Regression
     2. Multi Linear Regression
     3. KNN
     4. Logistic regression
     5. Linear Classifier
     6. Decision Tree
     7. Support Vector Machine
     8. Random Forest
     9. Naïve Bayes
  2. Unsupervised Learning
     1. Clustering
        1. Hierarchical
        2. K-Means
        3. DBSCAN

 

1. Anomaly Detection
   1. Local Outlier Factor
2. Dimensionality Reduction
   1. PCA – Principal Component Analysis

• Cloud Platforms for AIML
  1. Microsoft Azure
  2. IBM Watson

• Amazon

1. Google

• Visualization tools
  1. MicroStrategy
     1. Python data Dashboard
     2. Machine learning data Dashboard
  2. IBM Cognos Analytics
     1. Predictive Analytics Dashboard

• Tableau
  1. Visualization Dashboard

1. Qlikview
   1. Visualization Dashboard
2. Microsoft PowerBI
   1. Cloud Dashboard

• Natural Language Processing – NLP
  1. NLP toolkits
  2. IDF

• Google Word2Vec

1. POS
2. Stopwords
3. Tokenization

• Stemming
• Sentiment Analysis

1. NLP Predictive Analytics
2. Chatbots
3. Cloud Chatbots

• Computer Vision
  1. Image Processing Tools
     1. Pillow
        1. Image Processing Concepts
        2. Image processing Filters
     2. OpenCV
        1. Image Processing filters
        2. Image Transformation
        3. Image Conversion
        4. Image for Videos
        5. Feature Extraction

• Speech recognition
  1. Speech to text Concepts
  2. Hidden Markov Model

• SpeechRecognition Package

1. Working with different speech modulations

 

• Tensorflow 2.0
  1. Tensorflow Fundamentals
  2. Keras Fundamentals

• CUDA

1. GPU
2. Low level Tensorflow
3. HighlevelTensorflow

• Neural Networks
  1. Image Classification
  2. Image Detection

• Classification Vs Detection

1. Perceptron
2. Multi-layer Perceptron
3. Forward propagation

• Backward propagation
• Activation Functions

1. Gradient Descent
2. Optimization Techniques
   1. Adam
   2. Adaboost

• Momentum

1. Deep Belief Networks

• Deep Learning
  1. Popular Neural Network Architectures
     1. Filter
     2. Kernel

• Pooling

1. Optimization
2. Regularization
3. Generalization

1. Conventional Neural Networks

• Resnets

1. Inception
2. Recurrent Neural Network – RNN
3. LSTM Long Short term memory

• Auto Encoders
• Siamese Networks

1. GRU
2. GAN – generative adversarial networks

• Tensorflow for Mobile
  1. Introduction
  2. Architecture

• Tensorflow Environment for mobile

Projects

• Sales data ingestion
• Machine Learning Projects
  1. Healthcare predictive Analysis
  2. Prediction of discounts Online Electronic Store
  3. Prediction of Insurance Claims
• NLP Project
  1. Sentiment Analytics
  2. Social Media Analytics
• Chatbot
• Computer Vision
  1. Image Segmentation prediction