Machine Learning & Artificial Intelligence
Master Python, statistics, and core machine learning algorithms to build intelligent systems. A complete program covering supervised & unsupervised learning, data visualization, and real-world AI applications to make you job-ready in the AI/ML field.
Program Overview
This program provides a strong foundation in Artificial Intelligence and Machine Learning. You’ll learn the fundamentals of Python, explore statistics and probability, work with data visualization tools, and apply machine learning algorithms on real-world projects. With hands-on mentorship and portfolio development, this program prepares you to kickstart your career in AI/ML.
Duration
3 Months
Format
Live Classes + Project
Outcome
AI/ML Certification + Portfolio
Course Syllabus
Introduction to AI & Python
Introduction to AI & Machine Learning
Python Basics & Packages
Hands-On: Install Python & relevant packages
Python Fundamentals
Variables, Identifiers, Keywords & Comments
Hands-On: Practice storing data in variables
Operators & Control Statements
Operators in Python
Conditions & Loops
Hands-On: Perform basic operations using loops & conditions
Data Structures in Python
List, Tuple, Dictionary, Set
Break, Continue, Pass statements
Hands-On: Practice with data types & statements
Functions in Python
User-Defined, Built-in & Lambda Functions
Hands-On: Practice with Lambda & User-Defined Functions
Comprehensions & Error Handling
List & Dictionary Comprehensions
File Handling & Exception Handling
Hands-On: Handle errors using exception handling
Introduction to NumPy
NumPy Arrays vs Python Lists
1D, 2D, 3D Arrays
Special Functions: zeros(), ones(), full()
Hands-On: Create & manipulate N-dimensional arrays
NumPy Operations
Random Number Generation
Type Conversion, Memory Management
Arithmetic & Statistical Operations
Sorting, Joining, Splitting, Reshape, Transpose
Hands-On: Generate random numbers with NumPy
Introduction to Pandas
Series & DataFrames
Create DataFrames (List & Dictionary)
Insert, Delete, Indexing, Slicing
Hands-On: Create & manipulate DataFrames
Data Analysis with Pandas
Read CSV & JSON files
Exploratory Data Analysis (EDA)
Handle Missing, Duplicate & Outlier Data
Merge, Concat, Join, GroupBy
Date-Time functionalities
Hands-On: Perform EDA on CSV data
Data Visualization with Matplotlib
Line, Bar, Scatter, Histogram, Pie, 3D Plots
Hands-On: Create multiple visualizations
Data Visualization with Seaborn
Histogram, Boxplot, Distplot, Heatmap
Hands-On: Detect outliers with Boxplot
Assignment: Perform data analysis using NumPy, Pandas, Matplotlib & Seaborn
Introduction to Statistics
Descriptive vs Inferential Stats
Population, Sampling Techniques
Simple Random, Systematic, Stratified, Cluster Sampling
Variables & Measures
Quantitative vs Qualitative Variables
Frequency, Central Tendency, Dispersion
Variance, Standard Deviation, Z-Score
Data Distribution & Outliers
Quartile, Quantile, Percentile, Decile
Five-Number Summary & IQR
Outlier Detection & Removal
Probability & Correlation
Normal Distribution & Empirical Rule
Central Limit Theorem
Covariance & Pearson Correlation
Hypothesis Testing & Regression
Confidence Intervals
Regression Analysis
Hypothesis Testing: T-Test, Z-Test, F-Test, ANOVA, Chi-Square
Null vs Alternate Hypotheses, P-Value & Significance
Databases for AI/ML
Introduction to Databases
Relational vs Non-Relational Databases
DB vs DBMS vs SQL
SQL vs NoSQL & Database Design
Top Hiring Companies
How the Process Works
Enroll
Secure your seat by registering for the course. Our admissions team will guide you through the simple enrollment process.
Learn by Doing
Work on real AI/ML projects under expert mentorship.
Get Hired
Build a powerful portfolio and leverage our career services for job referrals, interview preparation, and placement assistance.