ACLM Institute of Professional Studies

Artificial Intelligence (AI) Course

Artificial Intelligence (AI) Course for students and working professionals

08 MonthsOnline / OfflineAdvancedACLM CertificationACLM Certification

The Artificial Intelligence (AI) course is designed to equip learners with a solid foundation in AI, practical tools (like ChatGPT, Bard, Copilot), machine learning models, Python programming, data preprocessing, and real-world projects. It blends theory with hands-on tools like Google Colab, Scikit-learn, TensorFlow, and AutoML platforms.

COURSE OVERVIEW

Course Overview

The ACLM Institute of Professional Studies Artificial Intelligence (AI) Course is an advanced, practical 08-month programme for students and working professionals. It covers Python for AI, data preprocessing, visualisation, machine learning, deep learning with TensorFlow and Keras, natural language processing, generative AI, prompt engineering, AI API integration, business applications and no-code AI tools. Training is delivered through online and offline modes, with instructor-led, one-on-one and group training options. Offline training is available in Greater Noida West, Greater Noida, Noida and Delhi NCR. Online learning is available for learners in India and international markets.

WHY THIS COURSE

Why Learn This Course?

Artificial Intelligence is being applied across business, education, marketing, sales, HR, automation, content creation and data-driven decision-making. This course provides a structured path from Python and data preparation to machine learning, deep learning, NLP and generative AI. Learners work on practical assignments and projects involving prediction, classification, clustering, image recognition, sentiment analysis and AI assistant development.

LEARNING OUTCOMES

What You Will Learn

AI, machine learning, deep learning and generative AI concepts
Career paths and professional roles in the AI domain
Python programming essentials for AI development
NumPy, Pandas, Matplotlib and Jupyter or Google Colab workflows
Data cleaning, missing-value handling, categorical encoding, scaling and normalisation
Exploratory data analysis and visualisation using Matplotlib and Seaborn
Regression, classification and clustering models
Model evaluation metrics and interpretation of results
Neural network fundamentals and deep learning workflows using TensorFlow and Keras
CNN and image-processing basics
Text cleaning, vectorisation and sentiment analysis
Chatbot fundamentals using Dialogflow or Python
Prompt engineering and ChatGPT integration
OpenAI API integration concepts and practical use cases
AI applications in marketing, sales, HR and education
No-code AI tools including Teachable Machine, Pictory, Leonardo.ai and AutoML

SKILLS

Skills You Will Gain

Build and interpret introductory-to-intermediate AI and machine learning models
Prepare, transform and visualise structured datasets
Apply regression, classification and clustering techniques to practical problems
Develop basic neural-network and CNN-based solutions
Perform text preprocessing and sentiment analysis
Create chatbot and mini AI-assistant prototypes
Write effective prompts for generative AI tools
Integrate generative AI capabilities through API-based workflows
Use AI tools for business and productivity use cases
Present AI projects with suitable documentation, assignments and results

WHO SHOULD JOIN

Who Should Join?

Students and working professionals who want to develop practical skills in artificial intelligence, machine learning, data analysis and generative AI.

PREREQUISITES

Prerequisites

Basic knowledge of programming in Python, JavaScript and databases is required. Familiarity with basic programming logic will help learners progress through the practical exercises.

TRAINING APPROACH

Training Methodology

ACLM Institute of Professional Studies delivers the course through instructor-led, one-on-one and group training. The programme includes custom notes, projects, assignments and ebooks. Learning is structured around demonstrations, guided coding, model-building exercises, tool-based activities and project work. The course is available online for India and international markets, and offline in Greater Noida West, Greater Noida, Noida and Delhi NCR.

CURRICULUM

Detailed Course Syllabus

Module	Topics / Lessons
Module 1: Introduction to AI and Career Landscape Establish the conceptual foundation of artificial intelligence and distinguish AI, machine learning, deep learning and generative AI. Learners also examine real-life applications and possible AI-related roles.	What Is Artificial Intelligence? — Meaning, objectives, capabilities and limitations of AI systems. 90 min AI, Machine Learning and Deep Learning — Understand the relationship between AI, ML and DL through practical examples. 90 min Real-Life AI Applications — Explore AI use cases in business, education, marketing, sales, HR, automation and content creation. 90 min Career Paths and AI Roles — Review skill areas associated with AI developer, AI data scientist and related technical roles. 90 min Introduction to Generative AI — Understand the purpose and common uses of ChatGPT, Gemini, Claude and Copilot. 90 min
Module 2: Python for AI Build the programming foundation required for data preparation, visualisation and model development. Exercises progress from Python basics to notebook-based data work.	Python Environment Setup — Set up and use Jupyter Notebook and Google Colab for AI exercises. 90 min Variables, Data Types and Operators — Practise storing, converting and processing values in Python. 90 min Conditions, Loops and Functions — Develop reusable logic for calculations and data-processing tasks. 120 min Lists, Dictionaries and File-Oriented Workflows — Use core Python structures to organise and process information. 90 min NumPy Fundamentals — Work with arrays and numerical operations used in AI workflows. 120 min Pandas for Data Handling — Load, inspect, filter and transform tabular data. 120 min Matplotlib and Notebook Visualisation — Create basic charts and communicate patterns in data. 90 min Python Practice Projects — Build a BMI Calculator, Student Grading System and Data Summary App. 180 min
Module 3: Data Preprocessing and Visualisation Learn how to convert raw datasets into analysis-ready inputs and use visual exploration to understand data quality, relationships and patterns.	Understanding Dataset Structure — Inspect rows, columns, data types, distributions and basic summaries. 90 min Handling Missing Data — Identify missing values and apply suitable handling approaches. 120 min Encoding Categorical Variables — Convert categorical information into model-ready representations. 120 min Scaling and Normalisation — Understand why feature scales matter and apply suitable transformations. 120 min Exploratory Data Analysis — Combine summaries and visual checks to investigate a dataset. 120 min Visualisation with Matplotlib and Seaborn — Create comparison, distribution and relationship plots. 120 min EDA Project: IPL, Titanic or Sales Data — Complete a guided EDA project and present observations from the data. 180 min
Module 4: Machine Learning Models Introduce supervised and unsupervised learning through model-building exercises. Learners compare algorithms, prepare inputs and interpret evaluation results.	Machine Learning Workflow — Frame a problem, identify features and target variables, split data and plan an experiment. 120 min Supervised and Unsupervised Learning — Understand the difference between labelled and unlabelled learning tasks. 90 min Linear and Polynomial Regression — Build models for numerical prediction and examine model behaviour. 150 min Logistic Regression and K-Nearest Neighbours — Apply introductory classification methods to labelled data. 150 min Support Vector Machines — Understand the classification principle and practise a guided implementation. 120 min K-Means Clustering — Group observations into clusters and interpret the results. 120 min DBSCAN Clustering — Explore density-based clustering and compare it with K-Means. 120 min Model Evaluation Metrics — Use suitable metrics to review regression, classification and clustering results. 150 min Applied ML Projects — Work on salary prediction, fraud detection and customer segmentation scenarios. 240 min
Module 5: Deep Learning with TensorFlow and Keras Move from traditional machine learning to neural-network workflows, including activation functions, image inputs and CNN fundamentals.	Neural Network Fundamentals — Understand neurons, layers, weights, training and prediction. 120 min Activation Functions — Study the purpose of activation functions in neural-network learning. 90 min TensorFlow and Keras Workflow — Prepare data, define a model, train it and review results. 150 min Training, Validation and Evaluation — Understand training behaviour and assess model performance. 120 min CNN Fundamentals — Learn the role of convolution and pooling in image-processing tasks. 150 min Image Processing Basics — Prepare image inputs and discuss practical considerations for image models. 120 min MNIST Handwritten Digit Recognition — Build a guided digit-recognition project using a neural-network or CNN workflow. 240 min
Module 6: Natural Language Processing Introduce the core steps involved in working with text data, from cleaning and vectorisation to sentiment analysis and chatbot foundations.	NLP Concepts and Text Data — Understand common NLP tasks and the challenges of language data. 90 min Text Cleaning — Prepare text by handling case, punctuation, unwanted content and common variations. 120 min Text Vectorisation — Convert text into numerical representations suitable for analysis and modelling. 150 min Sentiment Analysis — Build and evaluate a basic sentiment-analysis workflow on tweets or product reviews. 180 min Chatbot Basics with Dialogflow or Python — Plan intents, inputs and responses for a basic chatbot experience. 180 min NLP Project Review — Document the text-processing pipeline, results, limitations and possible improvements. 120 min
Module 7: Generative AI and ChatGPT Integration Develop practical generative AI skills, from prompt design to integrating AI responses into simple workflows and productivity applications.	Generative AI Concepts — Understand generated text and media, common use cases and important limitations. 90 min Prompt Engineering Foundations — Create clear prompts using context, instructions, constraints, examples and desired output formats. 150 min Prompt Testing and Refinement — Compare responses, identify weaknesses and improve prompts systematically. 120 min ChatGPT Integration Workflow — Design a mini AI assistant using structured inputs and generated responses. 150 min OpenAI API Integration Concepts — Understand the components of an API-based generative AI workflow and responsible handling of outputs. 150 min AI Productivity and Creative Tools — Explore Canva AI, Notion AI, D-ID and Eleven Labs for selected content and productivity use cases. 150 min Generative AI Projects — Build a mini AI assistant and an AI Resume Builder or Email Generator. 240 min
Module 8: AI in Business and No-Code AI Apply AI concepts to business functions and explore no-code tools that support experimentation, content creation and model prototyping.	AI in Marketing and Sales — Examine use cases for content, customer understanding, communication and workflow support. 120 min AI in HR and Education — Explore responsible applications for learning, people processes and information support. 120 min No-Code AI Overview — Understand when no-code tools can support prototyping and experimentation. 90 min Teachable Machine and AutoML — Explore no-code model training concepts and review the resulting outputs. 150 min Pictory and Leonardo.ai — Practise selected content and creative workflows using AI-enabled tools. 120 min Responsible AI Practice — Discuss privacy, consent, accuracy, bias, human review and appropriate use of AI outputs. 120 min Final Project Presentation — Present a selected AI project, explain the workflow, review results and identify next steps. 180 min

HANDS-ON EXPERIENCE

Practical Projects

Python Application Foundations — Build a BMI Calculator, Student Grading System and Data Summary App to practise variables, conditions, loops, functions, input handling and basic data processing. Tools: Python, Jupyter Notebook or Google Colab
Exploratory Data Analysis Project — Perform data cleaning, summary analysis and visualisation on IPL, Titanic or sales data. Learners identify patterns, compare variables and communicate findings through charts. Tools: Python, Pandas, NumPy, Matplotlib and Seaborn
Salary Prediction — Prepare a suitable dataset, train a regression model, evaluate prediction performance and interpret the factors associated with the predicted salary outcome. Tools: Python, Pandas and machine learning libraries
Fraud Detection — Frame a classification problem, prepare the data, train a suitable model and assess results using relevant evaluation metrics, with attention to imbalanced outcomes. Tools: Python, Pandas and machine learning libraries
Customer Segmentation — Use clustering techniques to group customers based on selected attributes and present the business meaning of the resulting segments. Tools: Python, Pandas, visualisation tools, K-Means or DBSCAN
Handwritten Digit Recognition — Develop a basic image-recognition workflow using the MNIST dataset, covering image preparation, neural-network training and result evaluation. Tools: Python, TensorFlow and Keras
Sentiment Analysis — Clean and vectorise tweets or product reviews, classify sentiment and review the strengths and limitations of the resulting NLP workflow. Tools: Python, NLP techniques and machine learning libraries
Mini AI Assistant — Build a mini AI assistant using ChatGPT integration, focusing on prompt design, user inputs, response handling and responsible use of generated output. Tools: Python, ChatGPT and OpenAI API integration concepts
AI Resume Builder or Email Generator — Create a practical generative AI workflow for drafting resumes or emails, with structured prompts and review steps for accuracy, relevance and user control. Tools: ChatGPT, prompt engineering and selected AI productivity tools
Face Recognition or Voice Cloning Application — Create a guided prototype exploring face-recognition or voice-cloning concepts, with emphasis on consent, privacy, appropriate use and responsible handling of personal data. Tools: Python and relevant image or audio AI tools

CAREER APPLICATION

Career Opportunities

The course supports skill development for roles and projects such as AI Developer, AI Data Scientist, Machine Learning Practitioner, Data Analyst with AI skills, Generative AI Practitioner and AI-focused business or automation contributor. Actual role suitability depends on the learner’s prior education, portfolio, technical ability and experience.

CERTIFICATION

Certification

Learners who complete the applicable course requirements can receive an ACLM Certificate of Participation. The certification is issued by ACLM Institute of Professional Studies and does not represent an employment, placement, accreditation or university-affiliation claim.

FAQ

Frequently Asked Questions

What is the duration of the ACLM Artificial Intelligence Course?

The course duration is 08 Months.

Who can join this course?

The course is designed for students and working professionals.

What are the prerequisites?

Basic knowledge of programming in Python, JavaScript and databases is required.

Is the course available online and offline?

Yes. Training is available online and offline. Offline training is available in Greater Noida West, Greater Noida, Noida and Delhi NCR. Online learning is available for India and international markets.

Is recorded training available?

No. The supplied course format specifies online and offline training; recorded training is not available.

What training methods are offered?

ACLM provides instructor-led, one-on-one and group training, supported by custom notes, assignments, projects and ebooks.

Which technologies and tools are covered?

The curriculum includes Python, NumPy, Pandas, Matplotlib, Seaborn, Jupyter, Google Colab, TensorFlow, Keras, Dialogflow or Python chatbot workflows, ChatGPT, OpenAI API integration concepts, Canva AI, Notion AI, D-ID, Eleven Labs, Teachable Machine, Pictory, Leonardo.ai and AutoML.

What projects will learners work on?

Projects include EDA, salary prediction, fraud detection, customer segmentation, MNIST digit recognition, sentiment analysis, a mini AI assistant, an AI resume or email generator, and a face-recognition or voice-cloning prototype.

Is certification available?

Yes. Eligible learners can receive an ACLM Certificate of Participation.

What skills can learners develop?

Learners can develop skills in building AI models, AI development and AI-focused data science, along with practical Python, machine learning, deep learning, NLP and generative AI skills.

START YOUR JOURNEY

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