Duration
5 days
Course Structure
This course focuses on the practical aspects of Machine Learning, Deep Learning, and Artificial Intelligence. The objective is to make use of TensorFlow for various types of neural networks. The participants will build and train deep learning models.
Intended Audience
The intended audience for this course:
- Data Engineers
- Data Scientists
- Machine Learning Engineers
- Integration Engineers
- Architects
Prerequisites and Lab details
Participants should preferably have some hands-on experience on Python.
Course Objectives
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
Environment for Experiments
- Installing Anaconda
- Setting up Jupyter Notebook
- Experiencing Notebooks
- Key Python Syntax Recap
- Hands-on Exercises
Key Statistics
- Summary Statistics
- Inferential Statistics
- Exploratory Analysis
- Distribution Modeling
- Numerical Computation using Python
- Hands-on Exercises
Data Visualization
Overview
- Using MatPlot Lib
- Key types of plots
- Exploratory Analysis using MatPlot Lob
- Hands-on Exercises
Acquiring & Preparing Data
- Content Acquisition Approaches, Pros & Cons
- Working with Beautiful Soup
- Acquiring data using Rest Based APIs
- Data Cleaning & Wrangling using Pandas
- Missing Values and Outlier
- Hands-on Exercises
Feature Engineering
- What is Feature Engineering?
- Why Feature Engineering?
- How to apply Feature Engineering?
- Discussions on various scenarios
- Hands-on Exercises
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?
- Code Walkthrough
- Hands-on Exercises
XGBoost using Scikit Learn
Introduction
- Classification with XGBoost
- Regression with XGBoost
- Hands-on Exercises
Supervised Machine Learning
- Key Classification Algorithms
- Naïve Bayes Classifier
- Confusion Matrix
- Accuracy
- Key Regression Algorithms
- Linear, Logistic and Other Key types of Regressions
- Decision trees
- Random forest
- Gradient Descent
- Loss function
- Bias vs Variance Tradeoff
- Evaluating Models
- Hands-on Exercises
Un-Supervised Machine Learning
- Principal Component Analysis
- Performing Clustering of data
- Using Collaborative filtering for Recommendations
- Hands-on Exercises
Spark Overview
- What is Spark?
- Why Spark?
- Data Abstraction - RDD
- Logical Architecture of Spark
- Programming Languages in Spark
- Functional Programming with Spark
- Hands-on Exercise
Data Processing using Spark
- Using the Spark Shell
- Resilient Distributed Datasets (RDDs)
- Functional Programming with Spark
- RDD Operations
- Key-Value Pair RDDs
- MapReduce and Pair RDD Operations
- Building and Running a Spark Application
- Spark SQL
- Dataframe API
- Hands-on Exercises
Caching and Checkpointing
- Dealing with RDD Infinite Lineages
- Caching Overview
- Distributed Persistence
- Checkpointing of an Iterative Machine Learning Algorithm
Spark Machine Learning using MLLib
- Spark ML vs Spark MLLib
- Data types and key terms
- Feature Extraction
- Linear Regression using Spark MLLib
- Hands-on Exercises
Spark Machine Learning using ML
- Spark ML Overview
- Transformers and Estimators
- Pipelines
- Implementing Decision Trees
- K-Means Clustering using Spark ML
- Hands-on Exercises
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?
Artificial Neural Network
- Introduction to Neural Networks
- Introduction to Perceptron
- Neural Network Activation Functions
- Basic Neural Nets
- Single Hidden Layer Model
- Single Hidden Layer Explained
- Multiple Hidden Layer Model
- Multiple Hidden Layer Results
TensorFlow API
- What is TensorFlow?
- Installing TensorFlow
- TensorFlow Graphs
- Variables and Placeholders
- Activation Functions
- Building Models
- Deploying Models on Google Cloud
- Monitoring Model through Tensorboard
- Hands-on Exercises
Keras API
- What is Keras?
- Why Keras?
- Keras Basics
- Working with Keras
- Hands-on Exercises
Convolutional Neural Networks (CNN)
- CNN History
- Understanding CNNs
- Various Layers like Pooling, Convolution, Relu etc.
- CNN Applications
- Hands-on Exercises
Conversational AI
- Intro to ChatBots
- Key options available
- Building ChatBot
- Hands-on Exercises
Programme scope, delivery arrangements and suitability are confirmed during enquiry.