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IBM
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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
December, 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
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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
Pregrad Career Assist Access
Mentoring
career-specific resume tailoring
Personal Branding
Build and showcase your skills in public
Strategic LinkedIn profiling
Community Session
Strengthen Communication
Improve presentation skills
Interview Preparation
Mock community sessions & GD
Art of negotiation
Domain workshops/Masterclasses
Masterclasses from professionals
HR Session
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
Live Learning delivered by Industry veteran
Sessions Backup
Hands-On Projects & Challenges
Global Certifications
Access to Career Assist cell*
Payment gateway Razorpay
FAQ
Is this course for Beginner, Intermediate, and Advanced level?
What is the duration of this Program?
Is it possible to shift my batch?
Is this course for Beginner, Intermediate, and Advanced level?
What is the class schedule for the Program?
Will I be provided with recordings of classes and how long will we have access to it?
What is the role of the mentor?
What are the profiles of the mentors?
How often does the new Batch start?
When will we be getting internship opportunities?
How is placement at Pregrad?
What is the success rate of the Pregrad's Pre-graduation Program?
Is this course for Beginner, Intermediate, and Advanced level?
What is the duration of this Program?
Is it possible to shift my batch?
Is this course for Beginner, Intermediate, and Advanced level?
What is the class schedule for the Program?
Will I be provided with recordings of classes and how long will we have access to it?
What is the role of the mentor?
What are the profiles of the mentors?
How often does the new Batch start?
When will we be getting internship opportunities?
How is placement at Pregrad?
What is the success rate of the Pregrad's Pre-graduation Program?
Is this course for Beginner, Intermediate, and Advanced level?
What is the duration of this Program?
Is it possible to shift my batch?
Is this course for Beginner, Intermediate, and Advanced level?
What is the class schedule for the Program?
Will I be provided with recordings of classes and how long will we have access to it?
What is the role of the mentor?
What are the profiles of the mentors?
How often does the new Batch start?
When will we be getting internship opportunities?
How is placement at Pregrad?
What is the success rate of the Pregrad's Pre-graduation Program?
G Block Sector 6, Noida, Uttar Pradesh - 201301
hello@pregrad.in
+91 9971300194
G Block Sector 6, Noida, Uttar Pradesh - 201301
hello@pregrad.in
+91 9971300194