Certified Artificial Intelligence (AI) Associate Program

About Course

This is the Certified Artificial Intelligence (AI) Associate program, a high-demand course offered as part of the government’s Employability Enhancement and Livelihood Training Program (EELTP). This is a 240-hour, NSQF Level-4 certification course designed to provide free, job-oriented skills in the advanced fields of Artificial Intelligence and Machine Learning. The program is delivered by SimpleShpere Technologies in partnership with NIELIT to prepare students for technical roles in the IT industry.

This program is designed to build your skills for in-demand technology roles. While a detailed lesson-by-lesson syllabus isn't provided in the documents, the program will equip you with the necessary skills to become an:
ML Associate / AI Associate
Data Analyst
Machine Learning Engineer
AI Engineer
The training will involve hands-on programming with Python on both Windows and Linux operating systems.

Course Content

Module 1: Python Programming (30 Hours)
This module covers the fundamental programming skills required for all subsequent AI and Data Science modules.

Lesson 1: Introduction to Python: Understanding Python, its basics, and syntax.
Lesson 2: Program Flow and Functions: Learning to use loops, conditionals, and how to write reusable functions and modules.
Lesson 3: Exception and File Handling: Managing errors and learning how to read from and write to files.
Lesson 4: Object-Oriented Programming: Understanding classes and objects in Python.

Module 2: Statistical Concepts (20 Hours)
This module builds the mathematical foundation necessary to understand how machine learning models work.

Module 3: Data Science and Analytics (30 Hours)
This module focuses on the tools and techniques for cleaning, manipulating, and visualizing data.

Module 4: Machine Learning (80 Hours)
This is a deep dive into traditional machine learning algorithms for making predictions and finding patterns.

Module 5: Deep Learning (80 Hours)
This module covers advanced neural network architectures for complex tasks like image and text recognition.

Student Ratings & Reviews

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Certified Artificial Intelligence (AI) Associate Program

About Course

What Will You Learn?

Course Content

Module 1: Python Programming (30 Hours)
This module covers the fundamental programming skills required for all subsequent AI and Data Science modules.

Lesson 1: Introduction to Python: Understanding Python, its basics, and syntax.

Lesson 2: Program Flow and Functions: Learning to use loops, conditionals, and how to write reusable functions and modules.

Lesson 3: Exception and File Handling: Managing errors and learning how to read from and write to files.

Lesson 4: Object-Oriented Programming: Understanding classes and objects in Python.

Module 2: Statistical Concepts (20 Hours)
This module builds the mathematical foundation necessary to understand how machine learning models work.

Lesson 1: Fundamentals of Statistics: Learning Descriptive and Inferential Statistics.

Lesson 2: Probability: Understanding Marginal, Joint, and Conditional Probability, as well as Bayes’ Theorem and Probability Distributions.

Lesson 3: Hypothesis Testing: Learning how to validate statistical assumptions.

Lesson 4: Model Evaluation Concepts: Understanding Regression, Correlation, Confusion Matrix, Bias, and Variance.

Module 3: Data Science and Analytics (30 Hours)
This module focuses on the tools and techniques for cleaning, manipulating, and visualizing data.

Lesson 1: Introduction to Data Science: An overview of the data science workflow.

Lesson 2: Data Analysis with NumPy: Using the NumPy library for numerical operations.

Lesson 3: Data Manipulation with Pandas: Using the Pandas library to load, clean, and process data.

Lesson 4: Data Visualization: Creating charts and graphs using libraries like Matplotlib, Seaborne, and Plotly.

Module 4: Machine Learning (80 Hours)
This is a deep dive into traditional machine learning algorithms for making predictions and finding patterns.

Lesson 1: Introduction to Machine Learning: Understanding the core concepts and types of ML.

Lesson 2: Regression Algorithms: Building predictive models with Linear Regression and Logistic Regression.

Lesson 3: Classification Algorithms: Learning to categorize data using K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Naive Bayes, Decision Trees, and Random Forests.

Lesson 4: Unsupervised Learning: Finding hidden patterns in data using K-Means Clustering and Principal Component Analysis (PCA).

Lesson 5: Introduction to Neural Networks: A brief introduction to Artificial Neural Networks (ANN).

Module 5: Deep Learning (80 Hours)
This module covers advanced neural network architectures for complex tasks like image and text recognition.

Lesson 1: Fundamentals of Deep Learning: Understanding Artificial Neural Networks, Loss Functions, and Gradient Descent.

Lesson 2: Convolutional Neural Networks (CNN): Learning the architecture used for image recognition.

Lesson 3: Recurrent Neural Networks (RNNs): Learning the architecture used for sequential data like text or time series.

Lesson 4: Natural Language Processing (NLP): Applying deep learning to understand and process human language.

Lesson 5: Computer Vision: Using OpenCV and deep learning for computer vision tasks.

Lesson 6: Model Deployment: A conceptual overview of how to deploy trained models.

About Course

What Will You Learn?

Course Content

Module 1: Python Programming (30 Hours) This module covers the fundamental programming skills required for all subsequent AI and Data Science modules.

Lesson 1: Introduction to Python: Understanding Python, its basics, and syntax.

Lesson 2: Program Flow and Functions: Learning to use loops, conditionals, and how to write reusable functions and modules.

Lesson 3: Exception and File Handling: Managing errors and learning how to read from and write to files.

Lesson 4: Object-Oriented Programming: Understanding classes and objects in Python.

Module 2: Statistical Concepts (20 Hours) This module builds the mathematical foundation necessary to understand how machine learning models work.

Lesson 1: Fundamentals of Statistics: Learning Descriptive and Inferential Statistics.

Lesson 2: Probability: Understanding Marginal, Joint, and Conditional Probability, as well as Bayes’ Theorem and Probability Distributions.

Lesson 3: Hypothesis Testing: Learning how to validate statistical assumptions.

Lesson 4: Model Evaluation Concepts: Understanding Regression, Correlation, Confusion Matrix, Bias, and Variance.

Module 3: Data Science and Analytics (30 Hours) This module focuses on the tools and techniques for cleaning, manipulating, and visualizing data.

Lesson 1: Introduction to Data Science: An overview of the data science workflow.

Lesson 2: Data Analysis with NumPy: Using the NumPy library for numerical operations.

Lesson 3: Data Manipulation with Pandas: Using the Pandas library to load, clean, and process data.

Lesson 4: Data Visualization: Creating charts and graphs using libraries like Matplotlib, Seaborne, and Plotly.

Module 4: Machine Learning (80 Hours) This is a deep dive into traditional machine learning algorithms for making predictions and finding patterns.

Lesson 1: Introduction to Machine Learning: Understanding the core concepts and types of ML.

Lesson 2: Regression Algorithms: Building predictive models with Linear Regression and Logistic Regression.

Lesson 3: Classification Algorithms: Learning to categorize data using K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Naive Bayes, Decision Trees, and Random Forests.

Lesson 4: Unsupervised Learning: Finding hidden patterns in data using K-Means Clustering and Principal Component Analysis (PCA).

Lesson 5: Introduction to Neural Networks: A brief introduction to Artificial Neural Networks (ANN).

Module 5: Deep Learning (80 Hours) This module covers advanced neural network architectures for complex tasks like image and text recognition.

Lesson 1: Fundamentals of Deep Learning: Understanding Artificial Neural Networks, Loss Functions, and Gradient Descent.

Lesson 2: Convolutional Neural Networks (CNN): Learning the architecture used for image recognition.

Lesson 3: Recurrent Neural Networks (RNNs): Learning the architecture used for sequential data like text or time series.

Lesson 4: Natural Language Processing (NLP): Applying deep learning to understand and process human language.

Lesson 5: Computer Vision: Using OpenCV and deep learning for computer vision tasks.

Lesson 6: Model Deployment: A conceptual overview of how to deploy trained models.

Student Ratings & Reviews

Module 1: Python Programming (30 Hours)
This module covers the fundamental programming skills required for all subsequent AI and Data Science modules.

Module 2: Statistical Concepts (20 Hours)
This module builds the mathematical foundation necessary to understand how machine learning models work.

Module 3: Data Science and Analytics (30 Hours)
This module focuses on the tools and techniques for cleaning, manipulating, and visualizing data.

Module 4: Machine Learning (80 Hours)
This is a deep dive into traditional machine learning algorithms for making predictions and finding patterns.

Module 5: Deep Learning (80 Hours)
This module covers advanced neural network architectures for complex tasks like image and text recognition.