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Machine Learning and Artificial Intelligence

  /    /  Machine Learning and Artificial Intelligence

Machine Learning and Artificial Intelligence

Artificial Intelligence

Machine Learning and Artificial Intelligence are creating a huge buzz worldwide. The plethora of applications in Artificial Intelligence has changed the face of technology. These terms Machine Learning and Artificial Intelligence are often used interchangeably. However, there is a stark difference between the two that is still unknown to the industry professionals.

Course Overview

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.

The intended audience for this course:

  • Data Engineers
  • Data Scientists
  • Machine Learning Engineers
  • Integration Engineers
  • Architects
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
  • 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
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

Participants should preferably have some hands-on experience on Python

Course Information


5 Days

Mode of Delivery

Instructor led/Virtual


Beginner to Intermediate

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