DeepNeuron online master's in Data Science program lets you gain proficiency in Data Science. You will work on real-world projects in Data Science with R, Hadoop Dev, Test, and Analysis, Apache Spark, Scala, Deep Learning, Tableau, Data Science with SAS, SQL, MongoDB, and more. As part of this online & classroom training, you will receive Three additional self-paced courses co-created with IBM, namely, Excel, MongoDB, MS-SQL. M Enroll now and pursue your MS in Data Science online. IBM is the second-largest Predictive Analytics and Machine Learning solutions provider globally (source: The Forrester Wave report, September 2018). A joint partnership with Data2business insights and IBM introduces students to integrated blended learning, making them experts in Artificial Intelligence and Data Science. The Data Science course in collaboration with IBM will make students industry-ready for Artificial Intelligence and Data Science job roles. What can I expect from these Data Science courses developed in collaboration with IBM? Upon completion of this Data Scientist online Master's program, you will receive the certificates from IBM(for IBM courses) and DeepNeuron for the courses in the learning path. These certificates will testify to your skills as an expert in Data Science.

Access to IBM Cloud Lite account

Industry-recognized Data Scientist Master's certificate

Data Scientist is one of the hottest professions.IBM predicts the demand for Data Scientists will rise by 28% by 2020. Data Scientist Master’s program co-developed with IBM encourages you to master skills including statistics, hypothesis testing, data mining, clustering, decision trees, linear and logistic regression, data wrangling, data visualization, regression models, Hadoop, Spark, PROC SQL, SAS Macros, recommendation engine, supervised, and unsupervised learning and more.

Machine Learning project management methodology

Data Collection - Surveys and Design of Experiments

Data Types namely Continuous, Discrete, Categorical, Count, Qualitative, Quantitative and its identification and application

Further classification of data in terms of Nominal, Ordinal, Interval & Ratio types

Balanced versus Imbalanced datasets

Cross Sectional versus Time Series vs Panel / Longitudinal Data

Batch Processing vs Real Time Processing

Structured versus Unstructured vs Semi-Structured Data

Sampling techniques for handling Balanced vs. Imbalanced Datasets

What is the Sampling Funnel and its application and its components?

Population

Sampling frame

Simple random sampling

Sample

Measures of Central Tendency & Dispersion

Population

Mean/Average, Median, Mode

Variance, Standard Deviation, Range

A Data scientist is the top ranking professional in any analytics organization. Glassdoor ranks Data Scientists first in the 25 Best Jobs for 2019. In today’s market, Data Scientists are scarce and in demand. As a Data Scientist, you are required to understand the business problem, design a data analysis strategy, collect and format the required data, apply algorithms or techniques using the correct tools, and make recommendations backed by data.

Data Visualization helps understand the patterns or anomalies in the data easily and learn about various graphical representations in this module. Understand the terms univariate and bivariate and the plots used to analyze in 2D dimensions. Understand how to derive conclusions on business problems using calculations performed on sample data. You will learn the concepts to deal with the variations that arise while analyzing different samples for the same population using the central limit theorem.

Gain an in-depth understanding of data structure and data manipulation

Understand and use linear and non-linear regression models and classification techniques for data analysis

Obtain an in-depth understanding of supervised and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline

Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave

Gain expertise in mathematical computing using the NumPy and Scikit-Learn packages

Understand the different components of the Hadoop ecosystem

Learn to work with HBase, its architecture, and data storage, learning the difference between HBase and RDBMS, and use Hive and Impala for partitioning

Understand MapReduce and its characteristics, plus learn how to ingest data using Sqoop and Flume

Master the concepts of recommendation engine and time series modeling and gain practical mastery over principles, algorithms, and applications of machine learning

Learn to analyze data using Tableau and become proficient in building interactive dashboards

Programming Languages,Tools & Packages

Data Science & AI Course Modules

Many modules are in great demand for the requirements in the present changing business. The black box is the most powerful technique used to validate against the external factors that are responsible for software issues. The supervised machine learning algorithms include Linear Regression, Logistic Regression, Naive Bayes, Decision Trees, Support Vector systems, and many more. Deep learning is the lineage of Machine learning algorithms. Deep learning is mainly used in Computer vision, Bioinformatics, Audio recognition, and medical analyzing systems. Deep learning algorithms include Convolutional Neural Networks, Artificial Neural Networks, Multiple Linear Regression, Logistic regression, etc. Unsupervised learning in data mining includes Clustering, Neural networks, Principal component Analysis, Local outlier factor, and soon.

Data Science project management methodology, CRISP-DM will be explained in this module in finer detail. Learn about Data Collection, Data Cleansing, Data Preparation, Data Munging, Data Wrapping, etc. Learn about the preliminary steps taken to churn the data, known as exploratory data analysis. In this module, you also are introduced to statistical calculations which are used to derive information from data. We will begin to understand how to perform a descriptive analysis.

Machine Learning project management methodology

Data Collection - Surveys and Design of Experiments

Data Types namely Continuous, Discrete, Categorical, Count, Qualitative, Quantitative and its identification and application

Further classification of data in terms of Nominal, Ordinal, Interval & Ratio types

Balanced versus Imbalanced datasets

Cross Sectional versus Time Series vs Panel / Longitudinal Data

Batch Processing vs Real Time Processing

Structured versus Unstructured vs Semi-Structured Data

Sampling techniques for handling Balanced vs. Imbalanced Datasets

What is the Sampling Funnel and its application and its components?

Population

Sampling frame

Simple random sampling

Sample

Measures of Central Tendency & Dispersion

Population

Mean/Average, Median, Mode

Variance, Standard Deviation, Range

Learn about various statistical calculations used to capture business moments for enabling decision makers to make data driven decisions. You will learn about the distribution of the data and its shape using these calculations. Understand to intercept information by representing data by visuals. Also learn about Univariate analysis, Bivariate analysis and Multivariate analysis.

Measure of Skewness

Measure of Kurtosis

Spread of the Data

Various graphical techniques to understand data

Bar Plot

Histogram

Boxplot

Scatter Plot

Data Visualization helps understand the patterns or anomalies in the data easily and learn about various graphical representations in this module. Understand the terms univariate and bivariate and the plots used to analyze in 2D dimensions. Understand how to derive conclusions on business problems using calculations performed on sample data. You will learn the concepts to deal with the variations that arise while analyzing different samples for the same population using the central limit theorem.

Line Chart

Pair Plot

Sample Statistics

Population Parameters

Inferential Statistics

In this tutorial you will learn in detail about continuous probability distribution. Understand the properties of a continuous random variable and its distribution under normal conditions. To identify the properties of a continuous random variable, statisticians have defined a variable as a standard, learning the properties of the standard variable and its distribution. You will learn to check if a continuous random variable is following normal distribution using a normal Q-Q plot. Learn the science behind the estimation of value for a population using sample data.

Random Variable and its definition

Probability & Probability Distribution

Continuous Probability Distribution / Probability Density Function

Discrete Probability Distribution / Probability Mass Function

Normal Distribution

Standard Normal Distribution / Z distribution

Z scores and the Z table

QQ Plot / Quantile - Quantile plot

Sampling Variation

Central Limit Theorem

Sample size calculator

Confidence interval - concept

Confidence interval with sigma

T-distribution / Student's-t distribution

Confidence interval

Population parameter with Standard deviation known

Population parameter with Standard deviation not known

A complete recap of Statistics

Learn to frame business statements by making assumptions. Understand how to perform testing of these assumptions to make decisions for business problems. Learn about different types of Hypothesis testing and its statistics. You will learn the different conditions of the Hypothesis table, namely Null Hypothesis, Alternative hypothesis, Type I error and Type II error. The prerequisites for conducting a Hypothesis test, interpretation of the results will be discussed in this module.

Formulating a Hypothesis

Choosing Null and Alternative Hypothesis

Type I or Alpha Error and Type II or Beta Error

Confidence Level, Significance Level, Power of Test

Comparative study of sample proportions using Hypothesis testing

2 Sample t-test

ANOVA

2 Proportion test

Chi-Square test

Learn about insights on how data is assisting organizations to make informed data-driven decisions. Data is treated as the new oil for all the industries and sectors which keep organizations ahead in the competition. Learn the application of Big Data Analytics in real-time, you will understand the need for analytics with a use case. Also, learn about the best project management methodology for Data Mining - CRISP-DM at a high level.

All About Data2bussinessinsights.in

Dos and Don'ts as a participant

Introduction to Big Data Analytics

Data and its uses – a case study (Grocery store)

Interactive marketing using data & IoT – A case study

Course outline, road map, and takeaways from the course

Stages of Analytics - Descriptive, Predictive, Prescriptive, etc.

Cross-Industry Standard Process for Data Mining

Data Mining supervised learning is all about making predictions for an unknown dependent variable using mathematical equations explaining the relationship with independent variables. Revisit the school math with the equation of a straight line. Learn about the components of Linear Regression with the equation of the regression line. Get introduced to Linear Regression analysis with a use case for prediction of a continuous dependent variable. Understand about ordinary least squares technique.

Scatter diagram

Correlation analysis

Correlation coefficient

Ordinary least squares

Principles of regression

Simple Linear Regression

Exponential Regression, Logarithmic Regression, Quadratic or Polynomial Regression

Confidence Interval versus Prediction Interval

Heteroscedasticity / Equal Variance

In the continuation to Regression analysis study you will learn how to deal with multiple independent variables affecting the dependent variable. Learn about the conditions and assumptions to perform linear regression analysis and the workarounds used to follow the conditions. Understand the steps required to perform the evaluation of the model and to improvise the prediction accuracies. You will be introduced to concepts of variance and bias.

LINE assumption

Linearity

Independence

Normality

Equal Variance / Homoscedasticity

Collinearity (Variance Inflation Factor)

Multiple Linear Regression

Model Quality metrics

Deletion Diagnostics

Learn about overfitting and underfitting conditions for prediction models developed. We need to strike the right balance between overfitting and underfitting, learn about regularization techniques L1 norm and L2 norm used to reduce these abnormal conditions. The regression techniques Lasso and Ridge techniques are discussed in this module .

Understanding Overfitting (Variance) vs. Underfitting (Bias)

Generalization error and Regularization techniques

Different Error functions or Loss functions or Cost functions

Lasso Regression

Ridge Regression

You have learnt about predicting a continuous dependent variable. As part of this module, you will continue to learn Regression techniques applied to predict attribute Data. Learn about the principles of the logistic regression model, understand the sigmoid curve, the usage of cutoff value to interpret the probable outcome of the logistic regression model. Learn about the confusion matrix and its parameters to evaluate the outcome of the prediction model. Also, learn about maximum likelihood estimation.

Extension to logistic regression We have a multinomial regression technique used to predict a multiple categorical outcome. Understand the concept of multi logit equations, baseline and making classifications using probability outcomes. Learn about handling multiple categories in output variables including nominal as well as ordinal data.

Logit and Log-Likelihood

Category Baselining

Modeling Nominal categorical data

Handling Ordinal Categorical Data

Interpreting the results of coefficient values

As part of this module you learn further different regression techniques used for predicting discrete data. These regression techniques are used to analyze the numeric data known as count data. Based on the discrete probability distributions namely Poisson, negative binomial distribution the regression models try to fit the data to these distributions. Alternatively, when excessive zeros exist in the dependent variable, zero-inflated models are preferred, you will learn the types of zero-inflated models used to fit excessive zeros data.

Poisson Regression

Poisson Regression with Offset

Negative Binomial Regression

Treatment of data with Excessive Zeros

Zero-inflated Poisson

Zero-inflated Negative Binomial

Hurdle Model

k Nearest Neighbor algorithm is distance based machine learning algorithm. Learn to classify the dependent variable using the appropriate k value. The k-NN classifier also known as lazy learner is a very popular algorithm and one of the easiest for application.

Deciding the K value

Thumb rule in choosing the K value

Building a KNN model by splitting the data

Checking for Underfitting and Overfitting in KNN

Generalization and Regulation Techniques to avoid overfitting in KNN

Decision Tree & Random forest are some of the most powerful classifier algorithms based on classification rules. In this tutorial, you will learn about deriving the rules for classifying the dependent variable by constructing the best tree using statistical measures to capture the information from each of the attributes. Random forest is an ensemble technique constructed using multiple Decision trees and the final outcome is drawn from the aggregating the results obtained from these combinations of trees.

Elements of classification tree - Root node, Child Node, Leaf Node, etc.

Greedy algorithm

Measure of Entropy

Attribute selection using Information gain

Ensemble techniques - Stacking, Boosting and Bagging

Decision Tree C5.0 and understanding various arguments

Checking for Underfitting and Overfitting in Decision Tree

Generalization and Regulation Techniques to avoid overfitting in Decision Tree

Random Forest and understanding various arguments

Checking for Underfitting and Overfitting in Random Forest

Generalization and Regulation Techniques to avoid overfitting in Random Forest

Learn about improving reliability and accuracy of decision tree models using ensemble techniques. Bagging and Boosting are the go to techniques in ensemble techniques. The parallel and sequential approaches taken in Bagging and Boosting methods are discussed in this module.

Overfitting

Underfitting

Pruning

Boosting

Bagging or Bootstrap aggregating

The Boosting algorithms AdaBoost and Extreme Gradient Boosting are discussed as part of this continuation module You will also learn about stacking methods. Learn about these algorithms which are providing unprecedented accuracy and helping many aspiring data scientists win the first place in various competitions such as Kaggle, CrowdAnalytix, etc.

AdaBoost / Adaptive Boosting Algorithm

Checking for Underfitting and Overfitting in AdaBoost

Generalization and Regulation Techniques to avoid overfitting in AdaBoost

Gradient Boosting Algorithm<

Checking for Underfitting and Overfitting in Gradient Boosting

Generalization and Regulation Techniques to avoid overfitting in Gradient Boosting

Extreme Gradient Boosting (XGB) Algorithm

Checking for Underfitting and Overfitting in XGB

Generalization and Regulation Techniques to avoid overfitting in XGB

Learn to analyse the unstructured textual data to derive meaningful insights. Understand the language quirks to perform data cleansing, extract features using a bag of words and construct the key-value pair matrix called DTM. Learn to understand the sentiment of customers from their feedback to take appropriate actions. Advanced concepts of text mining will also be discussed which help to interpret the context of the raw text data. Topic models using LDA algorithm, emotion mining using lexicons are discussed as part of NLP module.

Sources of data

Bag of words

Pre-processing, corpus Document Term Matrix (DTM) & TDM

Revise Bayes theorem to develop a classification technique for Machine learning. In this tutorial you will learn about joint probability and its applications. Learn how to predict whether an incoming email is a spam or a ham email. Learn about Bayesian probability and the applications in solving complex business problems.

Probability – Recap

Bayes Rule

Naïve Bayes Classifier

Text Classification using Naive Bayes

Checking for Underfitting and Overfitting in Naive Bayes

Generalization and Regulation Techniques to avoid overfitting in Naive Bayes

Perceptron algorithm is defined based on a biological brain model. You will talk about the parameters used in the perceptron algorithm which is the foundation of developing much complex neural network models for AI applications. Understand the application of perceptron algorithms to classify binary data in a linearly separable scenario.

Neurons of a Biological Brain

Artificial Neuron

Perceptron

Perceptron Algorithm

Use case to classify a linearly separable data

Multilayer Perceptron to handle non-linear data

Neural Network is a black box technique used for deep learning models. Learn the logic of training and weights calculations using various parameters and their tuning. Understand the activation function and integration functions used in developing a neural network.

Principles of Gradient Descent (Manual Calculation)

Learning Rate (eta)

Batch Gradient Descent

Stochastic Gradient Descent

Minibatch Stochastic Gradient Descent

Optimization Methods: Adagrad, Adadelta, RMSprop, Adam

Convolution Neural Network (CNN)

ImageNet Challenge – Winning Architectures

Parameter Explosion with MLPs

Convolution Networks

Recurrent Neural Network

Language Models

Traditional Language Model

Disadvantages of MLP

Back Propagation Through Time

Long Short-Term Memory (LSTM)

Gated Recurrent Network (GRU)

Support Vector Machines / Large-Margin / Max-Margin Classifier

Hyperplanes

Best Fit "boundary"

Linear Support Vector Machine using Maximum Margin

SVM for Noisy Data

Non- Linear Space Classification

Non-Linear Kernel Tricks

Linear Kernel

Polynomial

Sigmoid

Gaussian RBF

SVM for Multi-Class Classification

One vs. All

One vs. One

Directed Acyclic Graph (DAG) SVM

Data mining unsupervised techniques are used as EDA techniques to derive insights from the business data. In this first module of unsupervised learning, get introduced to clustering algorithms. Learn about different approaches for data segregation to create homogeneous groups of data. Hierarchical clustering, K means clustering are most commonly used clustering algorithms. Understand the different mathematical approaches to perform data segregation. Also learn about variations in K-means clustering like K-medoids, K-mode techniques, learn to handle large data sets using CLARA technique.

• Hierarchical • Supervised vs Unsupervised learning • Data Mining Process • Hierarchical Clustering / Agglomerative Clustering • Dendrogram • 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

K-Means Clustering

Measurement metrics of clustering

Within the Sum of Squares

Between the Sum of Squares

Total Sum of Squares

Choosing the ideal K value using Scree Plot / Elbow Curve

Other Clustering Techniques

K-Medians

K-Medoids

K-Modes

Clustering Large Application (CLARA)

Partitioning Around Medoids (PAM)

Density-based spatial clustering of applications with noise (DBSCAN)

Dimension Reduction (PCA) / Factor Analysis Description: Learn to handle high dimensional data. The performance will be hit when the data has a high number of dimensions and machine learning techniques training becomes very complex, as part of this module you will learn to apply data reduction techniques without any variable deletion. Learn the advantages of dimensional reduction techniques. Also, learn about yet another technique called Factor Analysis.

Why Dimension Reduction

Advantages of PCA

Calculation of PCA weights

2D Visualization using Principal components

Basics of Matrix Algebra

Factor Analysis

Learn to measure the relationship between entities. Bundle offers are defined based on this measure of dependency between products. Understand the metrics Support, Confidence and Lift used to define the rules with the help of Apriori algorithm. Learn pros and cons of each of the metrics used in Association rules.

What is Market Basket / Affinity Analysis

Measure of Association

Support

Confidence

Lift Ratio

Apriori Algorithm

Sequential Pattern Mining

Personalized recommendations made in e-commerce are based on all the previous transactions made. Learn the science of making these recommendations using measuring similarity between customers. The various methods applied for collaborative filtering, their pros and cons, SVD method used for recommendations of movies by Netflix will be discussed as part of this module.

User-based Collaborative Filtering

A measure of distance/similarity between users

Driver for Recommendation

Computation Reduction Techniques

Search based methods/Item to Item Collaborative Filtering

SVD in recommendation

The vulnerability of recommendation systems

Study of a network with quantifiable values is known as network analytics. The vertex and edge are the node and connection of a network, learn about the statistics used to calculate the value of each node in the network. You will also learn about the google page ranking algorithm as part of this module.

Definition of a network (the LinkedIn analogy)

The measure of Node strength in a Network

Degree centrality

Closeness centrality

Eigenvector centrality

Adjacency matrix

Betweenness centrality

Cluster coefficient

Introduction to Google page ranking

AutoML Methods

AutoML Systems

AutoML on Cloud - AWS

Amazon SageMaker

Sagaemaker Notebook Instance for Model Development, Training and

Deployment

XG Boost Classification Model

Hyperparameter tuning jobs

AutoML on Cloud - Azure

Workspace

Environment

Compute Instance

Automatic Featurization

AutoML and ONNX

AutoML on Cloud - GCP

AutoML Natural Language Performing Document Classification

Performing Sentiment Analysis using AutoML Natural Language API

Cloud ML Engine and Its Components

Training and Deploying Applications on Cloud ML Engine

Choosing Right Cloud ML Engine for Training Jobs

Kaplan Meier method and life tables are used to estimate the time before the event occurs. Survival analysis is about analyzing this duration or time before the event. Real-time applications of survival analysis in customer churn, medical sciences and other sectors is discussed as part of this module. Learn how survival analysis techniques can be used to understand the effect of the features on the event using Kaplan Meier survival plot.

Examples of Survival Analysis

Time to event

Censoring

Survival, Hazard, Cumulative Hazard Functions

Introduction to Parametric and non-parametric functions

Time series analysis is performed on the data which is collected with respect to time. The response variable is affected by time. Understand the time series components, Level, Trend, Seasonality, Noise and methods to identify them in a time series data. The different forecasting methods available to handle the estimation of the response variable based on the condition of whether the past is equal to the future or not will be introduced in this module. In this first module of forecasting, you will learn the application of Model-based forecasting techniques.

Introduction to time series data

Steps to forecasting

Components to time series data

Scatter plot and Time Plot

Lag Plot

ACF - Auto-Correlation Function / Correlogram

Visualization principles

Naïve forecast methods

Errors in the forecast and it metrics - ME, MAD, MSE, RMSE, MPE, MAPE

Model-Based approaches

Linear Model

Exponential Model

Quadratic Model

Additive Seasonality

Multiplicative Seasonality

Model-Based approaches Continued

AR (Auto-Regressive) model for errors

Random walk

In this continuation module of forecasting learn about data-driven forecasting techniques. Learn about ARMA and ARIMA models which combine model-based and data-driven techniques. Understand the smoothing techniques and variations of these techniques. Get introduced to the concept of de-trending and deseasonalize the data to make it stationary. You will learn about seasonal index calculations which are used for reseasonalize the result obtained by smoothing models.

ARMA (Auto-Regressive Moving Average), Order p and q

ARIMA (Auto-Regressive Integrated Moving Average), Order p, d, and q

A data-driven approach to forecasting

Smoothing techniques

Moving Average

Exponential Smoothing

Holt's / Double Exponential Smoothing

Winters / Holt-Winters

De-seasoning and de-trending

Econometric Models

Forecasting using Python

Forecasting using R

This course will be the first stepping stone towards Artificial Intelligence and Deep Learning. In this module, you will be introduced to the analytics programming languages. R is a statistical programming language and Python is a general-purpose programming language. These are the most popular tools currently being employed to churn data for deriving meaningful insights.

All About Data2bussinessinsights.in

Dos and Don'ts as a Participant

Introduction to Artificial intelligence and Deep learning

Course Outline, Road Map and Takeaways from the Course

Cross-Industry Standard Process for Data Mining

Artificial Intelligence Applications

Different packages can be used to build Deep Learning and Artificial Intelligence models, such as Tensorflow, Keras, OpenCV, and PyTorch. You will learn more about these packages and their applications in detail.

Tensorflow and Keras libraries can be used to build Machine Learning and Deep Learning models. OpenCV is used for image processing and PyTorch is highly useful when you have no idea how much memory will be required for creating a Neural Network Model.

Introduction to Deep Learning libraries – Torch, Theono, Caffe, Tensorflow, Keras, OpenCV and PyTorch

Deep dive into Tensorflow, Keras, OpenCV and PyTorch

Introduction to Anaconda, R, R studio, Jupyter and Spyder

Environment Setup and Installation Methods of Multiple Packages

Understand the types of Machine Learning Algorithms. Learn about the life cycle and the detailed understanding of each step involved in the project life cycle. The CRISP-DM process is applied in general for Data Analytics /AI projects. Learn about CRISP-DM and the stages of the project life cycle in-depth.

You will also learn different types of data, Data Collection, Data Preparation, Data Cleansing, Feature Engineering, EDA, Data Mining and various Error Functions. Understand about imbalanced data handling techniques and algorithms.

Introduction to Machine Learning

Machine Learning and its types - Supervised Learning, Unsupervised Learning, Reinforcement Learning, Semi-supervised Learning, Active Learning, Transfer Learning, Structured Prediction

Understand Business Problem – Business Objective & Business Constraints

Data Collection - Surveys and Design of Experiments

Data Types namely Continuous, Discrete, Categorical, Count, Qualitative, Quantitative and its identification and application

Further classification of data in terms of Nominal, Ordinal, Interval & Ratio types

Balanced vs Imbalanced datasets

Cross-Sectional vs Time Series versus Panel / Longitudinal Data

Batch Processing versus Real-Time Processing

Structured vs Unstructured vs Semi-Structured Data

Error Functions - Y is Continuous - Mean Error, Mean Absolute Deviation, Mean Squared Error, Mean Percentage Error, Root Mean Squared Error, Mean Absolute Percentage Error

Error Functions - Y is Discrete - Cross Table, Confusion Matrix, Binary Cross Entropy & Categorical Cross Entropy

Machine Learning Projects Strategy

Maximize or minimize the error rate using Calculus. Learn to find the best fit line using the linear least-squares method. Understand the gradient method to find the minimum value of a function where a closed-form of the solution is not available or not easily obtained.

Under Linear Algebra, you will learn sets, function, scalar, vector, matrix, tensor, basic operations and different matrix operations. Under Probability one will learn about Uniform Distribution, Normal Distribution, Binomial Distribution, Discrete Random Variable, Cumulative Distribution Function and Continuous Random Variables.

Optimizations - Applications

Foundations - Slope, Derivatives & Tangent

Derivatives in Optimization

Maxima & Minima - First Derivative Test, Second Derivative Test, Partial Derivatives, Cross Partial Derivatives, Saddle Point, Determinants, Minor and Cofactor

Linear Regression Ordinary Least Squares using Calculus

You will have a high level understanding of the human brain, importance of multiple layers in the Neural Network, extraction of features layers wise, composition of the data in Deep Learning using an image, speech and text.

You will briefly understand feature extraction using SIFT/HOG for images, Speech recognition and feature extraction using MFCC and NLP feature extraction using parse tree syntactic.

Introduction to neurons, which are connected to weighted inputs, threshold values, and an output. You will understand the importance of weights, bias, summation and activation functions.

Human Brain – Introduction to Biological & Artificial Neuron

Compositionality in Data – Images, Speech & text

Mathematical Notations

Introduction to ANN

Neuron, Weights, Activation function, Integration function, Bias and Output

Learn about single-layered Perceptrons, Rosenblatt’s perceptron for weights and bias updation. You will understand the importance of learning rate and error. Walk through a toy example to understand the perceptron algorithm. Learn about the quadratic and spherical summation functions. Weights updating methods - Windrow-Hoff Learning Rule & Rosenblatt’s Perceptron.

Network Topology – Key characteristics and Number of layers

Weights Calculation in Back Propagation

Understand the difference between perception and MLP or ANN. Learn about error surface, challenges related to gradient descent and the practical issues related to deep learning. You will learn the implementation of MLP on MNIST dataset - multi class problem, IMDB dataset - binary classification problem, Reuters dataset - single labelled multi class classification problem and Boston Housing dataset - Regression Problem using Python and Keras.

Error Surface – Learning Rate & Random Weight Initialization

Local Minima issues in Gradient Descent Learning

Is DL a Holy Grail? Pros and Cons

Practical Implementation of MLP/ANN in Python using Real Life Use Cases

Deep Learning Practical Issues – Avoid Overfitting, DropOut, DropConnect, Noise, Data Augmentation, Parameter Choices, Weights Initialization (Xavier, etc.)

Convolution Neural Networks are the class of Deep Learning networks which are mostly applied on images. You will learn about ImageNet challenge, overview on ImageNet winning architectures, applications of CNN, problems of MLP with huge dataset.

You will understand convolution of filter on images, basic structure on convent, details about Convolution layer, Pooling layer, Fully Connected layer, Case study of AlexNet and few of the practical issues of CNN.

Convolution Layers with Filters and Visualizing Convolution Layers

Pooling Layer, Padding, Stride

Transfer Learning - VGG16, VGG19, Resnet, GoogleNet, LeNet, etc.

Practical Issues – Weight decay, Drop Connect, Data Manipulation Techniques & Batch Normalization

You will learn image processing techniques, noise reduction using moving average methods, different types of filters - smoothing the image by averaging, Gaussian filter and the disadvantages of correlation filters. You will learn about different types of filters, boundary effects, template matching, rate of change in the intensity detection, different types of noise, image sampling and interpolation techniques.

You will also learn about colors and intensity, affine transformation, projective transformation, embossing, erosion & dilation, vignette, histogram equalization, HAAR cascade for object detection, SIFT, SURF, FAST, BRIEF and seam carving.

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

Understand the language models for next word prediction, spell check, mobile auto-correct, speech recognition, and machine translation. You will learn the disadvantages of traditional models and MLP. Deep understanding of the architecture of RNN, RNN language model, backpropagation through time, types of RNN - one to one, one to many, many to one and many to many along with different examples for each type.

Introduction to Adversaries

Language Models – Next Word Prediction, Spell Checkers, Mobile Auto-Correction, Speech Recognition & Machine Translation

Traditional Language model

Disadvantages of MLP

Introduction to State & RNN cell

Introduction to RNN

RNN language Models

Back Propagation Through time

RNN Loss Computation

Types of RNN – One to One, One to Many, Many to One, Many to Many

Introduction to the CNN and RNN

Combining CNN and RNN for Image Captioning

Architecture of CNN and RNN for Image Captioning

Bidirectional RNN

Deep Bidirectional RNN

Disadvantages of RNN

You will learn to build an object detection model using Fast R-CNN by using bounding boxes, understand why fast RCNN is a better choice while dealing with object detection. You will also learn by instance segmentation problems which can be avoided using Mask RCNN.

CNN-RNN Variants

R-CNN

Fast R-CNN

Faster R-CNN

Mask R-CNN

Understand and implement Long Short-Term Memory, which is used to keep the information intact, unless the input makes them forget. You will also learn the components of LSTM - cell state, forget gate, input gate and the output gate along with the steps to process the information. Learn the difference between RNN and LSTM, Deep RNN and Deep LSTM and different terminologies. You will apply LSTM to build models for prediction.

Introduction to LSTM – Architecture

Importance of Cell State, Input Gate, Output Gate, Forget Gate, Sigmoid and Tanh

Mathematical Calculations to Process Data in LSTM

RNN vs LSTM - Bidirectional vs Deep Bidirectional RNN

Deep RNN vs Deep LSTM

Gated Recurrent Unit, a variant of LSTM solves this problem in RNN. You will learn the components of GRU and the steps to process the information.

Introduction to GRU

Architecture & Gates - Update Gate, Reset Gate, Current Memory Content, Final Memory at Current Timestep

Applications of GRUs

You will learn about the components of Autoencoders, steps used to train the autoencoders to generate spatial vectors, types of autoencoders and generation of data using variational autoencoders. Understanding the architecture of RBM and the process involved in it.

Autoencoders

Intuition

Comparison with other Encoders (MP3 and JPEG)

Implementation in Keras

Deep AutoEncoders

Intuition

Implementing DAE in Keras

Convolutional Autoencoders

Intuition

Implementation in Keras

Variational Autoencoders

IntuitionImplementation in Keras

Introduction to Restricted Boltzmann Machines - Energy Function, Schematic implementation, Implementation in TensorFlow

You will learn the difference between CNN and DBN, architecture of deep belief networks, how greedy learning algorithms are used for training them and applications of DBN.

Introduction to DBN

Architecture of DBN

Applications of DBN

DBN in Real World

Understanding the generation of data using GAN, the architecture of the GAN - encoder and decoder, loss calculation and backpropagation, advantages and disadvantages of GAN.

Introduction to Generative Adversarial Networks (GANS)

Data Analysis and Pre-Processing

Building Model

Model Inputs and Hyperparameters

Model losses

Implementation of GANs

Defining the Generator and Discriminator

Generator Samples from Training

Model Optimizer

Discriminator and Generator Losses

Sampling from the Generator

Advanced Applications of GANS

Pix2pixHD

CycleGAN

StackGAN++ (Generation of photo-realistic images)

GANs for 3D data synthesis

Speech quality enhancement with SEGAN

You will learn to use SRGAN which uses the GAN to produce the high-resolution images from the low-resolution images. Understand about generators and discriminators.

Introduction to SRGAN

Network Architecture - Generator, Discriminator

Loss Function - Discriminator Loss & Generator Loss

Implementation of SRGAN in Keras

You will learn Q-learning which is a type of reinforcement learning, exploiting using the creation of a Q table, randomly selecting an action using exploring and steps involved in learning a task by itself.

Reinforcement Learning

Deep Reinforcement Learning vs Atari Games

Maximizing Future Rewards

Policy vs Values Learning

Balancing Exploration With Exploitation

Experience Replay, or the Value of Experience

Q-Learning and Deep Q-Network as a Q-Function

Improving and Moving Beyond DQN

Keras Deep Q-Network

Learn to Build a speech to text and text to speech models. You will understand the steps to extract the structured speech data from a speech, convert that into text. Later use the unstructured text data to convert into speech.

Speech Recognition Pipeline

Phonemes

Pre-Processing

Acoustic Model

Deep Learning Models

Decoding

Learn to Build a chatbot using generative models and retrieval models. We will understand RASA open-source and LSTM to build chatbots.

Introduction to Chatbot

NLP Implementation in Chatbot

Integrating and implementing Neural Networks Chatbot

Introduction to Sequence to Sequence models and Attention

Learn the tools which automatically analyzes your data and generates candidate model pipelines customized for your predictive modeling problem.

AutoML Methods

Meta-Learning

Hyperparameter Optimization

Neural Architecture Search

Network Architecture Search

AutoML Systems

MLBox

Auto-Net 1.0 & 2.0

Hyperas

AutoML on Cloud - AWS

Amazon SageMaker

Sagemaker Notebook Instance for Model Development, Training and Deployment

XG Boost Classification Model

Training Jobs

Hyperparameter Tuning Jobs

AutoML on Cloud - Azure

Workspace

Environment

Compute Instance

Compute Targets

Automatic Featurization

AutoML and ONNX

AutoML on Cloud - GCP

AutoML Natural Language

Performing Document Classification

AutoML Version API's for Image Classification

Performing Sentiment Analysis using AutoML Natural Language API

Tensor-Flow Models Using Cloud ML Engine

Cloud ML Engine and Its Components

Training and Deploying Applications on Cloud ML Engine

Choosing Right Cloud ML Engine for Training Jobs

Learn the methods and techniques which can explain the results and the solutions obtained by using deep learning algorithms.

Introduction to XAI - Explainable Artificial Intelligence

Why do we need it?

Levels of Explainability

Direct Explainability

Simulatability

Decomposability

Algorithmic Transparency

Post-hoc Explainability

Model-Agnostic Algorithms

Explanation by simplification (Local Interpretable Model-Agnostic Explanations (LIME))

Feature relevance explanation

SHAP

QII

SA

ASTRID

XAI

Visual Explanations

General AI vs Symbolic Al vs Deep Learning

GET AHEAD WITH DEEPNEURON CERTIFICATE

Earn your certificate

Our Data Science Master Program program is exhaustive and this certificate is proof that you have taken a big leap in mastering the domain.

Differentiate yourself with a Data Science Master Program Certificate

The knowledge and Data Science Master Program skills you've gained working on projects, simulations, case studies will set you ahead of the competition.

Share your achievement

Talk about it on Linkedin, Twitter, Facebook, boost your resume, or frame it - tell your friends and colleagues about it.

DeepNeuron Testimonials

-Anand Shyam,Data Scientist

"Am proud to be one of your students.I learnt Big data, Data Science and thoroughly enough.Here every session was great and very valuable. It was extremely useful and we... learnt data science concept more than what I expected."

-Y.Naidu,Project Manager

"The hands-on training sessions were very good.Your excellent explanation on statistics super....Great job with your ppts!.You have covered most of the algorithms which were never covered even by reputed institutions."

-Bhuvanesh,Senior Data Scientist

"The Training Which I Underwent From Sathya Who Is In This Industry For Long Is The Best....He Teaches Things Practically And How To Apply The Techniques Which Is Most Important , Its Worth Everything."

-Boobathy,Project Manager

"Done An Awesome Job. Am thoroughly Satisfied With The Training Provided By Sasken(A) Sathya....End of the day I felt like "I am data scientist".I recommend whoever would like to be a Big Data Analytics/ Data Scientist."

-Shiva Project Manager, CTS

I have never seen such an excellent faculty like Mr.sasken who is the best faculty . I feel very lucky and blessed to be a student ...of sasken , I would recommend anybody who has a passion..

-ThulasiDass,CSC ,Chennai

"without doubt that his (Sasken (a) Sathya) training is the best compared to others. The way he explains statistical concepts in the intuitive way is very good, so that a person without any statistics background can understand and remember the terminology very easily....The training covered all of my expectations and helping me in the process of changing my carrier from legacy based technical skill sets to emerging hot technical sill sets The content of the material, code’s(R, Python& SAS) and datasets he uses to explain various machine learning algorithms are the best compared other training institutes/trainers.

-Babu Lakshman -Cognizant

"The course is a combination of various data science concepts such as machine learning, visualization, data mining, programming, data munging. It gives hands on experience with statistical modelling techniques. The course has real world examples of how...analytics have been used to significantly improve business performance. Apart from machine learning the course introduces to map reduce, spark and Hadoop ecosystems, bias vs variance, learning curves, etc. The course gives a good understanding of R and also good introduction to Python and SAS."

-Vijay , HCL

"The training was very perfect, as the teaching approach was very clear and the pace is slow, The way the faculty explain each and every topics in detail with simples examples helped me to understand ...even the complex algorithms. The training material is really good and helpful."

FAQs

What is Data Science course?

Data Science is a broad field and you need to learn about so many concepts if you are a beginner. A Data Science course is a training program of around six to twelve months, often taken by industry experts to help candidates build a strong foundation in the field. Apart from the theoretical material, an online data science course includes virtual labs, industry projects, interactive quizzes, and practice tests which can give you enhanced learning experience.

Will this course help me to learn Data Science from scratch?

Professionals who do not have any prior knowledge of the field can easily begin with this Data Scientist Master’s program as you’ll gain a thorough knowledge of the basic concepts as well.

What is the total duration of this Data Science course?

The Data Science course can be completed in around six months if you dedicate a few hours daily to learning.

How do I become a Data Scientist?

This course co-developed with IBM will give you an insight into Data Science tools and methodologies, which is enough to prepare you to excel in your next role as a Data Scientist. You will earn an industry-recognized certificate from IBM and data2businessinsights.inThis course co-developed with IBM will give you an insight into Data Science tools and methodologies, which is enough to prepare you to excel in your next role as a Data Scientist. You will earn an industry-recognized certificate from IBM and data2businessinsights.in that will attest to your new skills and on-the-job expertise. The program will train you on R and Python, Machine Learning techniques, data reprocessing, regression, clustering, data analytics with SAS, data visualization with Tableau, and overview of the Hadoop ecosystem. that will attest to your new skills and on-the-job expertise. The program will train you on R and Python, Machine Learning techniques, data reprocessing, regression, clustering, data analytics with SAS, data visualization with Tableau, and overview of the Hadoop ecosystem.

What are the top online Data Science courses provided in this Master's program?

This comprehensive Data Scientist Master’s program incorporates the following courses:

Data Science with R Programming

Data Science with Python

Machine Learning

Tableau Training

Big Data Hadoop and Spark Developer

Data Science Capstone

What can I expect from this deepneuron.in Data Science course developed in collaboration with IBM?

As a part of this Data Science online course, in collaboration with IBM, you will receive the following:

Lifetime access to e-learning course syllabus for all of the Data Science courses included in the learning path (*only for courses)

Industry-recognized certificates from IBM*(for IBM courses) and Simplilearn upon successful completion of the program

USD 1200 worth of IBM cloud credits that you can leverage for hands-on exposure

Access to IBM cloud platforms featuring IBM Watson and other software for 24/7 practice

*For which all courses will I get certificates from IBM?

Following are the list of courses for which you will get IBM certificates:

R Programming for Data Science

Python for Data Science

How do I earn the Master’s certificate?

Upon completion of the following minimum requirements, you will be eligible to receive the Data Scientist Master’s certificate that will testify to your skills as an expert in Data Science.

Course

Course completion certificate

Criteria

Data Science and Statistics Fundamentals

Required

85% of Online Self-paced completion

Data Science with R

Required

85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation in at least 1 project

Data Science with SAS

Required

85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation in at least 1 project

Data Science with Python

Required

85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in course-end assessment and successful evaluation in at least 1 project

Machine Learning and Tableau

Required

85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and successful evaluation in at least 1 project

Big Data Hadoop and Spark Developer

Required

85% of Online Self-paced completion or attendance of 1 Live Virtual Classroom, and score above 75% in the course-end assessment, and successful evaluation of at least 1 project

Capstone Project

Required

Attendance of 1 Live Virtual Classroom and successful completion of the capstone project

How do I enroll for the Data Scientist course?

You can enroll in this Data Science training on our website and make an online payment using any of the following options:

Visa Credit or Debit Card

MasterCard

American Express

Diner’s Club

PayPal

Once payment is received you will automatically receive a payment receipt and access information via email.

If I need to cancel my enrollment, can I get a refund?

Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, please read our Refund Policy.

I am not able to access the online Data Science courses. Who can help me?

Yes, we do offer a money-back guarantee for many of our training programs. Refer to our Refund Policy and submit refund requests via our Help and Support. portal.

Who are the instructors and how are they selected?

All of our highly qualified Data Science trainers are industry experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.

What is Global Teaching Assistance?

Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.

What is covered under the 24/7 Support promise?

We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.

Does Deep Neuron provide practice exams related to this Data Scientist course?

Yes, we offer the following Data Science associated practice tests –