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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
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)
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
APPLY NOW
Live Learning delivered by Industry veteran
Sessions Backup
Hands-On Projects & Challenges
Global Certifications
Access to Career Assist cell*
Payment gateway Razorpay
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G Block Sector 6, Noida, Uttar Pradesh - 201301
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G Block Sector 6, Noida, Uttar Pradesh - 201301
hello@pregrad.in