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MACHINE LEARNING WITH R

PREPARE YOURSELF FOR THE MOST SOUGHT AFTER CAREER OF THE 21ST CENTURY!

MACHINE LEARNING WITH R

Instructors With 20+ Years Of Combined Experience In Data Science & Data Analytics

Instructors Have Personally Trained 1,600+ Participants Across India

Participate From Your PC/Mobile With HD Video Live Streaming

Fully Interactive With Two-Way Live Communication

Access Recorded Classes For Easy Reference

Access To Instructor For 1 Month After Course Completion

The Machine Learning With R Certification Course from MyAbhyaas Academy has been designed to provide a detailed practical approach to the study of pattern recognition and computational learning – of prediction-making through the use of computers – in Artificial Intelligence. This course is for students who are looking forward to making a career in one of the most sought after fields of the 21st century – the Artificial Intelligence. Students will learn all about the theoretical foundation of Machine Learning followed by data visualization, model creation, and evaluation – all as applied in the real-world use of artificial intelligence.

This course is also designed for working professionals with a keen interest in learning this to enhance their ability to effectively use intelligence in their field. If you are intrigued by the ever-improving possibilities of the Artificial Intelligence and want to understand this better – our course is specifically for you!

During this course, participants will learn the practical know-how on using Machine Learning techniques using R-Programming. Through the course, participants will work on various assignments to understand how they can leverage Machine Learning algorithms for modelling data. – all explained using relevant case studies. Certification with grade will be issued based on submitted assignments as well as performance in the final test.

KEY LEARNING FROM THIS PROGRAM

After successful completion of this course, the participants would be well versed with:

1. A comprehensive understanding of the Machine Learning using R

2. A comprehensive understanding of key technical tools

3. A practical approach to data pre-processing and visualization

4. A practical approach to modeling data for effective implementation of machine learning

WHO SHOULD REGISTER FOR THIS COURSE?

The course is suitable for beginners as well as experienced professionals who want to make career in data science, business intelligence, data visualization, and data analytics. To benefit from this program, all you need is analytical bent of mind.

A basic understanding of any programming language is desired but not necessary.

Following set of people will find it useful:

1. IT & Software professionals looking to switch to data science and analytics

2. Professionals in data analytics and business analytics job

3. Engineers and MBAs looking to build career in analytics and data science

4. Statisticians and Business Analysts

5. Project Managers

6. Anyone with a genuine interest in the data science field

GET IN TOUCH TO KNOW MORE

Call Us: +91-96195-02101

MACHINE LEARNING COURSE BENEFITS

LEADING INSTRUCTORS

Learn from Instructors with 20+ years of combined experience in data science and data analytics; have trained 1,600+ participants!

30+ TOPICS

Specially designed to provide the requisite conceptual knowledge, tools, and skills to become an effective data science & analytics professional; focus on developing critical thinking abilities

LIVE INTERACTIVE CLASSES

Participate in 66 hours of instructor-led live training; fully interactive with two-way live communication; Designated forums to facilitate interaction and knowledge sharing among participants!

LEARN FROM ANYWHERE

Participate in this program from your PC/mobile with HD video live streaming; life time access to recorded classes for easy reference!

LEARN WITH ASSIGNMENTS

Online quiz and simulation activities to reinforce practical application of learned concepts; explanatory answers!

REAL LIFE APPLICATION

Focus on practical applications and usage of Machine Learning algorithms; many industry specific examples during the training

1-MONTH ACCESS

Access to the instructors for 1 month even after completing the course for guidance/support

CERTIFICATION

Certificate on completion with grades based on the performance in assignments and projects!

COURSE CURRICULUM

MODULE 1: INTRODUCTION TO THE COURSE (2 HRS)

  • Looking at the Big Picture
  • Role of Statistics in Data Science
  • Data Analytics Process

MODULE 2: INTRODUCTION & ANALYSIS WITH R (14 HRS)

  • R Base Software
  • Understanding CRAN
  • RStudio The IDE
  • Basic Building Blocks in R
  • Sequence of Numbers in R
  • Understanding Vectors in R
  • Handling Missing Values in R
  • Subsetting Vectors in R
  • Matrices and Data Frames in R
  • Logical Statements in R
  • Using the Lapply, sapply, vapply and tapply functions
  • Date and Time Functions
  • Looking at Data in R

MODULE 3: BUSINESS STATISTICS (32 HOURS)

  • Sources of Data
  • Types of Data
  • Types of Sample
  • Measures of Central Tendency
  • Measures of Dispersion
  • Measures of Shape
  • What is Probability?
  • Types of probability
  • Bayes Theorem
  • Marginal Probability
  • Joint and Conditional Probability
  • Covariance and Correlation
  • Independence
  • Random Variables – Discrete and Continuous
  • Binomial, Geometric and Normal with practice problems
  • Hypothesis testing & Confidence Interval
  • CI Questions. Discussion on Practice Problem
  • Z-test, t-test, ChiSquare, Anova

MODULE 4: MACHINE LEARNING WITH R (48 HOURS)

INTRODUCTION
  • What is Statistical Learning?
  • Intro to Linear Regression
LINEAR REGRESSION
  • Covariance and Correlation in Data
  • Multivariate Analysis
  • Assumptions of Linearity
  • Hypothesis Testing
  • Limitations of Regression
CASE STUDY FOR LINEAR REGRESSION
  • Extract the Data in R
  • Univariate Analysis of Data
  • Apply Data Transformations
  • Bivariate Analysis
  • Identify Multicollinearity in Data
  • Treatment on Data
  • Identify Hetroscedasticity
  • Discuss what could be the reason for Hetroscedasticity
  • Modelling of Data
  • Variable Significance Identification
  • Model Significance Test
  • Bifurcate Data into Training / Testing Data set
  • Build Model on Training Data Set
  • Predict using Testing Data Set
  • Validate the Model Performance
LASSO & RIDGE REGRESSION
  • What is Lasso and Ridge regression?
  • Application and comparison

MODULE 4: MACHINE LEARNING WITH R (CONTD.)

LOGISTIC REGRESSION
  • Reason for using Logistic Regression
  • The Logistic Transform
  • Logistic Regression Modelling
  • Model Optimisation
  • Understanding the ROC Curve
CASE STUDY FOR LOGISTIC REGRESSION
  • Model Parameter Significance Evaluation
  • Drawing the ROC Curve
  • Estimating the Classification Model Hit Ratio
  • Isolating the Classifier for Optimum Results
K-NEAREST NEIGHBOURS FOR CLASSIFICATION
  • Lazy Learning Notion
  • Computation of Distance Matrix
  • The Optimum K value
  • Data Transformations as a Pre Processing Phase
  • Model Building on Training Data Set
  • Model Validation on Testing Data Set
  • Evaluation of Model
  • Advantages & Disadvantages of KNN Models
NAIVE BAYES FOR MULTI CLASS PREDICTIONS 
  • Bayesian Theorem
  • Probabilities – The Prior and Posterior Probabilities
  • Conditional and Joint Probabilities Notion
  • Traditional Approach – Extract Important Features
  • Naive Approach – Independence of Features Assumption
  • Data Processing – Discretization of Features
  • Model Building / Testing / Validation
  • Advantages & Disadvantages of Naïve Bayes Models
DECISION TREE
  • Classification Tree’s
  • Regression Tree’s
  • Case for Prediction
  • Case for Classification
ENSEMBLE MODELS WITH CASE STUDY 
  • Understanding Entropy
  • Information Value
  • Model Building on Training Data Set
  • Selecting the Best Split in Data
  • Pruning a Decision Tree
  • Model Validation on Testing Data Set
  • Improve Model Performance
  • Bagging Tree’s
  • Boosting Tree’s
  • Random Forest’s
SUPPORT VACTOR MACHINES WITH CASE STUDY 
  • Understanding SVM
  • Concepts of Linearly seperable vs non seperable data
  • Build the Model
  • Training the Model
  • Testing and Validation
  • Tuning the Model
PRINCIPAL COMPONENT ANALYSIS
  • Understanding PCA
  • Applications of PCA
TIME SERIES
  • Definition of Time Series
  • Time Series Decomposition
  • Moving Average Method
  • Exponential Smoothing Method
  • AR Models
  • MA Models
  • ARMA Models
  • ARIMA Models
  • Using RMSE and MAPE for Model Performance

CAPSTONE PROJECTS

Successful execution of one of the 4 real-life, industry-based projects is mandatory for certificate eligibility. In addition, participants can work on one additional project under the guidance of the instructor to get better command over data science techniques.

PROJECT 1: INSURANCE SECTOR

Using predictive analysis on historical consumer data and trends to forecast future trends to help appraising and controlling of risks in underwriting, pricing, rating, etc.

PROJECT 2: TELECOM SECTOR

Using data science techniques on consumer behaviour data, forecast expected future trends in customers’ acquisition, retention and churn-rates.

PROJECT 3: E-COMMERCE SECTOR

Using data science techniques on consumer buying behaviour data, predict behaviour of different set of online consumers and help manage inventory in the system.

PROJECT 4: FINANCIAL SERVICES

Using data science techniques on customers’ specific parameters – such as demographic, income, etc. – predict probability of loan defaults

PARTICIPANTS' FEEDBACK

Harshita Tanwar

Data analytics course from MyAbhyaas is helping me to understand the concepts and its practical application. The guidance provided by the instructor is also helping me to understand key skills required to do better in this field and then acquire those skills!

Data Analyst, Wipro

2017-02-09T18:22:49+00:00

Data Analyst, Wipro

Data analytics course from MyAbhyaas is helping me to understand the concepts and its practical application. The guidance provided by the instructor is also helping me to understand key skills required to do better in this field and then acquire those skills!

Himanshu Chaubal

The Data Analytics with R-programming course by MyAbhyaas is an excellent course! It gave me a clear understanding of all the concepts and tools for business analytics needed to evaluate market trends for different global industries effectively.

Business Research & Advisory Professional, Mumbai

2017-02-10T10:26:57+00:00

Business Research & Advisory Professional, Mumbai

The Data Analytics with R-programming course by MyAbhyaas is an excellent course! It gave me a clear understanding of all the concepts and tools for business analytics needed to evaluate market trends for different global industries effectively.

Contact Us

Phone: +91-96195-02101
Email: info@myabhyaas.com


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