Description: Learn about High-level overview of Data Science project management methodology, Statistical Analysis using examples, understand Statistics and Statistics 101. Also, learn about exploratory data analysis, data cleansing, data preparation, feature engineering.

Topics

• High-Level overview of Data Science / Machine Learning project management methodology
• Videos for Data Collection - Surveys  and Design of Experiments will be provided
• The various Data Types namely continuous, discrete, categorical, count, qualitative, quantitative and its identification and application. Further classification of data in terms of Nominal, Ordinal, Interval and Ratio types
• Random Variable and its definition
• Probability and Probability Distribution – Continuous probability distribution / Probability density function and Discrete probability distribution / Probability mass function

Description: Continue with the discussion on understanding Statistics, the various Moments of business decision and other Basic Statistics Concepts. Also, learn about some graphical techniques in Analytics.

Topics

• Balanced vs Imbalanced datasets
• Various sampling techniques for handling balanced vs imbalanced datasets
• Videos for handling imbalanced data will be provided
• What is Sampling Funnel, its application and its components
  • Population
  • Sampling frame
  • Simple random sampling
  • Sample
• Measure of central tendency
  • Mean / Average
  • Median
  • Mode
• Measure of Dispersion
  • Variance
  • Standard Deviation
  • Range
• Expected value of probability distribution

Description: Learn about the other moments of business decision as part of Statistical Analysis. Learn more about Visual data representation and graphical techniques. Learn about Python, R programming with respect to Data Science and Machine Learning. Understand how to work with different Python IDE and Python programming examples.

Topics

• Measure of Skewness
• Measure of Kurtosis
• Various graphical techniques to understand data
  • Bar plot
  • Histogram
  • Box plot
  • Scatter plot
• Introduction to R and RStudio  
• Installation of Python IDE
• Anaconda and Spyder
• Working with Python and R with some basic commands

Description: Learn about Normal Distribution and Standard Normal Distribution. Rules and Principles of Normal distribution. And how to check for normality by QQ normal distribution Plot.

Topics

• Normal Distribution
• Standard Normal Distribution / Z distribution
• Z scores and Z table
• QQ Plot / Quantile-Quantile plot

Description: Under this last topic on Basics of statistics, learn some higher statistical concepts and gain understanding on interval estimates.

Topics

• Sampling Variation
• Central Limit Theorem
• Sample size calculator
• T-distribution / Student's-t distribution
• Confidence interval
  • Population parameter - Standard deviation known
  • Population parameter - Standard deviation unknown

Introduction to R and Python basic stats

Description: Get introduced to Hypothesis testing, various Hypothesis testing Statistics, understand what is Null Hypothesis, Alternative hypothesis and types of hypothesis testing.

Topics

• Parametric vs Non-parametric tests
• Formulating a Hypothesis
• Choosing Null and Alternative hypothesis
• Type I and Type II errors
• Comparative study of sample proportions using Hypothesis testing
• 2 sample t test

Description: Learn about the various types of tests in Hypothesis testing. Get introduced to the prerequisites and conditions needed to proceed with a Hypothesis test. Understand the interpretation of the results of a Hypothesis testing and probabilities of Alpha error.

Topics

• 1 sample t test
• 1 sample z test
• ANOVA
• 2 Proportion test
• Chi-Square test
• Non-Parametric test

Description: Continuing the discussion on Hypothesis testing, learn more about non-parametric tests. Perform different tests and interpret the results.

Topics

• Non-Parametric test continued
• Hypothesis testing using Python and R

• Scatter Diagram
• Correlation Analysis
• Principles of Regression
• Introduction to Simple Linear Regression
• R shiny and Python Flask
  • Introduction to R shiny and Python Flask (deployment)
• Multiple Linear Regression

Description: Learn about Linear Regression, components of Linear Regression viz regression line, Linear Regression calculator, Linear Regression equation. Get introduced to Linear Regression analysis, Multiple Linear Regression and Linear Regression examples.

Topics

• Scatter diagram
  • Correlation Analysis
  • Correlation coefficient
• Ordinary least squares
• Principles of regression
• Splitting the data into training, validation and testing datasets
• Understanding Overfitting (Variance) vs Underfitting (Bias)
• Generalization error and Regularization techniques
• Introduction to Simple Linear Regression
• Heteroscedasticity / Equal Variance

Description: In the second part of the tutorial, you will learn about the Models and Assumptions for building Linear Regression Models, build Multiple Linear Regression Models and evaluate the results of the Linear Regression Analysis.     

Topics

• LINE assumption
  • Collinearity (Variance Inflation Factor)
  • Linearity
  • Normality
• Multiple Linear Regression
• Model Quality metrics
• Deletion diagnostics

Description: Learn to analyse Attribute Data, understand the principles of Logistic Regression, Logit Model. Learn about Regression Statistics and Logistic Regression Analysis.

Topics

• Principles of Logistic Regression
• Types of Logistic Regression
• Assumption and Steps in Logistic Regression
• Analysis of Simple Logistic Regression result

Description: Learn about the Multiple Logistic Regression and understand the Regression Analysis, Probability measures and its interpretation. Know what is a confusion matrix and its elements. Get introduced to “Cut off value” estimation using ROC curve. Work with gain chart and lift chart.     

Topics

• Multiple Logistic Regression
• Confusion matrix
  • False Positive, False Negative
  • True Positive, True Negative
  • Sensitivity, Recall, Specificity, F1
• Receiver operating characteristics curve (ROC curve)
• Lift charts and Gain charts 

• Lasso and Ridge Regressions

Description: Get introduced to Multinomial regression, or otherwise known as multinomial logistic regression, learn about multinomial logit models and multinomial logistic regression examples.

Topics

• Logit and Log Likelihood
• Category Baselining
• Modeling Nominal categorical data
• Additional videos are provided on Lasso / Ridge regression for identifying the most significant variables

Description: As part of Data Mining Unsupervised get introduced to various clustering algorithms, learn about Hierarchial clustering, K means clustering using clustering examples and know what clustering machine learning is all about.

Topics

Hierarchial

• Supervised vs Unsupervised learning
• Data Mining Process
• Measure of distance
  • Numeric - Euclidean, Manhattan, Mahalanobis
  • Categorical - Binary Euclidean, Simple Matching Coefficient, Jaquard’s Coefficient
  • Mixed - Gower’s General Dissimilarity Coefficient
• Types of Linkages
  • Single Linkage / Nearest Neighbour
  • Complete Linkage / Farthest Neighbour
  • Average Linkage
  • Centroid Linkage
• Hierarchical Clustering / Agglomerative Clustering

Description: In this continuation lecture learn about K means Clustering, Clustering ratio and various clustering metrics. Get introduced to methods of making optimum clusters.

Topics

• Non-clustering
  • K-Means Clustering
  • Measurement metrics of clustering - Within Sum of Squares, Between Sum of Squares, Total Sum of Squares
  • Choosing the ideal K value using Scree plot / Elbow Curve
• Additional videos are provided to understand K-Medians, K-Medoids, K-Modes, Clustering Large Applications (CLARA), Partitioning Around Medoids (PAM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Ordering Points To Identify the Clustering Structure (OPTICS)

Description: Learn to apply data reduction in data mining using dimensionality reduction techniques. Gain knowledge about the advantages of dimensionality reduction using PCA and SVD.

Topics

• Why dimension reduction
• Advantages of PCA
• Calculation of PCA weights
• 2D Visualization using Principal components
• Basics of Matrix algebra
• SVD – Decomposition of matrix data

Description: Under data mining unsupervised techniques, learn about Network Analytics and the measures used. Get introduced to Network Analysis tools like NodeXL.

Topics

• Definition of a network (the LinkedIn analogy)
• Measure of Node strength in a Network
  • Degree centrality
  • Closeness centrality
  • Eigenvector centrality
  • Adjacency matrix
  • Betweenness centrality
  • Cluster coefficient
• Introduction to Google Page Ranking

Description: Learn one of the most important topic Association rules in data mining. Understand how the Apriori algorithm works, and the association rule mining algorithm.

Topics

• What is Market Basket / Affinity Analysis
• Measure of association
  • Support
  • Confidence
  • Lift Ratio
• Apriori Algorithm
• Sequential Pattern Mining

Description: Learn how online recommendations are made. Get insights about online Recommender System, Content-Based Recommender Systems, Content-Based Filtering and various recommendation engine algorithms. Get to know about people to people collaborative filtering and Item to item collaborative filtering.

Topics

• User-based collaborative filtering
• Measure of distance / similarity between users
• Driver for recommendation
• Computation reduction techniques
• Search based methods / Item to item collaborative filtering
• SVD in recommendation
• Vulnerability of recommender systems

Description: Text mining or Text data mining is one of the wide spectrum of tools for analyzing unstructured data. As a part of this course, learn about Text analytics, the various text mining techniques, its application, text mining algorithms and sentiment analysis.

Topics

• Sources of data
• Bag of words
• Pre-processing, corpus Document-Term Matrix (DTM) and TDM
• Word Clouds
• Corpus level word clouds
  • Sentiment Analysis
  • Positive Word clouds
  • Negative word clouds
  • Unigram, Bigram, Trigram
• Semantic network
• Clustering

Description: Learn how to extract data from Social Media, download user reviews from E-commerce and Travel websites. Generate various visualizations using the downloaded data.     

Topics

• Extract Tweets from Twitter
• Extract user reviews of the products from Amazon, Snapdeal and TripAdvisor

Description: Learn how to perform text analytics using Python and work with various libraries that aid in data extraction, text mining, sentiment analysis and  

Topics

• Install Libraries from Shell
• Extraction and text analytics in Python

Description: Natural language processing applications are in great demand now and various natural language processing projects are being taken up. As part of this tutorial, learn about Natural language and ‘Natural language understanding’.

Topics

• LDA
• Topic Modeling 
• Sentiment Extraction
• Lexicons and Emotion Mining

Description: Learn about Machine Learning modeling using KNN, the K nearest neighbour algorithm using KNN algorithm examples. The KNN classifier is one of the most popular classifier algorithms.

Topics

• Deciding the K value
• Building a KNN model by splitting the data
• Understanding the various generalization and regulation techniques to avoid overfitting and underfitting

Description: Under the Naive Bayes classifier tutorial, learn how the classification modeling is done using Bayesian classification, understand the same using Naive Bayes example. Learn about Naive Bayes through the example of text mining.

Topics

• Probability – Recap    
• Bayes Rule
• Naive Bayes Classifier
• Text Classification using Naive Bayes

Description: Decision Tree and Random Forest are one of the most powerful classifier algorithms today. Under this tutorial, learn about Decision Tree Analysis, Decision Tree examples and Random Forest algorithms.

Topics

• Elements of Classification Tree - Root node, Child Node, Leaf Node, etc.
• Greedy algorithm
• Measure of Entropy
• Attribute selection using Information Gain
• Ensemble techniques
• Decision Tree C5.0 and understanding various arguments
• Random Forest and understanding various arguments

Description: Bagging and Boosting is an ensemble technique which is a part of the random forest algorithm. Learn about Bagging and Boosting examples under this tutorial.

Topics

• Boosting / Bootstrap Aggregating
• AdaBoost / Adaptive Boosting
• Stacking
• Gradient Boosting
• Extreme Gradient Boosting (XGB)

Description: Artificial Neural Network and Support Vector Machines are the two powerful Deep learning algorithms. Get introduced to Neural Net, Convolutional Neural Network, Recurrent Neural Network. Learn how to work with Support Vector Machine, SVM classifiers and SVM regression.

Topics

• Artificial Neural Network
• Biological Neuron vs Artificial Neuron
• ANN structure
• Activation function
• Network Topology
• Support Vector Machines
• Classification Hyperplanes
• Best fit “boundary”
• Kernel Trick

• Concept with a business case

Description: Forecasting or Time Series Analysis is an important component in analytics. Here, get to know the various forecasting methods, forecasting techniques and business forecasting techniques. Get introduced to the time series components and the various time series analysis using time series examples.

Topics

• Introduction to time series data
• Steps of forecasting
• Components of time series data
• Scatter plot and Time Plot
• Lag Plot
• ACF - Auto-Correlation Function / Correlogram
• Visualization principles
• Naive forecast methods
• Errors in forecast and its metrics
• Model Based approaches
  • Linear Model
  • Exponential Model
  • Quadratic Model
  • Additive Seasonality
  • Multiplicative Seasonality
• Model-Based approaches
• AR (Auto-Regressive) model for errors
• Random walk
• ARMA (Auto-Regressive Moving Average), Order p and q
• ARIMA (Auto-Regressive Integrated Moving Average), Order p, d and q
• Data-driven approach to forecasting
• Smoothing techniques
  • Moving Average
  • Exponential Smoothing
  • Holts / Double Exponential Smoothing
  • Winters / HoltWinters
• De-seasoning and de-trending
• Econometric Models
• Forecasting Best Practices
• Forecasting using Python
• Forecasting using R

• Basic Statistics
  • Data types Identification and probability
  • Expected values, Measures of central tendencies
  • Skewness and Kurtosis & Boxplot
  • Practice Mean, Median, Varience, Standard Deviation and Graphical representations in R
  • Creating Python Objects
  • Practice Mean, Median, Varience, Standard Deviation and Graphical representations in Python
  • Confidence intervals and distributions
• Hypothesis Testing
  • Buyer ratio
  • Customer Order Form
  • Cutlets
  • Pantaloons
  • Lab TAT
• Linear regression
  • Prediction of weight based on Calories consumed
  • Delivery Time period Vs Sorting time
  • Employee Churn rate Vs Salary
  • Salary Prediction
• R shiny and Flask
  • Practice R shiny and Python Flask for Linear Regression assignments
• Multiple Linear Regression
  • 50 startups case study
  • Computer data Case study
  • Toyota Corolla
• Logistic Regression
  • Term deposit case study
  • Elections results Case study
• Multinomial Regression
  • Student Program Case study
• Hierarchical Clustering
  • Crime data
  • Eastwest Airlines
• K means Clustering
  • Insurance policy
  • Crime data
• PCA
  • Dimension Reduction for Wine data
• Network Analytics
  • Node Properties practice in R
• Association Rules
  • Association Rules for Book store
  • Association Rules for Mobile store
  • Association Rules for Retail Transactions
• Recommendation Engine
  • Recommend Jokes for subscribers
• Text mining, Web Extraction
  • Extraction of tweets from twitter
  • Reviews from ecommerce websites
• Text mining
  • Sentiment Analysis on extracted data
• NLP
  • Emotion mining by extracting a speech or novel from web
• Naive Bayes
  • Spam and Ham classifications
• KNN Classifier
  • Types of Glass
  • Classification of Animals
• Decision Tree and Random Forest
  • Fraud Check
  • Sales prediction of an Organization
• XGB and GLM
  • Social Networks Ads
• Lasso and Ridge Regression
  • Practice Lasso and Ridge with multiple Linear Assignments
• ANN
  • Forest Fires case study
• SVM
  • Classification of Alphabets
• Survival analysis
  • Prediction of Patient survival probability
• Forecasting model based
  • Airlines Forecasting
  • Forecasting of sales for a soft drinks case study
• Forecasting
  • Forecasting of Bike shares
  • Forecasting of Solar power consumption

• Industry : Aviation

  Predicting the flight delays

  • How to determine which flights would be delayed and by how long?


• Industry : Manufacturing

  Predict impurity in ore

  • The main goal is to use this data to predict how much impurity is in the ore concentrate As this impurity is measured every hour if we can predict how much silica (impurity) is in the ore concentrate, we can help the engineers giving them early information to take actions


• Industry : Oil and Gas

  Predicting the oil price

  • Oil production and prices data are for 1932-2014(2014 data are incomplete );gas production and prices are for 1955-2014 export and net export data are for 1986-2013


• Industry : Automotive

  Electric Motor Temperature

  • Predict the temperature of rotor and stator of E-Motor


• Industry : Daily Analysis of a product

  "Daily" Twitter Data Analysis for a Product

  • Sentiment Emotion mining of twitter data of new product


• Industry : E commerce

  Natural Language Processing

  • Top 5 relevant answers to be retrived based on input question

• Resume Preparation
• Interview Support

• Introduction to Big Data
• Challenges in Big Data and Workarounds
• Introduction to Hadoop and its Components
• Hadoop components and Hands-on
• Understand the MapReduce (Distributed Computation Framework) and its Drawback
• Introduction to Spark
• Spark Components
• Spark MLlib and Hands-on (one ML model in spark)

Introduction to R Programming

• Introduction to R
• Data Types in R

How To Install R & R Studio

Data Structures in R

• Variable in R
• R-Overview
  • Vector
  • Matrix
  • Array
  • List
  • Data-Frame
• Operators in R
  • arithmetic
  • Relational
  • Logical
  • Assignment
  • Miscellaneous
• Conditiional Statement
  • Decision Making<
    • IF Statement
    • IF-Else Statement
    • Nested IF-Else Statement
    • Switch Statement
  • Loops
    • While Loop
    • Repeat Loop
    • For Loop
  • Strings
  • Functions
    • User-defined Function
    • Calling a Function
    • Calling a Function without an Argument
    • Calling a Function with an Argument

Programming Statistical

• Box Plots
• Bar Charts
• Histogram
• Pareto Chart
• Pie Chart
• Line Chart
• Scatterplot

How to Import Dataset in R

• Read CSV Files
• Read Excel Files
• Read SAS Files
• Read STATA Files
• Read SPSS Files
• Read JSON Files
• Read Text Files

R-Packages

• DpLyr
• Hmisc or mise
• Ggplot2
• Caret
• Data Table

How to Integrate R and SQL

How to Get Data From SQL to R

Introduction to python

Description: You will get an introduction to the Python programming language and understand the importance of it. How to download and work with Python along with all the basics of Anaconda will be taught. You will also get a clear idea of downloading the various Python libraries and how to use them.

Topics

• Introduction to Python
• Installation of Anaconda Python
• Difference between Python2 and Python3
• Python Environment
• Operators
• Identifiers
• Exception Handling (Error Handling)

Basic Python

Description: In this module, you will learn the basics such as assigning a variable, differences between dictionary, sets, tuple, and some decision making statements. Also, you will learn about working with different loops, data types and its usage.

Topics

• Data Types
• Conditional Statements
• Functions
• Loops

Working with libraries like NumPy, Pandas, Matplotlib, Seaborn, SciPy, Sklearn In Python

Description: This module helps you to learn and understand the different libraries used in Python. You will also get a clear idea about the NumPy library and how you can use it. NumPy is a Numeric Python library which helps in dealing with the numeric calculations with data frames.

Topics

• NumPy Introduction
• Arrays
• Array Indexing
• NumPy Data Types
• Treating Missing and NA’s
• Reshaping and combining Arrays

Working experience with Pandas In Python

Description: In this module, you will learn how to download the Pandas package and syntax for the same. Pandas is also a library similar to Numpy which predominantly helps in working with series data and data frames. You will learn how to impute the data in the place of missing values called the missing value treatment done in the Pandas package itself.

Topics

• Pandas Introduction
• Basic Operations on Series
• Dataframe
• Working with Text Data
• Working with Missing Data
• Indexing and Selecting Data
• Merge, Join and Concatenate

Working experience with Matplotlib library In Python

Description: In this module, you will learn where, how and when to use the Matplotlib library. This library is used to visualize the data. You will get an in-depth understanding of the importance of this library.

Topics

• Introduction to Matplotlib
• Matplotlib design and different visualizations

Description: This module will help you to understand the importance of Seaborn package and downloading the library Just like the Matplotlib library, the Seaborn library is also used in visualizing the data allowing high-level visualizations with categorical data.

Topics

• Introduction to Seaborn Library
• Visualizing the Distribution of the Datasets
• Plotting the Categorical Data
• Visualizing Linear Relationships
• Visualizing Statistical Relationships

To work with Seaborn Library (High-level interface for drawing attractive and informative statistical graphics) In Python

Description: This module will help you to understand the importance of Seaborn package and downloading the library Just like the Matplotlib library, the Seaborn library is also used in visualizing the data allowing high-level visualizations with categorical data.

Topics

• Introduction to Seaborn Library
• Visualizing the Distribution of the Datasets
• Plotting the Categorical Data
• Visualizing Linear Relationships
• Visualizing Statistical Relationships

Introduction to SciPy and Sklearn Libraries In Python

Description: In this module, you will understand the importance of both Scipy and Sklearn libraries which are predominantly used in building Machine Learning Algorithms working with Linear Equations. Sklearn also known as Scikit-learn, is a machine learning library for the Python programming language. You will get a clear idea of where you can use these libraries along with some examples.

Topics

• Installing both SciPy and Sklearn Libraries
• Introduction to SciPy (Mathematical Algorithms)
• Introduction to Sklearn (Machine Learning Algorithms)

• Introduction to Cloud Computing
• Difference between On Premise and Cloud
• Types of Service Models
• Advantages of Cloud Computing
• Azure Global Infrastructure
• Creation of Free tire account inside Azure
• Sample Instance Creating Both UNIX and Windows and connecting them on cloud
• Storage options and Creating Extra Storage and attaching to the VMs
• Blob Storage
• Creating DB instance
• Creating Custom VN
• Brief introduction to Machine Learning Services on Cloud and more

• Introduction to What is DataBase
• Difference between SQL and NOSQL DB
• How to Install MYSQL and Workbench
• Connecting to DB
• Creating to DB
• What are the Languages inside SQL How to Create Tables inside DB and Inserting the Records
• Select statement and using Queries for seeing your data
• Joining 2 tables
• Where clause usage
• Indexes and views
• Different operations in SQL
• How to Connect to your applications from MYSQL includes R and Python

What is Data Visualization?

• Why Visualization came into Picture?
• Importance of Visualizing Data
• Poor Visualizations Vs. Perfect Visualizations
• Principles of Visualizations
• Tufte’s Graphical Integrity Rule
• Tufte’s Principles for Analytical Design
• Visual Rhetoric
• Goal of Data Visualization

Tableau – Data Visualization Tool

• Introduction to Tableau
• What is Tableau? Different Products and their functioning
• Architecture Of Tableau
• Pivot Tables
• Split Tables
• Hiding
• Rename and Aliases
• Data Interpretation

Tableau User Interface

• Understanding about Data Types and Visual Cues

Basic Chart types

• Text Tables, Highlight Tables, Heat Map
• Pie Chart, Tree Chart
• Bar Charts, Circle Charts

Intermediate Chart

• Time Series Charts
• Time Series Hands-On
• Dual Lines
• Dual Combination

Advanced Charts

• Bullet Chart
• Scatter Plot
• Introduction to Correlation Analysis
• Introduction to Regression Analysis
• Trendlines
• Histograms
• Bin Sizes in Tableau
• Box Plot
• Pareto Chart
• Donut Chart, Word Cloud
• Forecasting ( Predictive Analysis)

Maps in Tableau

• Types of Maps in Tableau
• Polygon Maps
• Connecting with WMS Server
• Custom Geo coding
• Data Layers
• Radial & Lasso Selection

Adding Background Image

• How to get Background Image and highlight the data on it
• Creating Data Extracts
• Filters and their working at different levels 
• Usage of Filters on at Extract and Data Source level
• Worksheet level filters
• Context, Dimension Measures Filter

Data Connectivity in-depth understanding

• Joins
• Unions
• Data Blending
• Cross Database Joins
• Sets
• Groups
• Parameters

Creating Calculated Fields

• Logical Functions
• Case-If Function
• ZN Function
• Else-If Function
• Ad-Hoc Calculations
• Quick Table Calculations
• Level of Detail (LoD)
• Fixed LoD
• Include LoD
• Exclude LoD

Responsive Tool Tips

• Dashboards
• Actions at Sheet level and Dashboard level
• Story

Connecting Tableau with Tableau Server

• Publishing our Workbooks in Tableau Server
• Publishing dataset on to Tableau Server
• Setting Permissions on Tableau Server

Connecting Tableau with R

• What is R?
• How to integrate Tableau with R?
• Tableau Prep

Description: Understand about the various mathematical concepts which are important to learn AI implementations. These concepts will help to understand Deep Learning concepts in detail. It will also serve as a refresher for learning various Neural Network algorithms, which are synonymous to Deep Learning.

Topics

• Introduction to Data Optimization
• Calculus and Derivatives Primer
• Finding Maxima and Minima using Derivatives in Data Optimization
• Data Optimization in Minimizing errors in Linear Regression
• Gradient Descent Optimization
• Linear Algebra Primer
• Probability Primer

Description: Understand about the basics of the first algorithm – Perceptron Algorithm, its drawbacks and how we can overcome those challenges using Artificial Neural Network or Multilayer Perceptron Algorithm. The various activation functions will be understood in detail and practical exposure to Python programming is the highlight of this module.

Topics

• Understand the history of Neural Networks
• Learn about Perceptron algorithm
• Understand about Backpropagation Algorithm to update weight
• Drawbacks of Perceptron Algorithm
• Introduction to Artificial Neural Networks or Multilayer Perceptron
• Manual calculation of updating weights of final layer and hidden layers in MLP
• Understanding of various Activation Functions
• R code and Python code to understand about practical model building using MNIST dataset

Description: Learn about the various Error functions, which are also called Cost functions or Loss functions. Also, understand about the entropy and its use in measuring error. Understand the various optimization techniques, drawbacks and ways to overcome the same. This you will learn alongside various terms in implementing neural networks.

Topics

• Understand about challenges in Gradient
• Introduction to various Error, Cost, Loss functions
• ME, MAD, MSE, RMSE, MPE, MAPE, Entropy, Cross Entropy
• Vanishing / Exploding Gradient
• Learning Rate (Eta), Decay Parameter, Iteration, Epoch
• Variants of Gradient Descent
  • Batch Gradient Descent (BGD)
  • Stochastic Gradient Descent (SGD)
  • Mini-batch Stochastic Gradient Descent (Mini-batch SGD)
• Techniques to overcome challenges of Mini-batch SGD
  • Momentum
  • Nesterov Momentum
  • Adagrad (Adaptive Gradient Learning)
  • Adadelta (Adaptive Delta)
  • RMSProp (Root Mean Squared Propagated)
  • Adam (Adaptive Moment Estimation)

Description: Learn about practical applications of MLP when output variable is continuous and discrete in two categories and multi-category. Understand also about handling balanced vs imbalanced datasets. Learn about techniques to avoid overfitting and various weight initialization techniques.

Topics

• Binary classification problem using MLP on IMDB dataset
• Multi-class classification problem using MLP on Reuters dataset
• Regression problem using MLP on Boston Housing dataset
• Types of Machine Learning outcomes – Self-supervised, Reinforcement Learning, etc.
• Handling imbalanced datasets and avoiding overfitting and underfitting
• Simple hold-out validation
  • K-Fold validation
  • Iterated K-fold validation with shuffling
  • Adding weight regularization
    • L1 regularization
    • L2 regularization
  • Drop Out and Drop Connect
  • Early Stopping
  • Adding Noise – Data Noise, Label Noise, Gradient Noise
  • Batch Normalization
  • Data Augmentation
• Weight initialization techniques
  • Xavier, Glorot, Caffe, He

Description: Though CNN has replaced most of the computer vision and image processing concepts, a few application require the knowledge of Computer vision. We will learn about the application using the defacto library OpenCV for image processing. How to build machine learning models when we have limited data is explained as part of this module.

Topics

• Understanding about Computer Vision related applications
• Various challenges in handling Images and Videos
• Images to Pixel using Gray Scale and Color images
• Color Spaces – RGB, YUV, HSV
• Image Transformations – Affine, Projective, Image Warping
• Image Operations – Point, Local, Global
• Image Translation, Rotation, Scaling
• Image Filtering – Linear Filtering, Non-Linear Filtering, Sharpening Filters
• Smoothing / Blurring Filters – Mean / Average Filters, Gaussian Filters
• Embossing, Erosion, Dilation
• Convolution vs Cross-correlation
• Boundary Effects, Padding – Zero, Wrap, Clamp, Mirror
• Template Matching and Orientation of image
• Edge Detection Filters – Sobel, Laplacian, LoG (Laplacian of Gaussian)
• Bilateral Filters
• Canny Edge Detector, Non-maximum Suppression, Hysteresis Thresholding
• Image Sampling – Sub-sampling, Down-sampling
• Aliasing, Nyquist rate, Image pyramid
• Image Up-sampling, Interpolation – Linear, Bilinear, Cubic
• Detecting Face and eyes in the Video
• Identifying the interest points, key points
• Identifying corner points using Harris and Shi-Tomasi Corner Detector
• Interest point detector algorithms
  • Scale-invariant feature transform (SIFT)
  • Speeded-up robust features (SURF)
  • Features from accelerated segment test (FAST)
  • Binary robust independent elementary features (BRIEF)
  • Oriented FAST and Rotated Brief (ORB)
• Reducing the size of images using Seam Carving
• Contour Analysis, Shape Matching and Image segmentation
• Object Tracking, Object Recognition

Description: Understand about the various layers of CNN and understand how to build the CNN model from scratch as well as how to leverage upon the CNN model which is pre-trained. Understand about the best practices in building CNN algorithm and variants of convolution neural network.

Topics

• Understand about various Image related applications
• Understanding about Convolution Layer and Max-Pooling
• Practical application when we have small data
• Building the Convolution Network
• Pre-processing the data and Performing Data Augmentation
• Using pre-trained ConvNet models rather than building from scratch
• Feature Extraction with and without Data Augmentation
• How to Visualize the outputs of the various Hidden Layers
• How to Visualize the activation layer outputs and heatmaps

Description: Understand about how to deal with sequence data including textual data as well as time series data and audio processing. Understand about advanced RNN variant models including LSTM algorithm and GRU algorithm. Also learn about bidirectional RNN, LSTM and deep bidirectional RNN and LSTM. Learn about various unstructured textual pre-processing techniques.

Topics

• Understand about textual data
• Pre-processing data using words and characters
• Perform word embeddings by incorporating the embedding layer
• How to use pre-trained word embeddings
• Introduction to RNNs – Recurrent layers
• Understanding LSTM and GRU networks and associated layers
• Hands-on use case using RNN, LSTM, and GRU
• Recurrent dropout, Stacking recurrent layers, Bidirectional recurrent layers
• Solving forecasting problem using RNN
• Processing sequential data using ConvNets rather than RNN (1D CNN)
• Building models by combining CNN and RNN

Description: Understand about unsupervised learning algorithm such as GAN as well as Autoencoders. GANs are used extensively in artificially generating speech, images which can be used in computer games. Deep Dream is such an algorithm which using GAN to generate images. Autoencoders will take input as an image and traverse through the network and then regenerates the same image. Learn about how these intermediate layer representations can be used in other neural network deep learning models.

Topics

• Text generation using LSTM and generative recurrent networks
• Understanding about DeepDream algorithm
• Image generation using variational autoencoders
• GANs theory and practical models
• The Generator, the Discriminator, the Adversarial network
• Deep Convolution Generative Adversarial networks
• Producing audio using GAN
• Unsupervised learning using Autoencoders

Description: Reinforcement learning is majorly used in AI-based games. Q-learning is one such Reinforcement machine learning algorithm which is using in game theory. Finally, any of the ongoing Kaggle competition will be the prime focus and to be in the top 100 will be of prime importance. This will bring optimal visibility of the profiles to the employers and participants can be directly hired.

Topics

• Q-learning
• Exploration and Exploitation
• Experience Replay
• Model Ensembling
• Final project using a live Kaggle competition

• DNS Server
• What is Website?
• Dynamic, Static Websites
• Domain and Hosting registration
• Linking your domain to the Hosting
• Creating a Wordpress website
• Managing a Wordpress website.
• Free website creation
• Free webmails Creation up to 5
• Free database Creation

• Understanding the organizational structure
• Operations within the organization
• Importance of understanding the business operations
• Importance of Digital Marketing for a business

• What is Search Engine Optimization?
• History of Search engines?
• How is SEO important in Digital Marketing?
• How is search engine important for companies?
• How can search engine impact the brand and sales of a company?
• How does search engine algorithm work?
• Components of a search engine?
• Different types of search engines?
• Operators used in search engine
• Algorithms used in SEO
• Updates of SEO
• Site Structure Analysis
• Competitive and Exact Title Creation
• Meta Keywords Creation
• Meta Tag Optimization as per Keyword Research
• Perfect navigation as per the theme
• Breadcrumb Optimization
• Checking Broken Links
• Creating Sitemaps (HTML, XML, ROR, Text Sitemap) 
• Usage of Robots.txt in order assist crawler
• Robots.txt File Creation and validation
• Internal navigation
• Content Optimization as per Keyword Density, Proximity, Prominence
• 301 redirection for canonicalization problem
• Content Negotiation and Modification
• Regular Updation of the Sitemaps
• Create Custom Error 404 Page Optimization
• Heading Hierarchy (1H1, 2H2, 4H3,8H4 ..) 
• Image Optimization, Using Alt
• Google Analytics Account Set
• Create and Manage Google Webmaster Tool

• What is Link Building
• One Way link building
• Two Way or Reciprocal link
• Link Wheel Building
• White Hat Link Building
• Black Hat Link Building
• Grey Hat Link Building
• Google Panda Algorithms
• Google Penguin Algorithms
• Google Page Rank Algorithms
• Google Hummingbird Algorithms
• Page layout Algorithms
• Search Engine submission
• Directory submissions
• Dmoz Listing
• Forum Creation / submissions
• Blogs Creation, Posting and Commenting
• Blog Post Message Creation and Submission
• Articles Creation, Distribution and Commenting
• Press Release Creation and Distribution
• Newsletter posting and News Syndication
• Forums Posting and Comments
• Favicons Creation
• B2B Posting
• Driving traffic through dedicated Social Networking
• Facebook Marketing (Fan Pages and Profiles) 
• Twitter Marketing
• Social Bookmarking Strategy and Implementation
• Podcasting: Audio and Video Optimization
• Promoting Subsequent pages of the Website
• Google Site Map Submission
• Yahoo Site Map Submission
• Social Bookmarking SBM
• Social Networking
• Classified Ad Creation and Submission
• Posting in other Content Network
• Blog Creation and Commenting
• RSS Feed Creation and Validation
• Link Building
• Link Popularity Monitoring and Reporting
• Listed on Google/Yahoo Local Business Center
• Wikki Submission.

• Introduction to Online Marketing
• Types of Online Marketing 
• Introduction to Social Media 

Facebook Marketing 

• Facebook Marketing introduction
• Advantages of Facebook Marketing
  • What is Open Graph
  • Local business page creation
  • Fan page creation
  • Brand page creation
  • Organization page creation
  • Adding own logos and banners in facebook
  • How to promote your Facebook page
• Introduction to FBML
• Advantages to FBML
• Facebook optimization techniques
  • Creating a Facebook Application 
• Linking with YouTube
• Creating Events in Facebook
  • Do's and Don'ts in Facebook
  • Facebook credits
  • Facebook Connect (Like, Share, Comment) 
  •  Increase Facebook likes

Twitter marketing

• What is Twitter
• Why we use Twitter
  • witter Demographics
  • How to Setup a Twitter account
• What is Twitter Lingo
  • What is Tweet for pay
• Twitter Account Promotion
  • Tweeting (Responding to others, RT, HashTags, Direct Messages) 
• What is Twitiquette
  • Why use Short URL in Twitter

Google Plus Marketing

• What is Google Plus 
  • Google Plus Features
  • Google Plus: Circles, Hangouts, Stream
• Integration with your site
  • Promoting a Brand on Google+
  • Google Plus for Businesses
• Google Plus Tools and Techniques

LinkedIn Marketing

• What is LinkedIn
• LinkedIn advantages
• LinkedIn Groups
  • LinkedIn events, messaging
• Creating the right profile and settings
• How to do link building in LinkedIn
  • Linkedin Company Pages

Social Networking Sites

• Digg
• Delicious
• StumbleUpon
  • Reddit
  • Fave It
  • E-buzz

Web 2.0 Backlinks Building

• Blogger.com
  • WordPress.com
• Webs.com
  • Webnode.com
  • Yola.com
• Blinkweb
  • Clammo
  • 360.com
  • Adding Titles to blogs
• Adding Keywords to blogs
• Adding Description to blogs
  • Building Backlinks from blogs
• Promoting Blogs ways

Content sharing 

• Ezine articles approved
  • Squidoo Lens Creation
  • Hubpages Creation
  • Slideshare
• Scribd

Photo and Slides Sharing

• Flickr
• TinyPic
• Slide Roll

• Facebook Marketing (paid)
• Twitter Marketing (paid)
• LinkedIn Marketing (paid)

• YouTube Channel Creation
• YouTube Video Optimization
• Video SEO
• Increase YouTube views, Subscribers
• YouTube Ads
• Dailymotion
• Vimeo Videos
• Metacafe
• Google Videos

1. Introduction to Google Ads

2. Types of Google Ads

• Text Ads - Search Network
  • Keyword
  • Brand
  • Remarketing Text Ads - RLSA
• Google Display Network
  • Image / Display Ads
  • Text Ads
  • Remarketing Ads
• Video / YouTube Ads
  • Pre-roll ads (Video ads)
  • Image Ads
  • Remarketing image ads
  • Remarketing Video ads
• Shopping Ads
  • Search network
  • YouTube ads
• G-mail sponsored promotions (GSP)
  • Gmail ads

3. Ad extensions

• Callout Extensions
• Sitelink extensions
• Structured Snippets
• Call extensions
• Price extensions etc.,

4. Ad groups and ads setup

5. Type of Ads

• Expanded text ads
• Ads examples
• Google ads responsive display ads
• Google auto ads suggestions - turn this off

6. Adwords Editor tool explained

• Editor overview
• How to setup campaign
• Ad groups & keywords setup
• Single keyword targeting (SKAG's)
• How to setup a campaing from Excel
• How to add different match type keywords in SKAG's
• How to get and post your campaign
• How to make multiple changes by copy / paste

7. Budget

• Increase conversions by optimising budget

8. Call Ads

• Call only ads

9. Campaign setup

• Campaign setup
• Location bids

10. Conversions & goals

• What are goals and conversions
• The only 3 google ads metrics you should focus on
• Front end and backend profits
• How to see your conversions in google ads
• How to setup converison tracking code
• Conversions vs All conversions
• Call conversions

11. Google Analytics

• How to link google analytics to Google Ads
• Google Analytics Goals

12. Google Display Networks - GDN

• GDN explained
• Google display network campaign setup
• How to save money on GDN
• Google display network targeting options

13. Gmail Ads

• Introduction
• Gmail metrics
• 8 Gmail targeting methods
• How to setup gmail ads campaign
• How to setup gmail ads adgroup
• How to create gmail video ads
• How to create gmail image ads
• Targeting: customer match audiences
• Targeting: affinity audiences
• Targeting: In market audineces
• 8 ways to optimise gmail ads

14. Keywords

• Exact Macth keywords
• Phrase Match keywords
• Negative Keywords

15. Quality Score

• Qulaity score is an estimate of the quality of your ad, keyword and landing page
  • 10 - discounted by 50%
• Higher quality score can lead to lower prices and better positions
  • 9 - discounted by 44%
• each keyword has quality score
  • 8 - discounted by 37%
• QS is calculated on a 1-10 scale
  • 7 - discounted by 28%
• Its components are expected click through rate, ad relevance & landing page
  • 6 - discounted by 16%
  • 5 Google Benchmark
  • 4 - increased by 25%
  • 3 - increased by 67%
  • 2 - increased by 150%
  • 1 - increased by 400%
• How do i see my quality score
• How google calculate your Cost Per Click
  • Your price = the adrank of the advertiser below you / Your Quality Score + 0.78 rs
• How to do keyword research
• Use negative keywords to increase profitability
  • Singular & Plural
  • Research
  • Use keyword.io tool
  • Create negative keyword list
  • Match types
  • [exact]
  • "phrase"
  • broad match
  • Search Query report
  • Use negative keywords for both search & display campaigns
  • Audit your negative keywords regularly
  • GDN use broad match negatives only
  • YouTube use placements or keywords
  • Voice searches - use negaitve in 1st 10 words

16. Labels

• How to create labels
• How to apply & filter labels

17. Google ads landing page best practices

• Top 6 tips to otimize your landing page
• Offer - clearly state your offer.
• Benefits & Features
• Simple layout - No navigation bar - hosted in subdomain
• Strong Call to action - call you / download / watch a video / PDF / Buy
• Forms - keep it very short
• Video

18. Landing page case study

19. How to improve landing page experience

• Offer relevant and original content
• Be fully transperant & trustworthieness on your site
• Make your website secure with SSL
• Make navigation easy
• Decrease your landinga page loading time

20. How to calculate max. cost per aquisation

21. 5 deadly google ads mistakes to avoid

• How to craete custom columns
• Adwords preview & diagnosis tool
• Google Ads click fraud
• Google Ads Reporting
• Optimization workflow
  • Locations
  • Ad schedule
  • Ad schedule advanced
  • Devices
  • Improve Click through Rate - CTR
  • Negative keywords
  • Search term reports

22. Remarketing

• How to ceate YouTube remarketing lists in Google Ads

23. YouTube Video Ads

• Introduction
• How to grow your business with YouTube video ads
• Setup YouTube Account
• Setup Google Ads
• Link Google ads to YouTube
• Link Google Analytics
• Setup conversion tracking code
• Setup video remarketing list
• Setup Google ads remarketing tag
• Setup your 1st Video campaign
• Setup ad group & Targeting
• Creating ads
• Targeting options
  • Targeting: Affinity audiences
  • Targeting: custom affinity audiences
  • Targeting: In-market audiences
  • Targeting: Life events
  • Targeting: Remarekting
  • Targeting: Upload Linkedin lists
  • Targeting: Placements
  • Targeting: Negative placements
  • Targeting: Topics
  • Targeting: Topic+Kewyords
  • Campaign results
  • Campaign optimisation
  • Analyse Data using Excel
  • Google Data Studio

24. How to setup your sales funnel

25. My Client Center - MCC

26. List Of 10,000+ Junk & Spammy URLs To Stop Wasting Your Budget On GDN

27. Google Ads Live Project

• Navigate Google Analytics with ease
• Using Analytics as a part of SEO / SEM strategies
• Analyzing website traffic and content reports
• Setting up of goals, event and tracking campaigns
• Google Analytics Certification Guidance

• Introduction
  • When Social Media goes bad! 
  • The cost of a bad reputation
• Establish a Foundation
  • Protect your position  Register Twitter, URLs, etc
  • How to establish a Social Media company policy
  • The socialisation of business: Public relations to human relations
• Listen
  • Free social media monitoring tools
  • Search
  • Paid for social media solutions
  • What to do with the data
• Respond
  • Dealing with complaints
  • What to do with praise 
  • Evangelist
  • Crisis Q&A
  • Complaint escalation plan
• Engage
  • Enhancing reputation online
  • Be seen to listen
  • Ask customers for feedback
  • Build your customers into your R&D

• How Email Marketing works?
• Types of mails
• What to write
• How to write
• When to send
• Tools used in Email Marketing
• How to measure
• List creation
• List Management
• Auto responders
• Email Marketing report generation and its Metrics

• What is Mobile SEO?
• How to create a mobile site?
• Google Mobile
• Mobile Sitemap
• Search criteria for different smartphones
• Onsite Optimization Basics for moile
• Website Structure and Navigation in mobile
• Creating Filenames in Site
• Title Tag Optimization
• Meta Tags Optimization
• Copywriting and SEO Copywriting
• Header Optimization
• Anchor Links Optimization
• Snippets Creation for mobile

• Basics in ASO
• Important factors to get on top of the search list in play store and App Store
• Content optimization for App
• Importance of title tags in ASO
• Load time

1. Softwares / Plugins
2. Keyword Finders
3. Search Ranking Softwares
4. Content Spinners
5. Plagiarism Checkers
6. Authority Checkers
7. Rank Checkers
8. SEO Optimization Plugins - QK

1. Google Search Console (Webmaster)
2. A Key to SEO Training and Success
3. How to Rank your Website in Specific Country
4. Resolving Website Errors
5. Geo-Targeting
6. 301 Redirection
7. Domain Optimization
8. Spam Control
9. Site Links Control
10. Awesome tool to Check Incoming Links
11. Malware Check

1. SEO For Local Business
2. How to rank a website in local searches
3. Get Ranking on Local Keywords
4. Verification and Installation Process
5. How to Increase STAR Rankings
6. Yellow Pages Creation

• Google Ads - Fundamentals
  • Search Ads
  • Display Ads
  • Shopping Ads
  • Video Ads
• App Install Ads
• Facebook Blueprint Certification
• Analytics Certification
• Course Certification