Artificial Intelligence Course

AI refers to the development of computer systems that can perform tasks that would typically require human intelligence. These tasks include learning, problem-solving, speech recognition, decision-making, and even creativity.

At its core, AI aims to simulate human intelligence by processing vast amounts of data and using algorithms to make informed decisions or predictions.

This is no exception to this global trend. With its burgeoning tech industry and growing demand for skilled professionals, here we will explore the various aspects of artificial intelligence in Vizag and what we teach students in our AI training in Vizag.

What We Teach About Artificial Intelligence Course:

Students can choose from a wide range of programs that cover various aspects of AI, such as machine learning, natural language processing, computer vision, robotics, and more.

In our artificial intelligence course in Vizag With its state-of-the-art infrastructure and experienced faculty members, Datapro Computers provides students with hands-on training and practical exposure to real-world AI applications. The institute's curriculum is carefully crafted to cover all essential modules related to artificial intelligence while keeping up with the latest industry trends.

Our AI Course Modules:

• Our comprehensive AI Fundamentals course in Vizag is your entry point into the interesting and growing field of artificial intelligence. This AI training in Vizag is meant to provide a solid foundation and hands-on experience in AI ideas and applications, whether you're an expert trying to improve your abilities or a beginner interested in AI opportunities.
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• Module-1: EDA / Data Analytics using Python [14 Sessions] [using Pandas, Numpy, Matplotlib & Seaborn]
• Day 1
• Section-1: Data Analysis using Pandas
• Structural Perspective of Datatypes
• What is Data Analysis
• What is Pandas
• What is a DataFrame
• What is Jupyter Notebook
• How to use Anaconda
• Program: Reading tabular data file (data with rows and columns) into pandas – demo on read_table()
• Program: To display 1st 7 rows only
• Program: Explicitly specifying the delimiter symbol and solving header row problem
• Program: Explicitly specifying the column names
• Program: demo on read_csv()
• Day 2
• Program: Demo on type() and selecting a pandas series from a DataFrame
• Program: Creating a new series/column in a DataFrame
• Program: Filtering rows of a pandas DataFrame by column value
• Program: using the loc indexer – conditional lookup
• Program: Applying multiple filter criteria to a pandas DataFrame
• Program: Reading from a selection of columns
• Program: Demo on dropping a column or a row from display, also covers usage of “axis” parameter
• Program: Demo on mean() method
• Day 3
• Program: Demo on “groupby” in pandas
• Program: “groupby” Ex2
• Creating a new DataFrame from a collection of Series
• Program: Creating DataFrame from a Python Dictionary
• Program: Demo on using multiple aggregate functions at one go
• Program: Series Ex1
• Program: Retrieving alternate rows and retrieving rows in reverse order
• Program: Checking for null values in a DataFrame
• Program: To figure out whether there are any null values in a DataFrame
• Program: To figure out how many null values are present in a DataFrame
• Program: Dealing with null values/Missing data
• Program: Concatenation of DataFrames
• Program: Setting a column as Index
• loc() vs iloc()
• Day 4
• Program: Merging of DataFrames based on single key
• Program: Joining of DataFrames
• Program: Getting unique elements of a column in a DataFrame
• Program: Getting count of unique elements of a column in a DataFrame
• Program: Getting count of each unique element of a column in a DataFrame
• Program: Demo on apply() on a DataFrame
• Program: Demo on apply() on a lambda expression
• Program: Knowing column names of a DataFrame
• Program: Sorting a DataFrame
• Day 5
• Program: Writing to a CSV file
• Program: Reading from an Excel Spreadsheet file
• Program: Writing to an Excel Spreadsheet file scrapping
• Program: Reading from an HTML table (Web Scrapping in Pandas)
• Program: Pandas Data Analysis – Analysis on San Francisco Employees Salaries Data
• Program: Pandas Data Analysis - Analysis on E-Commerce Purchase Data
• Day 6
• Change the column names of a DataFrame
• Changing name of a Particular Column in a DataFrame
• Setting Column names while creating a DataFrame
• Changing Row Index
• Renaming Row Index
• Add a new Column with single value to a Dataframe
• Add a new Row with same value to a Dataframe
• Using iloc indexer to update a value
• Sorting by index column
• Sorting in descending order based on a column
• Sorting using two columns
• Grouping by multiple columns in a DataFrame
• Pandas Data Pre-processing for Optimal Model Execution
• Demo on Replacing the Missing Data – with the Mode
• Demo on fillna()’s parameter: method = ‘ffill’
• Demo1 on Processing Duplicate Data
• Demo2 - Find the duplicate value in the specific column data of the DataFrame
• Demo3 - Remove duplicate rows based on one or more columns
• Day 7
• Data Feature Engineering – Encoding Categorical Variables
• Demo on Integer Encoding
• Demo on One-Hot Encoding
• Demo on Dummy Variable Encoding
• Mean Target Encoding
• Demo on Mean Target Encoding
• Data Cleaning – Handling Outliers – method 1
• Data Cleaning – Handling Outliers – method 2
• Data Cleaning – Handling Inconsistent data
• Exploratory Data Analysis – Scores and Rankings
• Day 8
• Section-2: Numpy
• Why use NumPy Arrays
• NumPy Datatypes
• Program: NumPy Demo1
• Program: NumPy Demo2 – transforming the 1D array into 2D array
• Program: NumPy avoids copies wherever possible – demo
• Program: If we need a true copy of an array – demo
• Program: To demonstrate that in NumPy arrays, the operations are propagated to the individual elements.
• Program: Indexing Demo1
• Program: Array Indexing Demo3 – Generating arrays using arange()
• Program: Generating arrays using arange() – Ex2
• Program: To generate zeros
• Program: To generate ones
• Program: Demo on linspace()
• Program: To generate random numbers between 0 to 1
• Program: Generating random integers
• Program: Finding index of max value and min value in an array
• Program: Knowing datatype of an array
• Program: Slicing / Indexing 2-D arrays
• NumPy Operations
• Program: Performing arithmetic operations between two 1-D arrays
• Program: Applying a scalar value to a 1-D array
• Program: Using Universal array Functions on 1-D arrays
• Program: To retrieve non-zero elements from an array
• and more