Software Automation Testing and Devops

5918 Ratings
5/5

21059 Learners

Radical Data Science PG program consists of Practical learning  of Data Science with Python & R, Apache Spark & Scala, AI & Deep Learning with TensorFlow, Tableau. Consists of 25 Projects | 200 + Assignments and 50 hours of Mock interview session. You will acquire a knowledge of 4+ year experienced data scientist.

This program is good for freshers and up to 3 – 4 years of industry experienced professional   . Those who have career gap

Avg. salary of a Data Scientists is goes to 20 Lakhs per annum

Major courses are Data Science with Python & R, Apache Spark & Scala, AI & Deep Learning with TensorFlow ,Tableau  Along with this 30 Plus tools are covered as part of this training

Overview of tools covered

Machine Learning, K-Means Clustering, Decision Trees, Data Mining, Python Libraries, Statistics, Scala, Spark Streaming, RDDs, MLlib, Regression Modelling , Scala Programming language, HDFS, Sqoop, FLume, Spark GraphX, Apache Cassandra, Spark, and Kafka

0 +
Students Empowered

Online / Classroom

Format

12 months

Recommended 12-15 hrs/week

0 +
Hiring Partners

Every Month 1st

Start Date

Certified By

OTHM–UK Recognised by ofqual.gov.uk

About the Program

Project Driven industry mentorship, dedicated career support, learn 30 + programming tools . Expertise in Software languages with multiple assignments .Dedicated Trainers with ample of Industry Experience . Project based IT Training and Certification programs . Certified from register.ofqual.gov.uk . Provide Level 7 Certification , which is equal to Master’s program in the rest of the world . Word Wide recognised certification from UK.

About Ofqual Qualifications – The Office of Qualifications and Examinations Regulation (Ofqual) regulates qualifications, examinations and assessments in England. 

Program Overview

Key Highlights

  • Designed for Freshers to Working Professionals
  • Eligible for Ofqual regulates qualifications – UK Certified and World Recognised programs
  • 30+ Programming Tools & Languages
  • 30+ Industry Projects , 100 + Live assignments , 150+ coding Solutions.
  • Ofqual - UK validated PG Diploma from UK
  • 360 Degree Career Support
  • One-on-One with Industry Mentors
  • Dedicated Student Mentor
  • Job Assistance with Top Firms
  • No Cost EMI Option

Top Skills You Will Learn

Predictive Analytics using Python, Machine Learning, Data Visualization, Big Data, Natural Language Processing

Job Opportunities

Data Analyst, Data Scientist, Data Engineer, Product Analyst, Machine Learning Engineer, Decision Scientist

Who Is This Program For?

Engineers, Marketing & Sales Professionals, Freshers, Domain Experts, Software & IT Professionals , Those who having career gap , those who do not completed Degree .

Minimum Eligibility

Any Bachelor’s degree. Completed or not completed . No coding experience required . If you have any Educational Gap or any other career gap , you can do this program to boost up your career . The qualifications provided by UK Regulatory board is equal to Level 7 Masters Degree .

Programming Languages and Tools Covered .

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INTERNATIONAL POSTGRADUATE DIPLOMA FROM othm

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INTERNATIONAL POSTGRADUATE DIPLOMA FROM othm

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How You Benefit From This Program

  • Post Graduate Diploma without Spedning full time in College
  • Eligible for Ofqual regulates qualifications – UK Certified and World Recognised programs
  • Level 7 Program recognised world wide for your Higher studies
  • Get recognised by High Value world recognised UK equal Master Degree

COURSE RELATED FAQs

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Our Process

Training
Work On Industry
Scenarios
(60+ )
Do Major Projects
( 8+ )
Google Data Engineer Certification Preparation
Register into Radical HR Portal - Start Interview
Complete Live
Assignments
( 150+ )
Do Mini Projects
( 25 + )
Give Mock Interviews
Profile Creation

Course Curriculum

Python Statistics for Data Science Course Classroom / Online​

Python Scripting allows programmers to build applications easily and rapidly. This course is an introduction to Python scripting, which focuses on the concepts of Python, it will help you to perform operations on variable types using Pycharm. You will learn the importance of Python in real time environment and will be able to develop applications based on Object Oriented Programming concept. End of this course, you will be able to develop networking applications with suitable GUI

Python Statistics for Data Science Course

Python Scripting allows programmers to build applications easily and rapidly. This course is an introduction to Python scripting, which focuses on the concepts of Python, it will help you to perform operations on variable types using Pycharm.

Goal: In this module, you will be introduced to data and its types and accordingly sample data and derive meaningful information from the data in terms different statistical parameters.

Objectives: At the end of this Module, you should be able to:
Understand various data types
Learn Various variable types
List the uses of variable types
Explain Population and Sample
Discuss sampling techniques
Understand Data representation

Topics:
Introduction to Data Types
Numerical parameters to represent data
Mean
Mode
Median
Sensitivity
Information Gain
Entropy
Statistical parameters to represent data

Hands-On/Demo
Estimating mean, median and mode using python
Calculating Information Gain and Entropy

Goal: In this module, you should learn about probability, interpret & solve real-life problems using probability. You will get to know the power of probability with Bayesian Inference.

Objectives: At the end of this Module, you should be able to:
Understand rules of probability
Learn about dependent and independent events
Implement conditional, marginal and joint probability using Bayes Theorem
Discuss probability distribution
Explain Central Limit Theorem

Topics:
Uses of probability
Need of probability
Bayesian Inference
Density Concepts
Normal Distribution Curve

Hands-On/Demo:
Calculating probability using python
Conditional, Joint and Marginal Probability using Python
Plotting a Normal distribution curve

Goal: Draw inferences from present data and construct predictive models using different inferential parameters (as a constraint).

Objectives: At the end of this Module, you should be able to:
Understand the concept of point estimation using confidence margin
Draw meaningful inferences using margin of error
Explore hypothesis testing and its different levels

Topics:
Point Estimation
Confidence Margin
Hypothesis Testing
Levels of Hypothesis Testing

Hands-On/Demo:
Calculating and generalizing point estimates using python
Estimation of Confidence Intervals and Margin of Error

Goal: In this module, you should learn the different methods of testing the alternative hypothesis.

Objectives: At the end of this module, you should be able to:
Understand Parametric and Non-parametric Testing
Learn various types of parametric testing
Discuss experimental designing
Explain a/b testing

Topics:
Parametric Test
Parametric Test Types
Non- Parametric Test
Experimental Designing
A/B testing

Hands-On/Demo:
Perform p test and t tests in python
A/B testing in python

  • Goal: Get an introduction to Clustering as part of this Module which forms the basis for machine learning. 


    Objectives: At the end of this module, you should be able to:

    • Understand the concept of association and dependence
    • Explain causation and correlation
    • Learn the concept of covariance
    • Discuss Simpson’s paradox
    • Illustrate Clustering Techniques

    Topics:

    • Association and Dependence
    • Causation and Correlation
    • Covariance
    • Simpson’s Paradox
    • Clustering Techniques

    Hands-On/Demo:

    • Correlation and Covariance in python
    • Hierarchical clustering in python
    • K means clustering in python
    in python
  • Goal: Learn the roots of Regression Modelling using statistics. 

     

    Objectives: At the end of this module, you should be able to:

    • Understand the concept of Linear Regression
    • Explain Logistic Regression
    • Implement WOE
    • Differentiate between heteroscedasticity and homoscedasticity
    • Learn the concept of residual analysis

     

    Topics:

    • Logistic and Regression Techniques
    • Problem of Collinearity
    • WOE and IV
    • Residual Analysis
    • Heteroscedasticity
    • Homoscedasticity

     

    Hands-On/Demo:

    • Perform Linear and Logistic Regression in python
    • Analyze the residuals using python

Python Statistics for Data Science Course

Python Scripting allows programmers to build applications easily and rapidly. This course is an introduction to Python scripting, which focuses on the concepts of Python, it will help you to perform operations on variable types using Pycharm.

Goal: In this module, you will be introduced to data and its types and will accordingly sample data and derive meaningful information from the data in terms of different statistical parameters.

Objectives: At the end of this Module, you should be able to:
Understand various data types
Learn Various variable types
List the uses of Variable types
Explain Population and Sample
Discuss Sampling techniques
Understand Data representation

Topics:
Introduction to Data Types
Numerical parameters to represent data
Mean
Mode
Median
Sensitivity
Information Gain
Entropy
Statistical parameters to represent data

Hands-On/Demo:
Estimating mean, median and mode using R
Calculating Information Gain and Entropy

Goal: In this module, you will learn about probability, interpret & solve real-life problems using probability. You will get to know the power of probability with Bayesian Inference.

Objectives: At the end of this Module, you should be able to:
Understand rules of probability
Learn about dependent and independent events
Implement conditional, marginal and joint probability using Bayes Theorem
Discuss probability distribution
Explain Central Limit Theorem

Topics:
Uses of probability
Need of probability
Bayesian Inference
Density Concepts
Normal Distribution Curve

Hands-On/Demo:
Calculating probability using R
Conditional, Joint and Marginal Probability using R
Plotting a Normal distribution curve

Goal: In this module, you will be able to draw inferences from present data and construct predictive models using different inferential parameters (as the constraint).

Objectives: At the end of this Module, you should be able to:
Understand the concept of point estimation using confidence margin
Demonstrate the use of Level of Confidence and Confidence Margin
Draw meaningful inferences using margin of error
Explore hypothesis testing and its different levels

Topics:
Point Estimation
Confidence Margin
Hypothesis Testing
Levels of Hypothesis Testing

Hands-On/Demo:
Calculating and generalizing point estimates using R
Estimation of Confidence Intervals and Margin of Error

Goal: In this module, you will learn the different methods of testing the alternative hypothesis.

Objectives: At the end of this module, you should be able to:
Understand Parametric and Non-Parametric testing
Learn various types of Parametric testing
Explain A/B testing

Topics:
Parametric Test
Parametric Test Types
Non- Parametric Test
A/B testing

Hands-On/Demo:
Perform P test and T tests in R

  • Goal: In this module, you will get an introduction to Clustering which forms the basis for machine learning.

    Objectives: At the end of this module, you should be able to:
    Understand the concept of Association and Dependence
    Explain Causation and Correlation
    Learn the concept of Covariance
    Discuss Simpson’s paradox
    Illustrate Clustering Techniques

    Topics:
    Association and Dependence
    Causation and Correlation
    Covariance
    Simpson’s Paradox
    Clustering Techniques

    Hands-On/Demo:
    Correlation and Covariance in R
    Hierarchical clustering in R
    K means clustering in R

  • Goal: In this module, you will be able to learn about the roots of Regression Modelling using statistics.

    Objectives: At the end of this module, you should be able to:
    Understand the concept of Linear Regression
    Explain Logistic Regression
    Implement WOE
    Differentiate between heteroscedasticity and homoscedasticity
    Learn concept of residual analysis

    Topics:
    Logistic and Regression Techniques
    Problem of Collinearity
    WOE and IV
    Residual Analysis
    Heteroscedasticity
    Homoscedasticity

    Hands-On/Demo:
    Perform Linear and Logistic Regression in R
    Analyze the residuals using R
    Calculation of WOE values using R

Python Statistics for Data Science Course

Python Scripting allows programmers to build applications easily and rapidly. This course is an introduction to Python scripting, which focuses on the concepts of Python, it will help you to perform operations on variable types using Pycharm.

What is analytics & Data Science?
Common Terms in Analytics
Analytics vs. Data warehousing, OLAP, MIS Reporting
Relevance in industry and need of the hour
Types of problems and business objectives in various industries
How leading companies are harnessing the power of analytics?
Critical success drivers
Overview of analytics tools & their popularity
Analytics Methodology & problem solving framework
List of steps in Analytics projects
Identify the most appropriate solution design for the given problem statement
Project plan for Analytics project & key milestones based on effort estimates
Build Resource plan for analytics project

Why Python for data science?
Overview of Python- Starting with Python
Introduction to installation of Python
Introduction to Python Editors & IDE’s(Canopy, pycharm, Jupyter, Rodeo, Ipython etc…)
Understand Jupyter notebook & Customize Settings
Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
Installing & loading Packages & Name Spaces
Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
List and Dictionary Comprehensions
Variable & Value Labels – Date & Time Values
Basic Operations – Mathematical – string – date
Reading and writing data
Simple plotting
Control flow & conditional statements
Debugging & Code profiling
How to create class and modules and how to call them?
Scientific Distributions Used In Python For Data Science
NumPy, pandas, scikit-learn, stat models, nltk

Importing Data from various sources (Csv, txt, excel, access etc)
Database Input (Connecting to database)
Viewing Data objects – subsetting Data, methods
Exporting Data to various formats
Important python modules: Pandas, beautiful soup

Cleansing Data with Python
Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc)
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
Python Built-in Functions (Text, numeric, date, utility functions)
Python User Defined Functions
Stripping out extraneous information
Normalizing data
Formatting data
Important Python modules for data manipulation (Pandas, Numpy, re, math, string, datetime etc)

  • Introduction exploratory data analysis
    Descriptive statistics, Frequency Tables and summarization
    Univariate Analysis (Distribution of data & Graphical Analysis)
    Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
    Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
    Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, seaborn, Pandas and SciPy. Stats etc)

  • Basic Statistics – Measures of Central Tendencies and Variance
    Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
    Inferential Statistics -Sampling – Concept of Hypothesis Testing Statistical Methods – Z/t-tests( One sample, independent, paired), Analysis of variance, Correlations and Chi-square
    Important modules for statistical methods: NumPy, SciPy, Pandas

Concept of model in analytics and how it is used?
Common terminology used in analytics & Modelling process
Popular modelling algorithms
Types of Business problems – Mapping of Techniques
Different Phases of Predictive Modelling

Need for structured exploratory data
EDA framework for exploring the data and identifying any problems with the data (Data Audit Report)
Identify missing data
Identify outliers data
Visualize the data trends and patterns

Need of Data preparation
Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
Variable Reduction Techniques – Factor & PCA Analysis

Introduction to Segmentation
Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
Behavioural Segmentation Techniques (K-Means Cluster Analysis)
Cluster evaluation and profiling – Identify cluster characteristics
Interpretation of results – Implementation on new data

Introduction – Applications
Assumptions of Linear Regression
Building Linear Regression Model
Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
Assess the overall effectiveness of the model
Validation of Models (Re running Vs. Scoring)
Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
Interpretation of Results – Business Validation – Implementation on new data

Introduction – Applications
Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
Building Logistic Regression Model (Binary Logistic Model)
Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
Validation of Logistic Regression Models (Re running Vs. Scoring)
Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
Interpretation of Results – Business Validation – Implementation on new data

Introduction – Applications
Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
Classification of Techniques(Pattern based – Pattern less)
Basic Techniques – Averages, Smoothening, etc
Advanced Techniques – AR Models, ARIMA, etc
Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

Introduction to Machine Learning & Predictive Modelling
Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)
Overfitting (Bias-Variance Trade off) & Performance Metrics
Feature engineering & dimension reduction
Concept of optimization & cost function
Overview of gradient descent algorithm
Overview of Cross validation(Bootstrapping, K-Fold validation etc)
Model performance metrics (R-square, Adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )

What is segmentation & Role of ML in Segmentation?
Concept of Distance and related math background
K-Means Clustering
Expectation Maximization
Hierarchical Clustering
Spectral Clustering (DBSCAN)
Principle component Analysis (PCA)

Decision Trees – Introduction – Applications
Types of Decision Tree Algorithms
Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
Decision Trees – Validation
Overfitting – Best Practices to avoid

Concept of Ensembling
Manual Ensembling Vs. Automated Ensembling
Methods of Ensembling (Stacking, Mixture of Experts)
Bagging (Logic, Practical Applications)
Random forest (Logic, Practical Applications)
Boosting (Logic, Practical Applications)
Ada Boost
Gradient Boosting Machines (GBM)
XGBoost

Motivation for Neural Networks and Its Applications
Perceptron and Single Layer Neural Network, and Hand Calculations
Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
Neural Networks for Regression
Neural Networks for Classification
Interpretation of Outputs and Fine tune the models with hyper parameters
Validating ANN models

Motivation for Support Vector Machine & Applications
Support Vector Regression
Support vector classifier (Linear & Non-Linear)
Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
Interpretation of Outputs and Fine tune the models with hyper parameters
Validating SVM models

What is KNN & Applications?
KNN for missing treatment
KNN For solving regression problems
KNN for solving classification problems
Validating KNN model
Model fine tuning with hyper parameters

Concept of Conditional Probability
Bayes Theorem and Its Applications
Naïve Bayes for classification
Applications of Naïve Bayes in Classifications

Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
Finding patterns in text: text mining, text as a graph
Natural Language processing (NLP)
Text Analytics – Sentiment Analysis using Python
Text Analytics – Word cloud analysis using Python
Text Analytics – Segmentation using K-Means/Hierarchical Clustering
Text Analytics – Classification (Spam/Not spam)
Applications of Social Media Analytics
Metrics(Measures Actions) in social media analytics
Examples & Actionable Insights using Social Media Analytics
Important python modules for Machine Learning (SciKit Learn, stats models, scipy, nltk etc)
Fine tuning the models using Hyper parameters, grid search, piping etc.

Python Statistics for Data Science Course

Python Scripting allows programmers to build applications easily and rapidly. This course is an introduction to Python scripting, which focuses on the concepts of Python, it will help you to perform operations on variable types using Pycharm.

What is analytics & Data Science?
Common Terms in Analytics
Analytics vs. Data warehousing, OLAP, MIS Reporting
Relevance in industry and need of the hour
Types of problems and business objectives in various industries
How leading companies are harnessing the power of analytics?
Critical success drivers
Overview of analytics tools & their popularity
Analytics Methodology & problem solving framework
List of steps in Analytics projects
Identify the most appropriate solution design for the given problem statement
Project plan for Analytics project & key milestones based on effort estimates
Build Resource plan for analytics project
Why R for data science?

Introduction R/R-Studio – GUI
Concept of Packages – Useful Packages (Base & Other packages)
Data Structure & Data Types (Vectors, Matrices, factors, Data frames, and Lists)
Importing Data from various sources (txt, dlm, excel, sas7bdata, db, etc.)
Database Input (Connecting to database)
Exporting Data to various formats)
Viewing Data (Viewing partial data and full data)
Variable & Value Labels – Date Values

Data Manipulation steps
Creating New Variables (calculations & Binning)
Dummy variable creation
Applying transformations
Handling duplicates
Handling missings
Sorting and Filtering
Subsetting (Rows/Columns)
Appending (Row appending/column appending)
Merging/Joining (Left, right, inner, full, outer etc)
Data type conversions
Renaming
Formatting
Reshaping data
Sampling
Data manipulation tools
Operators
Functions
Packages
Control Structures (if, if else)
Loops (Conditional, iterative loops, apply functions)
Arrays
R Built-in Functions (Text, Numeric, Date, utility)
Numerical Functions
Text Functions
Date Functions
Utilities Functions
R User Defined Functions
R Packages for data manipulation (base, dplyr, plyr, data.table, reshape, car, sqldf, etc)

introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
Creating Graphs- Bar/pie/line chart/histogram/boxplot/scatter/density etc)
R Packages for Exploratory Data Analysis(dplyr, plyr, gmodes, car, vcd, Hmisc, psych, doby etc)
R Packages for Graphical Analysis (base, ggplot, lattice,etc)

  • Basic Statistics – Measures of Central Tendencies and Variance
    Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
    Inferential Statistics -Sampling – Concept of Hypothesis Testing
    Statistical Methods – Z/t-tests( One sample, independent, paired), Anova, Correlations and Chi-square

  • Concept of model in analytics and how it is used?
    Common terminology used in analytics & modelling process
    Popular modelling algorithms
    Types of Business problems – Mapping of Techniques
    Different Phases of Predictive Modelling

Accordion Content

 Need of Data preparation
Consolidation/Aggregation – Outlier treatment – Flat Liners – Missing values- Dummy creation – Variable Reduction
Variable Reduction Techniques – Factor & PCA Analysis

Introduction to Segmentation
Types of Segmentation (Subjective Vs Objective, Heuristic Vs. Statistical)
Heuristic Segmentation Techniques (Value Based, RFM Segmentation and Life Stage Segmentation)
Behavioral Segmentation Techniques (K-Means Cluster Analysis)
Cluster evaluation and profiling – Identify cluster characteristics
Interpretation of results – Implementation on new data

Introduction – Applications
Assumptions of Linear Regression
Building Linear Regression Model
Understanding standard metrics (Variable significance, R-square/Adjusted R-square, Global hypothesis ,etc)
Assess the overall effectiveness of the model
Validation of Models (Re running Vs. Scoring)
Standard Business Outputs (Decile Analysis, Error distribution (histogram), Model equation, drivers etc.)
Interpretation of Results – Business Validation – Implementation on new data

Introduction – Applications
Linear Regression Vs. Logistic Regression Vs. Generalized Linear Models
Building Logistic Regression Model (Binary Logistic Model)
Understanding standard model metrics (Concordance, Variable significance, Hosmer Lemeshov Test, Gini, KS, Misclassification, ROC Curve etc)
Validation of Logistic Regression Models (Re running Vs. Scoring)
Standard Business Outputs (Decile Analysis, ROC Curve, Probability Cut-offs, Lift charts, Model equation, Drivers or variable importance, etc)
Interpretation of Results – Business Validation – Implementation on new data

Introduction – Applications
Time Series Components( Trend, Seasonality, Cyclicity and Level) and Decomposition
Classification of Techniques(Pattern based – Pattern less)
Basic Techniques – Averages, Smoothening, etc
Advanced Techniques – AR Models, ARIMA, etc
Understanding Forecasting Accuracy – MAPE, MAD, MSE, etc

Introduction to Machine Learning & Predictive Modeling
Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
Overfitting (Bias-Variance Trade off) & Performance Metrics
Feature engineering & dimension reduction
Concept of optimization & cost function
Overview of gradient descent algorithm
Overview of Cross validation(Bootstrapping, K-Fold validation etc)
Model performance metrics (R-square, Adjusted R-squre, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )

What is segmentation & Role of ML in Segmentation?
Concept of Distance and related math background
K-Means Clustering
Expectation Maximization
Hierarchical Clustering
Spectral Clustering (DBSCAN)
Principle component Analysis (PCA)

Decision Trees – Introduction – Applications
Types of Decision Tree Algorithms
Construction of Decision Trees through Simplified Examples; Choosing the “Best” attribute at each Non-Leaf node; Entropy; Information Gain, Gini Index, Chi Square, Regression Trees
Generalizing Decision Trees; Information Content and Gain Ratio; Dealing with Numerical Variables; other Measures of Randomness
Pruning a Decision Tree; Cost as a consideration; Unwrapping Trees as Rules
Decision Trees – Validation
Overfitting – Best Practices to avoid

Concept of Ensembling
Manual Ensembling Vs. Automated Ensembling
Methods of Ensembling (Stacking, Mixture of Experts)
Bagging (Logic, Practical Applications)
Random forest (Logic, Practical Applications)
Boosting (Logic, Practical Applications)
Ada Boost
Gradient Boosting Machines (GBM)
XGBoost

Motivation for Neural Networks and Its Applications
Perceptron and Single Layer Neural Network, and Hand Calculations
Learning In a Multi Layered Neural Net: Back Propagation and Conjugant Gradient Techniques
Neural Networks for Regression
Neural Networks for Classification
Interpretation of Outputs and Fine tune the models with hyper parameters
Validating ANN models

Motivation for Support Vector Machine & Applications
Support Vector Regression
Support vector classifier (Linear & Non-Linear)
Mathematical Intuition (Kernel Methods Revisited, Quadratic Optimization and Soft Constraints)
Interpretation of Outputs and Fine tune the models with hyper parameters
Validating SVM models

What is KNN & Applications?
KNN for missing treatment
KNN For solving regression problems
KNN for solving classification problems
Validating KNN model
Model fine tuning with hyper parameters

Concept of Conditional Probability
Bayes Theorem and Its Applications
Naïve Bayes for classification
Applications of Naïve Bayes in Classifications

Taming big text, Unstructured vs. Semi-structured Data; Fundamentals of information retrieval, Properties of words; Creating Term-Document (TxD);Matrices; Similarity measures, Low-level processes (Sentence Splitting; Tokenization; Part-of-Speech Tagging; Stemming; Chunking)
Finding patterns in text: text mining, text as a graph
Natural Language processing (NLP)
Text Analytics – Sentiment Analysis using R
Text Analytics – Word cloud analysis using R
Text Analytics – Segmentation using K-Means/Hierarchical Clustering
Text Analytics – Classification (Spam/Not spam)
Applications of Social Media Analytics
Metrics(Measures Actions) in social media analytics
Examples & Actionable Insights using Social Media Analytics
Important R packages for Machine Learning (caret, H2O, Randomforest, nnet, tm etc)
Fine tuning the models using Hyper parameters, grid search, piping etc.

Course Reviews

I had a wonderful experience in Radical technologies where i did training in Hadoop development under the guidance of Shanit Sir. He started from the very basic and covered and shared everything he knew in this field. He was brilliant and had a lot of experience in this field. We did hands on for every topic we covered, and that’s the most important thing because honestly theoretical knowledge cannot land you a job.
Rohit Agrawal Hadoop
I have recently completed Linux course under Anand Sir and can assuredly say that it is definitely the best Linux course in Pune. Since most of the Linux courses from other sources are strictly focused on clearing the certification, they will not provide an insight into real-world server administration, but that is not the case with Anand Sir’s course. Anand Sir being an experienced IT infrastructure professional has an excellent understanding of how a data center works and all these information is seamlessly integrated into his classes.
Manu Sunil Linux
I had undergone oracle DBA course under Chetan sir’s Guidance an it was a very good learning experience overall since they not only provide us with theoretical knowledge but also conduct lot of practical sessions which are really fruitful and also the way of teaching is very fine clear and crisp which is easier to understand , overall I had a great time for around 2 months , they really train you well.also make it a point to clear all your doubts and provide you with clear and in-depth concepts hence hope to join sometime again
Reema banerjee Oracle DBA
I have completed Oracle DBA 11g from Radical technology pune. Excellent trainer (chetna gupta ). The trainer kept the energy level up and kept us interested throughout. Very practical, hands on experience. Gave us real-time examples, excellent tips and hints. It was a great experience with Radical technologies.
Mrudul Bhokare Oracle DBA
Linux learning with Anand sir is truly different experience… I don’t have any idea about Linux and system but Anand sir taught with scratch…He has a great knowledge and the best trainer…he can solve all your queries related to Linux in very simple way and giving nice examples… 100 🌟 to Anand Sir.
Harsh Singh Parihar Linux
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How You Benefit From This Program

  • Non-biased career guidance
  • Counselling based on your skills and preference
  • No repetitive calls, only as per convenience
  • Rigorous curriculum designed by industry experts
  • Complete this program while you work

Why we are the best Radical Technologies

Radical Technologies is truly progressing and offer best possible services. And recognition towards Radical Technologies is increasing steeply as the demand is growing rapidly.

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