Artificial Intelligence Associate
Become an AI Expert and Advance Your Career
Learn AI fundamentals, machine learning concepts, and practical tools through hands-on classroom training designed for beginners and freshers.
Duration :- 450 Hrs
Gain insight into a topic and learn the fundamentals.
⭐ 4.8
Most of student like this course and got placed.
Advance Level
Recommended for career Growth by Career Experts
Offline Classroom Course
Recommended for career Growth by Career Experts
What you'll learn
This course starts with Python programming, where students learn from basics to modular project development. They understand Python syntax, functions, and data structures, and then move into Data Science concepts such as data cleaning, preprocessing, and analysis using NumPy and Pandas, along with Exploratory Data Analysis (EDA) techniques.
Next, students learn data visualization using Matplotlib and Seaborn and are introduced to the fundamentals of Machine Learning. They build, train, and evaluate ML models using regression and classification techniques, understand model performance and accuracy, and finally work on end-to-end AI & ML projects to gain real-world experience.
Skills You’ll Gain
- Python programming & logical problem-solving
- Data preprocessing and feature engineering
- Statistical analysis for data-driven decision making
- Data visualization & storytelling with data
- Machine Learning model development using Scikit-Learn
- Predictive analytics and trend identification
- Analytical thinking for real-world business problems
- Hands-on project execution and job-ready technical skills
After completing this course, learners will be able to:
- Write, debug, and manage Python programs efficiently
- Convert raw and unstructured data into meaningful insights
- Analyze large datasets and draw statistical conclusions
- Build and deploy Machine Learning models for prediction tasks
- Measure and improve model accuracy using appropriate metrics
- Understand the complete lifecycle of an AI/ML project
- Apply AI and Data Science concepts to real industry use-cases
- Be job-ready with technical and employability skills
Programming with Python
- Installation and configuration of Python IDE
- Understanding Python syntax, data types, operators, and control flow
- Working with built-in data structures: lists, tuples, dictionaries, and sets
- Creating and using functions for structured programming
- Learning modular programming and package management
- Understanding different ways of passing parameters to functions
- Developing a small project using multiple modules and packages
Conceptualizing Data Science with Python
- Introduction to Data Science concepts and commonly used tools
- Understanding raw, unstructured, and structured data
- Data preprocessing and cleaning techniques
- Converting raw data into meaningful and usable formats
- Using NumPy for numerical operations
- Working with n-dimensional arrays
- Applying basic statistical analysis using Python
Data Analysis and Visualization
- Understanding the difference between NumPy and Pandas
- Working with Pandas DataFrames and Series
- Cleaning and organizing messy datasets
- Filtering, merging, segmenting, and transforming data
- Performing Exploratory Data Analysis (EDA)
- Using Matplotlib and Seaborn for data visualization
- Understanding business problems through visual insights
Fundamentals of Machine Learning
- Introduction to Machine Learning concepts
- Understanding types of learning: supervised, unsupervised, and reinforcement
- Setting up the Machine Learning environment using Scikit-learn
- Understanding applications of Machine Learning
- Learning classification and regression algorithms
- Building and evaluating Machine Learning models
- Understanding the complete lifecycle of an AI project
Performance and Accuracy of Machine Learning Models
- Predictive analysis using regression techniques
- Predictive analysis using classification techniques
- Applying statistical methods such as correlation and hypothesis testing
- Understanding normal distribution in model building
- Feature selection techniques to improve models
- Evaluating models using classification and regression metrics
- Implementing an end-to-end Machine Learning project
- Understanding how model accuracy impacts business problems
Employability Skills
- Developing workplace-ready professional skills
- Improving communication and job readiness
- Enhancing overall employability alongside technical skills
On-the-Job Training (OJT) / Project
- Practical exposure to real working environments
- Applying technical knowledge in job-based scenarios
- Understanding industry workflows and expectations
Our Courses
GET STARTED
Start Your Career Today
Fill out the form and our senior career counselor will get in touch with you within 24 hours for personalized guidance
- Free Career Counseling Session
- Personalized Learning Path
- Industry-Grade Projects & Labs
- 1:1 Mock Interview Sessions