Data Science (Artificial Intelligence – ML & DL)

39,000.00

This Data Science course curriculum is an intensive application oriented, real-world scenario based program using Artificial Intelligence, Machine Learning & Deep Learning. This course is a 200 hours program, intensive skill oriented, practical training program required for building AI based models. It is designed to give the participant enough exposure to the variety of applications that can be built using techniques covered under this program. This course is designed for the experienced professionals from variety of IT backgrounds. No prior knowledge of statistics or modeling is assumed.

Course Overview

Overview:

This Data Science course curriculum is an intensive application oriented, real-world scenario based program using Artificial Intelligence, Machine Learning & Deep Learning. This course is a 200 hours program, intensive skill oriented, practical training program required for building AI based models. It is designed to give the participant enough exposure to the variety of applications that can be built using techniques covered under this program. This course is designed for the experienced professionals from variety of IT backgrounds. No prior knowledge of statistics or modeling is assumed.

Prerequisites:

This curriculum of Data Science(AI-ML&DL) course has been designed for all levels, regardless of your prior knowledge of analytics, statistics, or coding. Familiarity with mathematics is helpful for this course.

Target Audience:

This course is ideal for anyone who wishes to learn the details of data science and pursue a career in this growing field of Artificial Intelligence, Machine Learning & Deep Learning.

 

Key Learning Outcomes:

When you complete this Introduction to Data Analytics course, you will be able to accomplish the following:

  • Stay Industry relevant and grow in your career.
  • Create AI/ML solutions for various business problems.
  • Understand how to apply Data Science practices in real-world scenarios
  • Define effective objectives for Data Science projects
  • Work with different types of data.
  • Build and deploy production grade AI/ML applications.
  • Apply AI/ML methods, techniques and tools immediate.

Delivery Mode:

  • Online Live Instructor led learning.

Syllabus

Module 01 – Data Science, Data Analytics, Artificial Intelligence, ML, & DL

  • Introduction
  • The Data Science Domain
  • Data Science, Data Analytics, and Machine Learning, Deep Learning, AI – Overlaps
  • Data Science Demystified
  • Data Science and Business Strategy
  • Successful Companies Using Data Science
  • Travel Industry
  • Retail
  • E-commerce and Crime Agencies
  • Analytical Platforms Across Industries
  • Key Takeaways.

Module 02 – Data Science & AI in Different Sectors

  • Introduction
  • AI & ML for Products or Services
  • How Google Uses Artificial Intelligence and Data Science
  • How LinkedIn Uses Artificial Intelligence and Data Science
  • How Amazon Uses Artificial Intelligence and Data Science
  • Netflix: Using Artificial Intelligence and Data Science to Drive Engagement
  • Media and Entertainment Industry
  • Education Industry
  • Healthcare Industry
  • Government
  • Weather Forecasting
  • Key Takeaways

Module 03 – Machine Learning-Introduction

  • Introduction of Machine Learning
  • Evolution of Machine Learning
  • Application of Machine Learning

Module 04 – Probability and Stats

  • Central tendency theorem
  • Probability
  • Bayes Theorem
  • Types of distribution

 

Module 05 – Machine Learning-Fundamental

  • Difference between Traditional Programming and ML Programming
  • Requirements for Machine Learning Practical Implementation
  • Required software and tools for Machine Learning implementations
  • Setup Anaconda
  • Installation of PyCharm or Spyder or Jupyter
  • Configure PyCharm/Spyder/Jupyter with Anaconda

Module 06 – Python Programming Foundation for Data Science

  • Introduction
  • Variables
  • Data Types with Python
  • Assisted Practice: Data Types in Python
  • Keywords and Identifiers
  • Expressions
  • Basic Operators
  • Operators in Python
  • Functions
  • Search for a Specific Element from a Sorted List
  • Create a Banking System Using Functions
  • String Operations
  • String Operations in Python
  • Tuples
  • Tuples in Python
  • Lists
  • Lists in Python
  • Sets
  • Sets in Python
  • Dictionaries
  • Dictionary in Python
  • Dictionary and its Operations
  • Conditions and Branching
  • Check the Scores of a Course
  • While Loop
  • Find Even Digit Numbers
  • Fibonacci Series Using While Loop
  • For Loop
  • Calculate the Number of Letters and Digits
  • Create a Pyramid of Stars
  • Break and Continue Statements

Module 07 – File handling and Package handling using Python

  • Learning Objectives
  • File Handling
  • File Opening and Closing
  • Reading and Writing Files
  • Directories in File Handling
  • Assisted Practice: File Handling
  • Modules and Packages
  • Assisted Practice: Package Handling

Module 08 – Mathematical Computing using NumPy

  • Learning objectives
  • NumPy
  • Create and Print Numpy Arrays
  • Operations
  • Executing Basic Operations in Numpy Array
  • Performing Operations Using Numpy Array
  • Demonstrate the Use of Copy and Use
  • Manipulate the Shape of an Array

 

 

Module 09 – Data Manipulation with Pandas

  • Learning Objectives
  • Introduction to Pandas
  • Data Structures
  • Create Pandas Series
  • DataFrame
  • Create Pandas DataFrames
  • Missing Values
  • Handle Missing Values
  • Various Data Operations
  • Data Operations in Pandas DataFrame

Module 10 – Data visualization with Python

  • Learning objectives
  • Data Visualization
  • Considerations of Data Visualization
  • Factors of Data Visualization
  • Python Libraries
  • Create Your First Plot Using Matplotlib
  • Line Properties
  • Create a Line Plot for Football Analytics
  • Multiple Plots and Subplots
  • Create a Plot with Annotation
  • Create Multiple Plots to Analyze the Skills of the Players
  • Create Multiple Subplots Using plt.subplots
  • Types of plots
  • Create a Stacked Histogram
  • Create a Scatter Plot of Pretest scores and Posttest Scores
  • Create a Pie Chart
  • Create a Bar Chart
  • Create Box Plots
  • Create a Waffle Chart
  • Analyzing Variables Individually
  • Key Takeaways

Module 11 – Steps of Machine Learning Implementations

  • Types of Machine Learning
  • Labelled Data and Unlabeled Data
  • Concept of Supervised Machine Learning
  • Concept of Unsupervised Machine Learning
  • Steps of Machine Learning
  • Concept of Collecting the historic training Data for ML
  • Concept of Preprocess data for Machine Learning
  • Concept of Train the ML model
  • Concept of Test the ML Algorithm
  • Concept of using the ML Algorithm

Module 12 – Data Collection for Machine Learning

  • Introduction
  • Types of Data collection- Offline Data and Online Data
  • Practical implementations of Reading the offline dataset using Numpy
  • Practical implementations of Reading the online dataset using Numpy
  • Practical implementations of Reading the offline dataset using Pandas
  • Practical implementations of Reading the online iris dataset using Pandas

Module 13 – Concept of Supervise & Unsupervised Machine Learning

  • Introduction
  • Types of Machine Learning
  • Labelled Data and Unlabeled Data
  • Concept of Supervised Machine Learning
  • Concept of Unsupervised Machine Learning
  • Regression and Classification
  • Linear Regression and Logistic Regression

Module 14 – Data Plotting for Machine Learning

  • Introduction
  • Concept of Univariate plots
  • Univariate Histogram Plots.
  • Univariate Density Plots.
  • Univariate Box and Whisker Plots.
  • Concept of Multivariate plots
  • Correlation Matrix Plot
  • Scatter Matrix Plot

Module 15 – Practical implementation of Supervised ML Algorithm

  • Introduction
  • Implementation Foundation of Supervised Machine Learning Algorithms
  • Regression and Classification
  • Linear Regression and Logistic Regression
  • Practical implementations of Supervised ML Algorithms- Linear Regression
  • Practical implementations of Supervised ML Algorithms- Logistic Regression
  • Concept of Sigmoid Function
  • k-NN Algorithm
  • Naive Bayes Classifiers
  • Decision trees and etc.
  • Support vector machines
  • Introduction to concepts of forecasting
  • Forecasting: ARIMA
  • Forecasting: Hotlz Winters
  • Dimension Reduction
  • Ensemble learning
  • Bagging (Random forest)
  • Gradient boosting

Module 16 – Practical implementation of Unsupervised Machine Learning

  • Introduction
  • Concepts and Steps of Unsupervised Machine Learning Algorithm
  • Concept of Clustering,
  • Practical implementations of Machine Learning Unsupervised Algorithms
  • K-Means Clustering.

Module 17 – Prepare Data for ML using Data Transformation Methods

  • Introduction
  • Need for Data Pre-processing
  • Data Transforms Steps
  • Types of Data Transformation Methods
  • Rescale Data
  • Standardize Data
  • Normalize Data
  • Binarize Data

Module 18 – Feature Selection for Machine Learning

  • Introduction
  • Feature Selection
  • Univariate Feature Selection
  • Recursive Feature Elimination
  • Principal Component Analysis
  • Feature Selection based on Importance

Module 19 – Data Resampling Methods for Evaluation of ML Models

  • Introduction
  • Evaluate Machine Learning Algorithms
  • Split into Train and Test Sets
  • K-fold Cross Validation
  • Leave One Out Cross Validation
  • Repeated Random Test-Train Splits
  • What Techniques to Use When

Module 20 – Machine Learning Algorithm Performance Evaluation Metrics

  • Introduction
  • Algorithm Evaluation Metrics
  • Logistic Regression Algorithm Performance Evaluation Metrics
  • Classification Accuracy (Default).
  • Logarithmic Loss.
  • Area Under ROC Curve (AUC).
  • Confusion Matrix.
  • Classification Report.
  • Linear Regression Algorithm Performance Evaluation Metrics
  • Mean Absolute Error.
  • Mean Squared Error.
  • R2 Error

Module 21 – Spot-Check Machine Learning Algorithms

  • Concept of Algorithm Spot-Checking
  • Algorithms Overview
  • Linear Machine Learning Algorithms Spot-check
  • Nonlinear Machine Learning Algorithms Spot-check

Module 22 – Save and Load Machine Learning Models

  • Introduction
  • Finalize Your Model with pickle
  • Finalize Your Model with Joblib

Module 23 – Introduction to Deep Learning

  • A revolution in Artificial Intelligence
  • Limitations of Machine Learning
  • What is Deep Learning?
  • Advantage of Deep Learning over Machine learning

Module 24 – Introduction to Neural Networks

  • How Deep Learning Works?
  • Introduction to Neural Networks
  • Neural Network Architecture
  • The Neuron
  • Training a Perceptron
  • Concept of Gradient Descent
  • Stochastic Gradient Descent (SDG)
  • Activation Functions
  • Neural Network Layers

Module 25 – Deep dive into ANN with Tensor Flow

  • Understand limitations of a Single Perceptron
  • Deepening the network
  • Tensor Flow code-basics
  • Tensor flow data types
  • CPU vs GPU vs TPU
  • Tensor flow methods
  • Overfitting and Regularization
  • Debugging Neural Networks
  • Visualizing NN using Tensor Flow
  • The MNIST Dataset
  • Coding MNIST NN
  • Linear Regression example revisited
  • Generalization, Overfitting, Under fitting

Module 26 – Keras API

  • Keras API
  • How to compose Models using Keras
  • Sequential Composition
  • Neural Network Layers with Keras & TensorFlow

Module 27 – Convolutional Neural Networks (CNN)

  • Introduction
  • Images and Pixels
  • How humans recognize images
  • Convolutional Neural Networks
  • ConvNet Architecture
  • Strides and Zero Padding
  • Max Pooling and ReLU activations
  • Dropout
  • Coding Deep ConvNets demo

Module 28 – Computer Vision (CV)

  • Introduction to image processing and computer vision
  • Convolutional features for visual recognition
  • Object detection
  • Object tracking and action recognition
  • Image segmentation and synthesis

Module 29 – Recurrent Neural Network (RNN)

  • Introduction of Recurrent Neural Network
  • Architecture of RNN
  • Applications of RNN

Module 30 – Natural Language Processing (NLP)

  • Introduction of NLP
  • Intro to text mining and applications
  • Basics of NLP (POS, entity recognition)
  • Applications of Regular expression
  • Sentiment Analysis
  • Topic Modelling
  • Clustering in Text documents

Module 31 – Tools for AI

  • Introduction to SQL on MySQL
  • Introduction to NoSQL on MongoDB
  • Introduction to Docker and Kubernetes
  • Data lake and centralization strategy
  • Using cloud specific services for AI solution
  • Creating and deploying API
  • Introduction to ELK Stack
  • Introduction to Spark and Distributed Computing

Module 32 – Deployment of AI Solutions

  • Unit and System testing
  • Model Lifecycle management
  • Integration with Dev Ops and relevant architectures
  • Retraining Pipeline
  • Deployment of AI on Cloud
  • AI Ops Best Practices

 

 

Module 33 – Mini-Project

  • Project Descriptions
  • Datasets
  • Coding & Implementations
  • Performance Evaluation

Prerequisite

This curriculum of Data Science(AI-ML&DL) course has been designed for all levels, regardless of your prior knowledge of analytics, statistics, or coding. Familiarity with mathematics is helpful for this course.

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