#1 Data Science Course, 100%MNC Job upto 60K, Free R, PowerBI, Alteryx, “SLA Training Institute”

Factors

• Using Factors
• Manipulating Factors
• Numeric Factors
• Creating Factors from Continuous Variables
• Convert the variables in factors or in others.

Reshaping

• Data Modifying
• Data Frame Variables
• Recoding Variables
• The recode Function
• Reshaping Data Frames
• The reshape Package

NOTE:-

• Assignments with Datasets

Explore many algorithms and models:

• Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
• Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests. Get ready to do more learning than your machine!

Module 2.6- Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning

• Machine Learning Languages, Types, and Examples
• Machine Learning vs Statistical Modelling
• Supervised vs Unsupervised Learning
• Supervised Learning Classification
• Unsupervised Learning

Module 2.7 – Supervised Learning I

• K-Nearest Neighbors
• Decision Trees
• Random Forests
• Reliability of Random Forests
• Advantages & Disadvantages of Decision Trees

Module 2.8 – Supervised Learning II

• Regression Algorithms
• Model Evaluation
• Model Evaluation: Overfitting&Underfitting
• Understanding Different Evaluation Models

Module 2.9 – Unsupervised Learning

• K-Means Clustering plus Advantages & Disadvantages
• Hierarchical Clustering plus Advantages & Disadvantages
• Measuring the Distances Between Clusters – Single Linkage Clustering
• Measuring the Distances Between Clusters – Algorithms for Hierarchy Clustering
• Density-Based Clustering

Module 2.10 – Dimensionality Reduction & Collaborative Filtering

• Dimensionality Reduction: Feature Extraction & Selection
• Collaborative Filtering & Its Challenges

Module 2.11 – Classification

• An Overview of Classification.
• Why Not Linear Regression?
• Logistic Regression
• The Logistic Model
• Estimating the Regression Coefficients
• Making Predictions
• Logistic Regression for 2 Response Classes
• Lab: Logistic Regression.
• The Stock Market Data
• Logistic Regression

NOTE

• Assignments with Different Datasets
• Business Scenerio/Group Discussion

Module 2.12 – Tree-Based Methods

• The Basics of Decision Trees
• Regression Trees
• Classification Trees
• Trees Versus Linear Models
• Advantages and Disadvantages of Trees
• Bagging, Random Forests, Boosting
• Bagging
• Random Forests
• Lab: Decision Trees
• Fitting Classification Trees
• Fitting Regression Trees

NOTE-:

• Assignments with Different Datasets.
• Business Scenerio/Group Discussion.

Module 2.13 – Time Series &Forecasting:

• Time series
• Estimating and Eliminating the Deterministic Components if they are present in the Model.
• Estimating and Eliminating Seasonality if it is present in the Model
• Modeling the Remainder using Auto Regressive Moving Average (ARMA) Models
• Identify ‘order’ of the ARMA model
• Forecast’ or Predict for Future Values
• Practise on R

Module 2.14- Support Vector Machines – Outline

• Understand when the Support Vector family of methods are an appropriate method of analysis.
• Understand what a hyperplane is and how they are used with the Support Vector methods.
• Identify the differences between Maximal Margin Classifiers, Support Vector Classifiers, and Support
• Vector Machines.
• Know how each of the algorithms determines the best separating hyperplane.
• Distinguish between hard and soft margins and when each is to be used.
• Know how to extend the method for nonlinear cases.

Module 2.15 – Principal Component Analysis

• Understand what principal components are and when principal component analysis is appropriate.
• Describe eigenvalues and eigenvectors and how they are used to calculate principal components.
• Understand loading and loading vectors.
• Know how to decide how many principal components to use in the analysis.
• Be able to use principal component analysis for regression.

Mathematics

• What is a matrix?
• Scalars and Vectors
• Linear Algebra and Geometry
• Arrays in Python – A Convenient Way To Represent Matrices
• What is a Tensor?
• Addition and Subtraction of Matrices
• Errors when Adding Matrices
• Transpose of a Matrix
• Dot Product
• Dot Product of Matrices
• Why is Linear Algebra Useful?

Deep Learning

• What to Expect from this Part?
• What is Machine Learning

Deep Learning – Introduction to Neural Networks

• Introduction to Neural Networks
• Training the Model
• Types of Machine Learning
• The Linear Model (Linear Algebraic Version)
• The Linear Model
• The Linear Model with Multiple Inputs
• The Linear model with Multiple Inputs and Multiple Outputs
• Graphical Representation of Simple Neural Networks
• What is the Objective Function?
• Common Objective Functions: L2-norm Loss
• Common Objective Functions: Cross-Entropy Loss
• Optimization Algorithm: 1-Parameter Gradient Descent
• Optimization Algorithm: n-Parameter Gradient Descent

Deep Learning – How to Build a Neural Network from Scratch with NumPy

Deep Learning – TensorFlow: Introduction

• How to Install TensorFlow
• A Note on Installing Packages in Anaconda
• TensorFlow Outline and Logic
• Actual Introduction to TensorFlow
• Types of File Formats, supporting Tensors

Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networks

• What is a Layer?
• What is a Deep Net?
• Digging into a Deep Net
• Non-Linearities and their Purpose
• Activation Functions
• Activation Functions: Softmax Activation
• Backpropagation
• Backpropagation picture
• Backpropagation – A Peek into the Mathematics of Optimization

Deep Learning – Overfitting

• What is Overfitting?
• Underfitting and Overfitting for Classification
• What is Validation?
• Training, Validation, and Test Datasets
• N-Fold Cross Validation
• Early Stopping or When to Stop Training

Deep Learning – Initialization

• What is Initialization?
• Types of Simple Initializations
• State-of-the-Art Method – (Xavier) Glorot Initialization

Deep Learning – Digging into Gradient Descent and Learning Rate Schedules

• Stochastic Gradient Descent
• Problems with Gradient Descent
• Momentum
• Learning Rate Schedules, or How to Choose the Optimal Learning Rate
• Learning Rate Schedules Visualized
• Adaptive Learning Rate Schedules (AdaGrad and RMSprop )
• Adam (Adaptive Moment Estimation)

Deep Learning – Preprocessing

• Preprocessing Introduction
• Types of Basic Preprocessing
• Standardization
• Preprocessing Categorical Data
• Binary and One-Hot Encoding

Deep Learning – Classifying on the MNIST Dataset

• MNIST: What is the MNIST Dataset?
• MNIST: How to Tackle the MNIST
• MNIST: Relevant Packages
• MNIST: Model Outline
• MNIST: Loss and Optimization Algorithm
• Calculating the Accuracy of the Model
• MNIST: Batching and Early Stopping
• MNIST: Learning
• MNIST: Results and Testing

Deep Learning – Business Case Example

• Business Case: Getting acquainted with the dataset
• Business Case: Outlining the Solution
• The Importance of Working with a Balanced Dataset
• Business Case: Preprocessing
• Business Case: Preprocessing Exercise
• Creating a Data Provider
• Business Case: Model Outline
• Business Case: Optimization
• Business Case: Interpretation
• Business Case: Testing the Model
• Business Case: A Comment on the Homework
• Business Case: Final Exercise

Deep Learning – Conclusion

• Summary on What You’ve Learned
• What’s Further out there in terms of Machine Learning
• An overview of CNNs
• DeepMind and Deep Learning
• An Overview of RNNs
• An Overview of non-NN Approaches
• Download All Resources

A Quick Introduction

• What is MSPBI and its scope
• Learn the common work flow in MSPBI
• Building blocks of MSPBI and its relations
• Quick demo how to create a business dashboard in MSPBI
• MSPBI components
• Old vs. new technologies
• Power BI Desktop/Service/Mobile

Getting Business Data

• Get data in shape for use with MSPBI
• Combining two or more data sets (source data) for reporting
• Tackling messy data in MSPBI
• Clean and transform data

Modelling in Power BI

• How to connect many different data sources
• Manage data source (database) relationships
• Unique keys
• Calculated columns and more
• Custom calculations to evaluate time-based functions
• Build calculated tables based on DAX formulas and expressions
• Creating and viewing visuals easier with optimized models
• Discover hierarchal drill-down tools for date fields

Data Visualization

• Create and customize visualization in MSPBI and its power
• Use combination charts
• Create and format slicers with it
• Map visualizations and its utilization
• Use tables and matrixes
• Long live bubbles
• scatter charts in action
• Advanced funnel and waterfall charts
• Drive fast dashboard insights with gauges and numbers
• Color your visualization world with colors
• shapes and scales
• Adding personal touch
• logo etc. to reports and dashboards
• Display and present your dashboard in a way you want with summarize data
• Control how your report elements overlap with each other
• Learn to drill into hierarchies
• Manage how levels are shared (Z-order in reports)
• How to use R visuals in MSPBI

Data Exploring and Sharing

• Quick insights in Power BI Service
• Create and configure a dashboard
• Share dashboard with your organization
• Display and edit visuals- tiles
• full screen
• Get more space on your dashboard
• Install and configure a personal gateway
• Excel and MSPBI
• Import and excel table into Power BI
• Import excel files with data models and power view sheets
• Connect One Drive for business to MSPBI
• Excel data in Power BI summary

DAX (Data Analysis Expression) Application

• DAX and its basic building blocks
• Create calculated columns (fields) and measures in MSPBI using DAX formulas
• Breadth of functions available in DAX
• User variables in DAX
• Create expressions across multiple tables with relational functions;
• Filter and evaluate tables using advanced table functions