Syllabus of Machine Learning with Python or R
Machine Learning Using R Training Syllabus
Module 1- Introduction to Data Analytics (Duration: 04:00:00 hrs)
- This module introduces you to some of the important keywords in R like Business Intelligence, Business Analytics, Data, and Information.
- You can also learn how R can play an important role in solving complex analytical problems.
- This module tells you what is R and how it is used by giants like Google, Facebook, etc.
- Also, you will learn the use of ‘R’ in the industry, this module also helps you compare R with other software in analytics, install R and its packages.
- Business Analytics, Data, Information
- Understanding Business Analytics and R
- Compare R with other software in analytics
- Install R
- Perform basic operations in R using command line
- Learn the use of IDE R Studio
- Use the ‘R help’ feature in R
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Module 2- Introduction to R programming (Duration: 03:00:00 hrs)
- This module starts from the basics of R programming like datatypes and functions.
- In this module, we present a scenario and let you think about the options to resolve it, such as which datatype should one to store the variable or which R function that can help you in this scenario.
- Will cover the basics of data types and conversion between them
- Variables in R
- Data frames
- Using c, Cbind, Rbind, attach and detach functions in R
Module 3- Data Manipulation in R (Duration: 04:00:00 hrs)
- In this module, we start with a sample of a messy dataset and perform Data Cleaning on it, resulting in a dataset, which is ready for any analysis.
- Before starting any analysis data cleaning is the first process to do
- Data sorting
- Find and remove duplicates record
- Cleaning data
- Recoding data
- Merging data
- Slicing of Data
- Merging Data
- Apply functions
Module 4- Data Import techniques in R (Duration: 04:00:00 hrs)
- This module tells you about the versatility and robustness of R which can take-up data in a variety of formats, be it from a csv file
- This module teaches you various data importing techniques in R.
- Reading and writing flat files – text,csv,Excel
- Reading Techniques in R
- How to connect mysql with R
- Saving and loading R format
Module 5 – Loops and Date (Duration: 04:00:00 hrs)
This module tells you the concepts of loop technique. From this, we can automate anything in R and give the enhancement to your code
Dates will come in any format so that converting the dates, difference and slicing date information is most important in real-time
- For loop
- While loop
- If loop
- Break and continue
- How to convert to date format
- How to slice dates information (Year, Quarters, Months, Days, Hours etc..,)
- How to get difference between two days (Year, Quarters, Months, Days, Hours)
Module 6 – Exploratory Data Analysis (Duration: 05:00:00 hrs)
- In this module, you will learn that exploratory data analysis is an important step in the analysis.
- EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis. You will also learn about the various tasks involved in a typical EDA process.
- Box plot
- Doughnut graph
- Pie graph
- Line chart
Module 7 – Statistics and Machine Learning – Regression and Classification (Duration: 12:00:00 hrs)
- This module touches the base of Descriptive and Inferential Statistics and machine learning concepts
- All algorithm explained by – what is the mathematics behind for that, how it is getting different from other algorithm, which scenario want to apply, how to interpret with R and what insights getting from final result
- Hypothesis Testing – Null Hypothesis and Alternate Hypothesis
- Standard deviation and Variance
- Outliers – Detection and Replacement method
- T test- Unpaired and Paired
- Chi square
Machine Learning Topics
- Linear Regression
- Multiple Regression
- Logisitic Regression
- Naïve Bayes Classifier
- Variable selection on model
- K means Clustering
- Decision Tree
- Time series forecasting
Module 7- Project work (Duration: 08:00:00 hrs)
- 3 Real-time projects
Machine Learning using Python Training Syllabus Total Duration: 44:00:00 hrs
PYTHON Programming (6 hrs)
- Installation & Python basics.
- Python looping, conditional statements.
- Data Structures.
- Classes & Functions.
Introduction to Python Libraries (7 hrs)
- Matplotlib & Seaborn
- Scikit learn
Statistics (6 hrs)
- Descriptive Statistics
- Measure of Frequency, Central tendency, dispersion/variation.
- Inferential Statistics
Data preparation (2hrs)
- Dimensionality Reduction
- Univariate & Multivariate Analysis
Machine Learning Algorithms (20hrs)
- Forecasting & Smoothing
- Logistic Regression
- Naive Bayes
- Decision Trees & Ensemble modeling
- Support Vector Machines
- Deep Learning
- Project on Real-Time Implementation