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Artificial Intelligence/Machine Learning

Artificial Intelligence/Machine Learning

Unlock the Power of AI: Master Machine Learning & Build Intelligent Systems (No PhD Required)

Unlock the Power of AI: Master Machine Learning & Build Intelligent Systems (No PhD Required)

Unlock the Power of AI: Master Machine Learning & Build Intelligent Systems (No PhD Required)

DURATION

3 months

Intensive

MODE

LIVE Online

Intensive

FORMAT

Hybrid

Hands-on, Theoretic

STARTING

June, 2024

Program Details

Program Details

Program Details

Dive into the world of Artificial Intelligence and Machine Learning (AI/ML) in this pre-graduation program! This program equips you with the in-demand skills to excel in this rapidly growing field. Master the Python programming language, the foundation for AI/ML, and leverage powerful libraries like TensorFlow to build and train your own machine learning models. Go beyond theory with practical applications: create compelling data visualizations using Matplotlib and Seaborn. Explore the cutting edge: delve into fundamental algorithms like clustering and classification, and venture into exciting fields like Natural Language Processing (NLP) and Large Language Models (LLMs). But that's not all! Gain invaluable real-world experience through 10+ live industry projects and solidify your knowledge with 20+ hands-on assessments. Don't just learn AI/ML – build the future with it! Enroll today and unlock a world of possibilities.

Dive into the world of Artificial Intelligence and Machine Learning (AI/ML) in this pre-graduation program! This program equips you with the in-demand skills to excel in this rapidly growing field. Master the Python programming language, the foundation for AI/ML, and leverage powerful libraries like TensorFlow to build and train your own machine learning models. Go beyond theory with practical applications: create compelling data visualizations using Matplotlib and Seaborn. Explore the cutting edge: delve into fundamental algorithms like clustering and classification, and venture into exciting fields like Natural Language Processing (NLP) and Large Language Models (LLMs). But that's not all! Gain invaluable real-world experience through 10+ live industry projects and solidify your knowledge with 20+ hands-on assessments. Don't just learn AI/ML – build the future with it! Enroll today and unlock a world of possibilities.

Dive into the world of Artificial Intelligence and Machine Learning (AI/ML) in this pre-graduation program! This program equips you with the in-demand skills to excel in this rapidly growing field. Master the Python programming language, the foundation for AI/ML, and leverage powerful libraries like TensorFlow to build and train your own machine learning models. Go beyond theory with practical applications: create compelling data visualizations using Matplotlib and Seaborn. Explore the cutting edge: delve into fundamental algorithms like clustering and classification, and venture into exciting fields like Natural Language Processing (NLP) and Large Language Models (LLMs). But that's not all! Gain invaluable real-world experience through 10+ live industry projects and solidify your knowledge with 20+ hands-on assessments. Don't just learn AI/ML – build the future with it! Enroll today and unlock a world of possibilities.

Who should enroll?

Who should enroll?

Who should enroll?

-You are keenly interested in Artificial Intelligence, Machine Learning, or Deep Learning.

-You want to explore the fundamentals and advanced concepts of AI and ML.

-You want to build a strong foundation to support your academic and career aspirations.

-You are looking to transition into AI and ML from another domain.

-You want a comprehensive program to kickstart your new career path.

-You are keenly interested in Artificial Intelligence, Machine Learning, or Deep Learning.

-You want to explore the fundamentals and advanced concepts of AI and ML.

-You want to build a strong foundation to support your academic and career aspirations.

-You are looking to transition into AI and ML from another domain.

-You want a comprehensive program to kickstart your new career path.

Join our community to learn, connect with like-minded peers, and get updates on the scholarship test

Limited Seats in the Cohort

APPLY NOW

TEACHING PLAN (3 Month Program)

TEACHING PLAN (3 Month Program)

Week 1

Session-1:

- Introduction to AI & Machine Learning

- Introduction to python

- Introduction to Python & it’s packages

Hands-On: Installed Python & relevant packages

Session-2:

- Fundamentals in python

- Variables & Identifiers

- Keywords & Comments

Hands-On: Practice with variables how to store data into it

Session-3:

- Operators in Python

- Control statement - Conditions

- Iterative statements - Loops

Hands-On: Perform basic operations on sample data using Loops & Conditions

Week 1

Session-1:

- Introduction to AI & Machine Learning

- Introduction to python

- Introduction to Python & it’s packages

Hands-On: Installed Python & relevant packages

Session-2:

- Fundamentals in python

- Variables & Identifiers

- Keywords & Comments

Hands-On: Practice with variables how to store data into it

Session-3:

- Operators in Python

- Control statement - Conditions

- Iterative statements - Loops

Hands-On: Perform basic operations on sample data using Loops & Conditions

Week 1

Session-1:

- Introduction to AI & Machine Learning

- Introduction to python

- Introduction to Python & it’s packages

Hands-On: Installed Python & relevant packages

Session-2:

- Fundamentals in python

- Variables & Identifiers

- Keywords & Comments

Hands-On: Practice with variables how to store data into it

Session-3:

- Operators in Python

- Control statement - Conditions

- Iterative statements - Loops

Hands-On: Perform basic operations on sample data using Loops & Conditions

Week 2

Session 4:

- Data structures in Python

- List / Tuple

- Dictionary / Set

- Break, Continue, Pass statements

Hands-On: Worked with data types & practised with all statements

Session 5:

- Functions in Python

- User-defined function

- Built-in functions

- Lambda functions

Hands-On: Practiced with Lambda & User-Defined functions

Session 6:

- List & dictionary comprehensions

- File Handling

- Exception Handling

Hands-On: Handled all the raised or built-in errors using Exception Handling.

Week 2

Session 4:

- Data structures in Python

- List / Tuple

- Dictionary / Set

- Break, Continue, Pass statements

Hands-On: Worked with data types & practised with all statements

Session 5:

- Functions in Python

- User-defined function

- Built-in functions

- Lambda functions

Hands-On: Practiced with Lambda & User-Defined functions

Session 6:

- List & dictionary comprehensions

- File Handling

- Exception Handling

Hands-On: Handled all the raised or built-in errors using Exception Handling.

Week 2

Session 4:

- Data structures in Python

- List / Tuple

- Dictionary / Set

- Break, Continue, Pass statements

Hands-On: Worked with data types & practised with all statements

Session 5:

- Functions in Python

- User-defined function

- Built-in functions

- Lambda functions

Hands-On: Practiced with Lambda & User-Defined functions

Session 6:

- List & dictionary comprehensions

- File Handling

- Exception Handling

Hands-On: Handled all the raised or built-in errors using Exception Handling.

Week 3

Session 7:

- Introduction to NumPy

- Numpy Array vs Python List

- Creation of 1D, 2D and 3D array

- Special Numpy Functions

- Zeros(), Ones(), full() etc.

Hands-On: Created N-dimensional arrays and performed certain operations

Session 8:

- Random Number Generation

- Data Type Conversion

- Memory Management

- Arithmetic Operations

- Statistical Operations

- Sorting, Joining, Splitting

- Transpose, Reshape, etc.

Hands-On: Generate random Numbers using the Random function.

Session 9:

- Introduction to Pandas

- Series and DataFrames

- Create data frame using List

- Create data frame using Dictionary

- Insert and Delete operation

- Arithmetic Operations

- Indexing and Slicing

Hands-On: Created series and data frame

Week 3

Session 7:

- Introduction to NumPy

- Numpy Array vs Python List

- Creation of 1D, 2D and 3D array

- Special Numpy Functions

- Zeros(), Ones(), full() etc.

Hands-On: Created N-dimensional arrays and performed certain operations

Session 8:

- Random Number Generation

- Data Type Conversion

- Memory Management

- Arithmetic Operations

- Statistical Operations

- Sorting, Joining, Splitting

- Transpose, Reshape, etc.

Hands-On: Generate random Numbers using the Random function.

Session 9:

- Introduction to Pandas

- Series and DataFrames

- Create data frame using List

- Create data frame using Dictionary

- Insert and Delete operation

- Arithmetic Operations

- Indexing and Slicing

Hands-On: Created series and data frame

Week 3

Session 7:

- Introduction to NumPy

- Numpy Array vs Python List

- Creation of 1D, 2D and 3D array

- Special Numpy Functions

- Zeros(), Ones(), full() etc.

Hands-On: Created N-dimensional arrays and performed certain operations

Session 8:

- Random Number Generation

- Data Type Conversion

- Memory Management

- Arithmetic Operations

- Statistical Operations

- Sorting, Joining, Splitting

- Transpose, Reshape, etc.

Hands-On: Generate random Numbers using the Random function.

Session 9:

- Introduction to Pandas

- Series and DataFrames

- Create data frame using List

- Create data frame using Dictionary

- Insert and Delete operation

- Arithmetic Operations

- Indexing and Slicing

Hands-On: Created series and data frame

Week 4

Session 10:

- Reading the CSV, JSON files in dataframe

- Exploratory Data Analysis (EDA)

- Handling MissingData

- Handling Duplicate Data

- Outliers Detection and Treatment

- Join I Concat I Merge Operation

- Date Time Functionalities

- Groupby(), Transpose(), Reshape()

Hands-On: Read CSV data and perform data analysis.

Session 11:

- Introduction to Matplotlib

- Line Plot

- Bar Plot

- Scatter Plot

- Histogram

- Pie Chart

- 3D Plots

Hands-On: Performed data visualization graph using multiple charts

Session 12:

- Introduction to Seaborn

- Histogram

- Boxplot

- Distplot

- Heatmap

Hands-On: Outlier detection using Boxplot.

Assignment 2: Perform basic data analysis on sample data using Numpy, Pandas, Matplotlib, and Seaborn library.

Week 4

Session 10:

- Reading the CSV, JSON files in dataframe

- Exploratory Data Analysis (EDA)

- Handling MissingData

- Handling Duplicate Data

- Outliers Detection and Treatment

- Join I Concat I Merge Operation

- Date Time Functionalities

- Groupby(), Transpose(), Reshape()

Hands-On: Read CSV data and perform data analysis.

Session 11:

- Introduction to Matplotlib

- Line Plot

- Bar Plot

- Scatter Plot

- Histogram

- Pie Chart

- 3D Plots

Hands-On: Performed data visualization graph using multiple charts

Session 12:

- Introduction to Seaborn

- Histogram

- Boxplot

- Distplot

- Heatmap

Hands-On: Outlier detection using Boxplot.

Assignment 2: Perform basic data analysis on sample data using Numpy, Pandas, Matplotlib, and Seaborn library.

Week 4

Session 10:

- Reading the CSV, JSON files in dataframe

- Exploratory Data Analysis (EDA)

- Handling MissingData

- Handling Duplicate Data

- Outliers Detection and Treatment

- Join I Concat I Merge Operation

- Date Time Functionalities

- Groupby(), Transpose(), Reshape()

Hands-On: Read CSV data and perform data analysis.

Session 11:

- Introduction to Matplotlib

- Line Plot

- Bar Plot

- Scatter Plot

- Histogram

- Pie Chart

- 3D Plots

Hands-On: Performed data visualization graph using multiple charts

Session 12:

- Introduction to Seaborn

- Histogram

- Boxplot

- Distplot

- Heatmap

Hands-On: Outlier detection using Boxplot.

Assignment 2: Perform basic data analysis on sample data using Numpy, Pandas, Matplotlib, and Seaborn library.

Week 5

Session 13:

- Introduction to Statistics

- Types of Statistics

- Descriptive Stats vs Inferential Stats

- Population and Sample data

- Sampling and their techniques.

- Simple Random Sampling

- Systematic Sampling

- Stratified Sampling

- Cluster Sampling

Session-14:

- Variables

- Types of Variables

- Quantitative vs Qualitative Variables

- Frequency and Cumulative Frequency

- Measure of Frequency

- Measure of Central Tendency

- Measure of Dispersion and Variance

- Z-Score, Standard Deviation

Session 15:

- Measure of Position or Data Distribution

- Quartile vs Quantilevs Percentile

- Pentile vs Decile

- Five Number Summary

- Interquartile Ranges

- Effect Of Outliers And Its Removal

- Outlier Detection using Boxplot

Week 5

Session 13:

- Introduction to Statistics

- Types of Statistics

- Descriptive Stats vs Inferential Stats

- Population and Sample data

- Sampling and their techniques.

- Simple Random Sampling

- Systematic Sampling

- Stratified Sampling

- Cluster Sampling

Session-14:

- Variables

- Types of Variables

- Quantitative vs Qualitative Variables

- Frequency and Cumulative Frequency

- Measure of Frequency

- Measure of Central Tendency

- Measure of Dispersion and Variance

- Z-Score, Standard Deviation

Session 15:

- Measure of Position or Data Distribution

- Quartile vs Quantilevs Percentile

- Pentile vs Decile

- Five Number Summary

- Interquartile Ranges

- Effect Of Outliers And Its Removal

- Outlier Detection using Boxplot

Week 5

Session 13:

- Introduction to Statistics

- Types of Statistics

- Descriptive Stats vs Inferential Stats

- Population and Sample data

- Sampling and their techniques.

- Simple Random Sampling

- Systematic Sampling

- Stratified Sampling

- Cluster Sampling

Session-14:

- Variables

- Types of Variables

- Quantitative vs Qualitative Variables

- Frequency and Cumulative Frequency

- Measure of Frequency

- Measure of Central Tendency

- Measure of Dispersion and Variance

- Z-Score, Standard Deviation

Session 15:

- Measure of Position or Data Distribution

- Quartile vs Quantilevs Percentile

- Pentile vs Decile

- Five Number Summary

- Interquartile Ranges

- Effect Of Outliers And Its Removal

- Outlier Detection using Boxplot

Week 6

Session 16:

- Normal or Gaussian Distribution

- Properties of Normal Distribution

- Empirical Rule in Normal Distribution

- Central Limit Theorem

- Covariance

- Pearson Coefficient Correlation

Session 17:

- Inferential Statistical Tests

- Confidence Interval

- Regression Analysis

- Hypotheses Testing

- T-Test I Z-Test I F-Test I Annova Test I Chi Square Test

- Null Hypotheses

- Alternate Hypotheses

- P - Value, Significance Level

Session 18:

- Intro to Databases

- Relational Databases vs Non-Relational Databases

- DB vs DBMS vs SQL

- SQL vs NoSQL

- Database Design

Week 6

Session 16:

- Normal or Gaussian Distribution

- Properties of Normal Distribution

- Empirical Rule in Normal Distribution

- Central Limit Theorem

- Covariance

- Pearson Coefficient Correlation

Session 17:

- Inferential Statistical Tests

- Confidence Interval

- Regression Analysis

- Hypotheses Testing

- T-Test I Z-Test I F-Test I Annova Test I Chi Square Test

- Null Hypotheses

- Alternate Hypotheses

- P - Value, Significance Level

Session 18:

- Intro to Databases

- Relational Databases vs Non-Relational Databases

- DB vs DBMS vs SQL

- SQL vs NoSQL

- Database Design

Week 6

Session 16:

- Normal or Gaussian Distribution

- Properties of Normal Distribution

- Empirical Rule in Normal Distribution

- Central Limit Theorem

- Covariance

- Pearson Coefficient Correlation

Session 17:

- Inferential Statistical Tests

- Confidence Interval

- Regression Analysis

- Hypotheses Testing

- T-Test I Z-Test I F-Test I Annova Test I Chi Square Test

- Null Hypotheses

- Alternate Hypotheses

- P - Value, Significance Level

Session 18:

- Intro to Databases

- Relational Databases vs Non-Relational Databases

- DB vs DBMS vs SQL

- SQL vs NoSQL

- Database Design

Download Complete 3 Months Plan

Download Complete 3 Months Plan

CAREER DEVELOPMENT TRACK

CAREER DEVELOPMENT TRACK

  1. Pregrad Career Assist Access

  • Mentoring

  • career-specific resume tailoring

  1. Personal Branding

  • Build and showcase your skills in public

  • Strategic LinkedIn profiling

  1. Community Session

  • Strengthen Communication

  • Improve presentation skills

  1. Interview Preparation

  • Mock community sessions & GD

  • Art of negotiation

  1. Domain workshops/Masterclasses

  • Masterclasses from professionals

  • HR Session

  1. Career Kick-start

  • Internship/Freelance/ Applications & Interview

  • Placement assistance in final year

Total Fee of the Program

₹ 20060/- Including tax

(Non-refundable)

0% cost EMI Option Available*

EMI options for admission will not be available on discounted Fee or admission through scholarship

APPLY NOW

Live Learning delivered by Industry veteran

Sessions Backup

Hands-On Projects & Challenges

Global Certifications

Access to Career Assist cell*

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Payment gateway Razorpay

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G Block Sector 6, Noida, Uttar Pradesh - 201301

hello@pregrad.in

Pregrad Logo

G Block Sector 6, Noida, Uttar Pradesh - 201301

hello@pregrad.in