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No 1 Software Training Online IT Institutes In India
+91-9150717320
info@immeksoftech.com
We, at ACTE are known for our affordable and quality training. The Amazon Web Services (AWS) Training in Chennai at ACTE is that the best training for this field. Our training is within the most practical manner facilitating it to be the foremost wanted training.
This preparation is given within the at-most viable way remembering that Amazon Web Services (AWS) Training in Chennai is meant to be straightforward and easy to adapt yet it’s one among the foremost generously compensated and most creating testing device. The individuals who enlist are going to be provided with their own understudy login and learning material while likewise being provided with continue arrangement, interpret interviews, specialized meetings and useful application on the innovation.
The Amazon Web Services (AWS) Training in Chennai at ACTE is suitable for applicants all things considered. We give preparing to most creating courses within the business, Data Science with Python is instructed in order that the competitors advantage the foremost out of it.
This training is provided within the at-most practical manner keeping in mind that Data Science with Python is intended to be simple and simple to find out yet it’s one among the very best paid and most developing testing tool. those that enroll are going to be supplied with their own student login and learning material whilst also being supplied with resume preparation, interpret interviews, technical interviews and utilization on the technology.
Our Amazon Web Services (AWS) Training in Chennai provides quality practical knowledge to the candidates which will help them in their work. we’ve got a team of execs which will help the scholars crack interviews for well-known MNC’s.
The students that enrol at the Amazon Web Services (AWS) Training in Chennai at ACTE are going to be given 24×7 live support, aiding them prepare their best for job placement. they’re also supplied with Internationally recognized certificate from ACTE’s tie up’s The national institute of education (NIE) in Singapore. The candidates are sent for interviews on a weekly basis until all of them are placed. Contact us for a free demo.
ACTE Chennai offers Data Science with Python Training in more than 27+ branches with expert trainers. Here are the key features,
ACTE TRAINING INSTITUTE PVT LTD is the unique Authorised Oracle Partner, Authorised Microsoft Partner, Authorised Pearson Vue Exam Center, Authorised PSI Exam Center, Authorised Partner Of AWS and National Institute of Education (nie) Singapore.
Amazon Web Services (AWS) Training in Chennai with Python Course Syllabus
1. What is Data Science?
2. What is Machine Learning?
3. What is Deep Learning?
4. What is AI?
5. Data Analytics & it’s types
1. What is Python?
2. Why Python?
3. Installing Python
4. Python IDEs
5. Jupyter Notebook Overview
1. Python Basic Data types
2. Lists
3. Slicing
4. IF statements
5. Loops
6. Dictionaries
7. Tuples
8. Functions
9. Array
10. Selection by position & Labels
1. Pandas
2. Numpy
3. Sci-kit Learn
4. Mat-plot library
1. Reading CSV files
2. Saving in Python data
3. Loading Python data objects
4. Writing data to csv file
1. Selecting rows/observations
2. Rounding Number
3. Selecting columns/fields
4. Merging data
5. Data aggregation
6. Data munging techniques
1. Central Tendency
1.1. Mean
1.2. Median
1.3. Mode
1.4. Skewness
1.5. Normal Distribution
2. Probability Basics
2.1. What does mean by probability?
2.2. Types of Probability
2.3. ODDS Ratio?
3. Standard Deviation
3.1. Data deviation & distribution
3.2. Variance
4. Bias variance Trade off
4.1. Underfitting
4.2. Overfitting
5. Distance metrics
5.1. Euclidean Distance
5.2. Manhattan Distance
6. Outlier analysis
6.1. What is an Outlier?
6.2. Inter Quartile Range
6.3. Box & whisker plot
6.4. Upper Whisker
6.5. Lower Whisker
6.6. Catter plot
6.7. Cook’s Distance
7. Missing Value treatments
7.1. What is a NA?
7.2. Central Imputation
7.3. KNN imputation
7.4. Domifications
8. Correlation
8.1. Pearson correlation
8.2. Positive & Negative correlation
9. Error Metrics
9.1. Classification
9.2. Confusion Matrix
9.3. Precision
9.4. Recall
9.5. Specificity
9.6. F1 Score
10. Regression
10.1. MSE
10.2. RMSE
10.3. MAPE
1. K-Means
2. K-Means ++
3. Hierarchical Clustering
1. K – Nearest Neighbors
2. Naïve Bayes Classifier
3. Decision Tree – CART
4. Decision Tree – C50
5. Random Forest
ACTE Chennai offers placement opportunities as add-on to every student / professional who completed our classroom or online training. Some of our students are working in these companies listed below.
Our ACTE Chennai Reviews are listed here. Reviews of our students who completed their training with us and left their reviews in public portals and our primary website of Immek Softech Academy.
ACTE has tie up with The National Institute of Education(NIE) in singapore and you will be getting internationally recognized certificate.For interested candidates we will assist you to prepare global certification and examination.You can do it in our ACTE examination center in chennai or bangalore.
Our training is made to be affordable by everyone from experts to under-graduates. The course duration is between 1-2 months depending on the session and the course enrolled for.
ACTE provides various support for placement even after the course completion. We provide our candidates with knowledge about Resume preparation, Mock interview and technical interviews preparing their best for the placement.
Weekend Training Batch (Saturday, Sunday & Holidays)
Seats Available : 8 (maximum)
Regular Batch (Morning, Afternoon & Evening)
Seats Available : 8 (maximum)
Fast Track Batch
Seats Available : 5 (maximum)
Online Training (Flexible timing)
Seats Available : One to One (or) 3 (maximum)
Microsoft Azure Trainers are
Our Somehow important lesson questions are here :
Data can be imported into R using various functions, depending on the format of the data. For example, read.csv() for CSV files, read.table() for tabular data, and readRDS() for RDS files. Additionally, packages like readxl can be used for Excel files, and jsonlite for JSON files.
The apply family of functions in R, including apply(), lapply(), sapply(), vapply(), and tapply(), are used for performing operations on data structures in an efficient and concise manner. apply() is used for applying functions over the margins of an array or matrix. lapply() and sapply() are used for list objects, with sapply() simplifying the output. vapply() is similar to sapply(), but with a predefined type of return value, making it safer and faster. tapply() applies a function over subsets of a vector.
ggplot2 is a plotting system for R based on the grammar of graphics. It provides a powerful framework for creating complex and aesthetically pleasing visualizations in a coherent and consistent manner. It’s widely used for exploratory data analysis and to visualize statistical models.
Missing values in R can be handled in several ways, including using na.omit() to remove rows with NA values, using na.exclude() to exclude NAs in model fitting, or using functions like impute() from the impute package to replace missing values with statistical imputations such as mean, median, or mode.
The Tidyverse is a collection of R packages designed for data science that share an underlying design philosophy, grammar, and data structures. It includes packages like ggplot2 for visualization, dplyr for data manipulation, tidyr for tidying data, and readr for reading data. The Tidyverse makes data analysis in R easier, more intuitive, and more consistent.
A linear model in R can be created using the lm() function. For example, model <- lm(y ~ x, data = dataset) creates a linear model predicting y from x using the data in dataset. The summary of the model can be obtained using summary(model).
Factors in R are used to represent categorical data and are stored as integers. Each integer has a corresponding label. Factors are useful in statistical modeling as they define the categorical nature of the data. They differ from continuous variables, which represent numeric data that can assume an infinite number of values within a range.
R packages are collections of R functions, data, and compiled code in a well-defined format. They extend the capability of R by adding new functions. Commonly used R packages in data science include dplyr for data manipulation, ggplot2 for data visualization, caret for machine learning, shiny for interactive web apps, and tidyr for data tidying.
Data frames are key data structures in R that represent datasets in a tabular form, similar to a spreadsheet. Data frames consist of rows and columns, where each column can be of a different type (numeric, character, factor, etc.), and each row represents an observation.
Subsetting data in R can be done using the square brackets [ ], the $ operator for specific columns, or using functions like subset(). For example, data[rows, columns] allows subsetting by row and column numbers or names, and subset(data, condition) subsets rows based on a condition.
rbind() (row bind) function combines data frames or matrices by rows, whereas cbind() (column bind) function combines them by columns. They are used to merge data structures by adding rows or columns, respectively.
Duplicate values in R can be identified and removed using the duplicated() function, which returns a logical vector indicating which rows are duplicates. The unique() function can be used to extract a data frame without duplicates.
The list() function in R is used to create lists, which can contain objects of different types and lengths, including other lists. The c() function combines its arguments to form a vector, but it only holds objects of the same type, coercing them if necessary.
set.seed() is used to specify the starting point for generating a sequence of random numbers in R. It ensures the reproducibility of results that involve random number generation.
Two data frames can be merged using the merge() function. For example, merge(df1, df2, by = "key") merges df1 and df2 on the column named “key”.
The %>% operator, also known as the pipe operator from the magrittr package and heavily used in Tidyverse, allows for the chaining of functions in a more readable manner. It passes the result of the left-hand side expression as the first argument to the function on the right-hand side.
Type conversion in R can be performed using functions like as.numeric(), as.character(), as.factor(), etc. These functions convert objects from one class to another.
The str() function provides a compact, human-readable summary of the structure of an R object. It’s useful for quickly understanding the type, length, and content of the object, making it an essential tool for data exploration.
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