Introduction to Power BI

• Overview of BI concepts

• Why we need BI

• Introduction to SSBI

• SSBI Tools

• Why Power BI

• What is Power BI

• Building Blocks of Power BI

• Getting started with Power BI Desktop

• Get Power BI Tools

• Introduction to Tools and Terminology

• Dashboard in Minutes

• Interacting with your Dashboards

• Sharing Dashboards and Reports

Power BI Desktop

• Power BI Desktop

• Extracting data from various sources

• Workspaces in Power BI

Power BI Data Transformation

• Data Transformation

• Query Editor

• Connecting Power BI Desktop to our Data Sources

• Editing Rows

• Understanding Append Queries

• Editing Columns

• Replacing Values

• Formatting Data

• Pivoting and Unpivoting Columns

• Splitting Columns

• Creating a New Group for our Queries

• Introducing the Star Schema

• Duplicating and Referencing Queries

• Creating the Dimension Tables

• Entering Data Manually

• Merging Queries

• Finishing the Dimension Table

• Introducing the another DimensionTable

• Creating an Index Column

• Duplicating Columns and Extracting Information

• Creating Conditional Columns

• Creating the FACT Table

• Performing Basic Mathematical Operations

• Improving Performance and Loading Data into the Data Model

Modelling with Power BI

• Introduction to Modelling

• Modelling Data

• Manage Data Relationship

• Optimize Data Models

• Cardinality and Cross Filtering

• Default Summarization & Sort by

• Creating Calculated Columns

• Creating Measures & Quick Measures

Data Analysis Expressions (DAX)

• What is DAX

• Data Types in DAX

• Calculation Types

• Syntax, Functions, Context Options

• DAX Functions

• Date and Time

• Time Intelligence

• Information

• Logical

• Mathematical

• Statistical

• Text and Aggregate

• Measures in DAX

• Measures and Calculated Columns

• ROW Context and Filter Context in DAX

• Operators in DAX - Real-time Usage

• Quick Measures in DAX - Auto validations

• In-Memory Processing DAX Performance

Power BI Desktop Visualisations

• How to use Visual in Power BI

• What Are Custom Visuals

• Creating Visualisations and Colour Formatting

• Setting Sort Order

• Scatter & Bubble Charts & Play Axis

• Tooltips and Slicers, Timeline Slicers & Sync Slicers

• Cross Filtering and Highlighting

• Visual, Page and Report Level Filters

• Drill Down/Up

• Hierarchies and Reference/Constant Lines

• Tables, Matrices & Conditional Formatting

• KPI's, Cards & Gauges

• Map Visualizations

• Custom Visuals

• Managing and Arranging

• Drill through and Custom Report Themes

• Grouping and Binning and Selection Pane, Bookmarks & Buttons

• Data Binding and Power BI Report Server

Introduction to Power BI Dashboard and Data Insights

• Why Dashboard and Dashboard vs Reports

• Creating Dashboards

• Conguring a Dashboard Dashboard Tiles, Pinning Tiles

• Power BI Q&A

• Quick Insights in Power BI

Direct Connectivity

• Custom Data Gateways

• Exploring live connections to data with Power BI

• Connecting directly to SQL Server

• Connectivity with CSV & Text Files

• Excel with Power BI Connect Excel to Power BI, Power BI Publisher for Excel

• Content packs

• Update content packs

Publishing and Sharing

• Introduction and Sharing Options Overview

• Publish from Power BI Desktop and Publish to Web

• Share Dashboard with Power BI Service

• Workspaces (Power BI Pro) and Content Packs (Power BI Pro)

• Print or Save as PDF and Row Level Security (Power BI Pro)

• Export Data from a Visualization

• Export to PowerPoint and Sharing Options Summary

Refreshing Datasets

• Understanding Data Refresh

• Personal Gateway (Power BI Pro and 64-bit Windows)

Introduction to Machine Learning

• What is Machine Learning

• Machine Learning Use-Cases

• Machine Learning Process Flow

• Machine Learning Categories

Supervised Learning

• Classification and Regression

• Where we use classification model and where we use regression

• Regression Algorithms and its types

Regression Algorithm

• Logistic Regression

• Evaluation Matrix of Regression Algorithm

Classification Algorithm

• Implementing KNN

• Implementing Naïve Bayes Classifier

• Implementation and Introduction to Decision Tree using CARTand ID3

• Introduction to Ensemble Learning

• Random Forest algorithm using bagging and boosting

• Evaluation Matrix of classification algorithms (confusion matrix, r2score, Accuracy,f1-score,recall and precision

Optimization Algorithm

• Hyperparameter Optimization

• Grid Search vs. Random Search

Dimensionality Reduction

• Introduction to Dimensionality

• Why Dimensionality Reduction

• PCA

• Factor Analysis

• Scaling dimensional model

• LDA

• ICA

Unsupervised Learning

• What is Clustering & its Use Cases

• What is K-means Clustering

• How does the K-means algorithm works

• How to do optimal clustering

• What is Hierarchical Clustering

• How does Hierarchical Clustering work

Association Rules Mining and Recommendation Systems

• What are Association Rules

• Association Rule Parameters

• Calculating Association Rule Parameters

• Recommendation Engines

• How do Recommendation Engines work

• Collaborative Filtering

• Content-Based Filtering

• Association Algorithms

• Implementation of Apriori Association Algorithm

Reinforcement Learning

• What is Reinforcement Learning

• Why Reinforcement Learning

• Elements of Reinforcement Learning

• Exploration vs. Exploitation dilemma

• Epsilon Greedy Algorithm

• Markov Decision Process (MDP)

• Q values and V values

• Q – Learning

• Values

Time Series Analysis

• What is Time Series Analysis

• Importance of TSA

• Components of TSA

Model Selection and Boosting

• What is Model Selection

• Need for Model Selection

• Cross Validation

• What is Boosting

• How do Boosting Algorithms work

• Types of Boosting Algorithms

Introduction to Text Mining and NLP

• Overview of Text Mining

• Need of Text Mining

• Natural Language Processing (NLP) in Text Mining

• Applications of Text Mining

• OS Module

• Reading, Writing to text and word files

• Setting the NLTK Environment

• Accessing the NLTK Corpora

Extracting, Cleaning and Preprocessing Text

• Tokenization

• Frequency Distribution

• Different Types of Tokenizers

• Bigrams, Trigrams & Ngrams

• Stemming

• Lemmatization

• Stopwords

• POS Tagging

• Named Entity Recognition

Analyzing Sentence Structure

• Syntax Trees

• Chunking

• Chinking

• Context Free Grammars (CFG)

• Automating Text Paraphrasing

Text Classification - I

• Machine Learning: Brush Up

• Bag of Words

• Count Vectorizer

• Term Frequency (TF)

• Inverse Document Frequency (IDF)

Getting Started with TensorFlow 2.0

• Introduction to TensorFlow 2.x

• Installing TensorFlow 2.x

• Defining Sequence model layers

• Activation Function

• Layer Types

• Model Compilation

• Model Optimizer

• Model Loss Function

• Model Training

• Digit Classification using Simple Neural Network in TensorFlow 2.x

• Improving the model

• Adding Hidden Layer

• Adding Dropout

• Using Adam Optimizer

Introduction to Deep Learning

• What is Deep Learning

• Curse of Dimensionality

• Machine Learning vs. Deep Learning

• Use cases of Deep Learning

• Human Brain vs. Neural Network

• What is Perceptron

• Learning Rate

• Epoch

• Batch Size

• Activation Function

• Single Layer Perceptron

Neural Networks

• What is NN

• Types of NN

• Creation of simple neural network using tensorflow

Convolution Neural Network

• Image Classification Example

• What is Convolution

• Convolutional Layer Network

• Convolutional Layer

• Filtering

• ReLU Layer

• Pooling

• Data Flattening

• Fully Connected Layer

• Predicting a cat or a dog

• Saving and Loading a Model

• Face Detection using OpenCV

Image Processing and Computer Vision

• Introduction to Vision

• Importance of Image Processing

• Image Processing Challenges – Interclass Variation, ViewPoint Variation, Illumination, Background Clutter, Occlusion & Number of Large Categories

• Introduction to Image – Image Transformation, Image Processing Operations & Simple Point Operations

• Noise Reduction – Moving Average & 2D Moving Average

• Image Filtering – Linear & Gaussian Filtering

• Disadvantage of Correlation Filter

• Introduction to Convolution

• Boundary Effects – Zero, Wrap, Clamp & Mirror

• Image Sharpening

• Template Matching

• Edge Detection – Image filtering, Origin of Edges, Edges in images as Functions, Sobel Edge Detector

• Effect of Noise

• Laplacian Filter

• Smoothing with Gaussian

• LOG Filter – Blob Detection

• Noise – Reduction using Salt & Pepper Noise using Gaussian Filter

• Nonlinear Filters

• Bilateral Filters

• Canny Edge Detector - Non Maximum Suppression, Hysteresis Thresholding

• Image Sampling & Interpolation – Image Sub Sampling, Image Aliasing, Nyquist Limit, Wagon Wheel Effect, Down Sampling with Gaussian Filter, Image Pyramid, Image Up Sampling

• Image Interpolation – Nearest Neighbour Interpolation, Linear Interpolation, Bilinear Interpolation & Cubic Interpolation

• Introduction to the dnn module
• Deep Learning Deployment Toolkit
• Use of DLDT with OpenCV4.0

• OpenVINO Toolkit
• Introduction
• Model Optimization of pre-trained models
• Inference Engine and Deployment process

Introduction to Cloud Computing

• In this module, you will learn what Cloud Computing is and what are the different models of Cloud Computing along with the key differentiators of different models. We will also introduce you to virtual world of AWS along with AWS key vocabulary, services and concepts.
• A Short history
• Client Server Computing Concepts
• Challenges with Distributed Computing
• Introduction to Cloud Computing
• Why Cloud Computing
• Benefits of Cloud Computing