Duration
2 days
Outline
Understanding the Big Picture
- Artificial Intelligence (AI) Overview
- What is Machine Learning (ML)?
- AI vs ML vs Data Science
- Relationship between Deep Learning (DL) and Machine Learning
- Practical Use cases
- Concepts and Terms
- Tools/Platforms for ML, DL and AI
- Machine Learning Project End to End Pipeline
- Scalable ML/AI: Big Data and Cloud fits into the Ecosystem
Machine Learning using Scikit Learn
- Types of Machine Learning
- Key Algorithms in Machine Learning
- Practical Applications of Machine Learning
- Various frameworks/Libraries popular for ML
- Concepts and Terms
- Why Scikit Learn?
Supervised Machine Learning
- Key Classification Algorithms
- Naïve Bayes Classifier
- Confusion Matrix
- Accuracy
- Key Regression Algorithms
- Linear, Logistic Regressions
- Gradient Descent
- Loss function
- Bias vs Variance Tradeoff
- Evaluating Models
Un-Supervised Machine Learning
- Why Un-supervised learning is important?
- Where it can be applied?
- Principal Component Analysis
- Performing Clustering of data
Introduction to Deep Learning
- What is Deep Learning?
- Relationship between Deep Learning and Machine Learning
- Deep Learning Use cases
- Concepts and Terms
- How to implement Deep Learning?
- Various Libraries, Pros & Cons
Artificial Neural Network
- Introduction to Neural Networks
- Introduction to Perceptron
- Neural Network Activation Functions
- Basic Neural Nets
- Concepts
TensorFlow API
- What is TensorFlow?
- Installing TensorFlow
- TensorFlow Graphs
- Variables and Placeholders
- Activation Functions
Convolutional Neural Networks (CNN)
- CNN History
- Understanding CNNs
- CNN Application
Conversational AI
- Intro to ChatBots
- Key options available
- Building ChatBot
- Hands-on Exercise using DialogFlow AP
Programme scope, delivery arrangements and suitability are confirmed during enquiry.