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
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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 –