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AI/ML, Automation Tools & Interview Preparation Program

A 12-module job-readiness syllabus — from AI/ML foundations and automation tools to a placement-ready capstone, resume and mock interviews. Built for freshers and first-time job seekers.

Program Overview

Job-ready in AI/ML, automation & interview skills — from zero.

This program builds job-ready capability in Artificial Intelligence, Machine Learning, and modern automation tools, and pairs that technical foundation with structured interview and career-readiness preparation. It's designed for freshers, final-year students, and first-time job seekers targeting entry-level roles in Data/AI/ML, Automation, or IT services — with basic computer literacy but no mandatory coding background.

Across 12 modules and roughly 74 contact hours, you'll move from core AI/ML concepts through deep learning and generative AI, into RPA and no-code/low-code automation platforms, and finish with a portfolio-ready capstone project plus dedicated resume, personal branding and mock-interview preparation.

🐍 Python & Jupyter/Colab 📊 NumPy & Pandas 🤖 scikit-learn 🧠 TensorFlow / Keras 💬 NLTK / spaCy / Hugging Face ⚙️ UiPath / Power Automate (RPA) 🔗 Zapier / Make 🗂️ Excel, Power Query & Copilot
ModeLive Online
LevelBeginner · Freshers & First-Time Job Seekers
Duration10–11 Weeks (~74 Contact Hrs)
Modules12
PrerequisitesBasic computer literacy only
CertificateDataSpeaks Certificate
FeeContact for current pricing
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How the Program Is Built

Structure, outcomes & assessment at a glance

The full week-by-week, topic-by-topic curriculum unlocks after registration below — here's the shape of the program.

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Program Structure

  • Modules 1–7: AI/ML foundations, from core concepts to deep learning and generative AI
  • Modules 8–9: Automation tools — RPA and no-code/low-code AI productivity platforms
  • Module 10: Capstone project integrating ML and/or automation skills
  • Modules 11–12: Resume, personal branding & structured interview preparation
🎯

Learning Outcomes

  • Explain core AI/ML concepts and apply them using Python and scikit-learn/TensorFlow
  • Build and evaluate supervised and unsupervised ML models, and a basic neural network
  • Use generative AI tools responsibly and write effective prompts
  • Design and deploy a basic RPA bot and a no-code automated workflow
  • Deliver a portfolio-ready capstone, an ATS-friendly resume, and clear a technical + HR interview
📝

Assessment

  • Module-end quizzes and hands-on labs (formative assessment)
  • Capstone project evaluation (rubric-based)
  • Final mock technical and HR interviews with structured feedback
Full Curriculum

12 modules, week by week — unlocked after registration

Register for this course to unlock the complete module-by-module syllabus, learning objectives, tools and reference documents.

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Full Curriculum Locked

Register your interest in the AI/ML, Automation Tools & Interview Preparation program to unlock the complete 12-module syllabus — learning objectives, detailed topics, tools and module outcomes for every week.

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1

Foundations of AI, ML & the Digital Economy

Week 1
5 hrs
Learning Objectives
  • Distinguish between AI, Machine Learning, Deep Learning, and Data Science
  • Understand how AI and automation are reshaping industries and jobs
  • Identify ethical considerations and career pathways in AI/ML and automation
Detailed Topics
  • What is AI, ML, Deep Learning and Data Science — definitions, relationships & a Venn-diagram view
  • A short history: rule-based systems, statistical ML, and the rise of deep learning & generative AI
  • Types of Machine Learning: supervised, unsupervised, reinforcement — with everyday examples
  • Real-world applications across healthcare, finance, retail, manufacturing & customer service
  • Introduction to AI ethics: bias in data/models, privacy, and responsible AI use
  • Career pathways: Data Analyst, ML Engineer, AI Engineer, RPA/Automation Developer
Tools & Technologies
Google Colab ChatGPT / Claude demos No installation required
Module Outcome: Learners can correctly define core AI/ML terms, map at least 3 real-world use cases to ML types, and articulate 2–3 career paths relevant to their interests.
2

Python Programming Essentials for AI/ML

Week 1–2
8 hrs
Learning Objectives
  • Write and debug basic Python programs
  • Use core data structures and control flow confidently
  • Perform introductory data manipulation with NumPy and Pandas
Detailed Topics
  • Python setup: Anaconda/Google Colab, Jupyter Notebook basics, running first scripts
  • Core syntax: variables, data types, operators, and input/output
  • Control flow: if-else, for/while loops, and list/dict comprehensions
  • Functions, arguments, and an introduction to OOP (classes, objects)
  • Data structures: lists, tuples, dictionaries, sets, and file handling (CSV/text)
  • Introduction to NumPy (arrays, indexing, vectorized ops) and Pandas (Series, DataFrames)
Tools & Technologies
Python 3 Jupyter Notebook / Google Colab VS Code NumPy Pandas
Module Outcome: Learners can write a 30–40 line Python script that reads a CSV file, applies basic filtering/transformation with Pandas, and prints summary output without instructor assistance.
3

Data Handling, Statistics & Exploratory Data Analysis (EDA)

Week 2–3
7 hrs
Learning Objectives
  • Apply core descriptive statistics and probability concepts to data
  • Clean and prepare messy, real-world datasets
  • Explore and visualize data to surface patterns and insights
Detailed Topics
  • Descriptive statistics: mean, median, mode, variance, standard deviation, percentiles
  • Probability basics: distributions (normal, binomial) and why they matter for ML
  • Data cleaning: missing values, duplicates, outliers, inconsistent formats
  • Feature scaling & encoding: normalization, standardization, one-hot/label encoding
  • Data visualization: bar/line/scatter/box plots & histograms using Matplotlib and Seaborn
  • End-to-end EDA workflow: dataset understanding, correlation analysis, initial insights
Tools & Technologies
Pandas NumPy Matplotlib Seaborn Microsoft Excel
Module Outcome: Learners can take a raw, unclean dataset, clean it, and produce a short EDA report with at least 4 visualizations and written insights.
4

Supervised Learning: Regression & Classification

Week 3–4
8 hrs
Learning Objectives
  • Understand how supervised learning algorithms work conceptually and mathematically at a basic level
  • Build, train, and evaluate regression and classification models
  • Select appropriate evaluation metrics for a given problem
Detailed Topics
  • Supervised learning workflow: train-test split, model fitting, prediction, evaluation
  • Regression: simple & multiple linear regression, assumptions, interpreting coefficients
  • Classification: logistic regression, decision trees, random forest, KNN, intro to SVM
  • Evaluation metrics: RMSE/MAE; accuracy, precision, recall, F1-score, confusion matrix
  • Cross-validation basics and why a single train-test split can be misleading
  • Hands-on: house-price regression model and customer-churn classification model
Tools & Technologies
scikit-learn Pandas Matplotlib / Seaborn Jupyter Notebook
Module Outcome: Learners can independently build, train, and evaluate one regression model and one classification model on a provided dataset, and justify the metrics used.
5

Unsupervised Learning & Model Evaluation

Week 4
6 hrs
Learning Objectives
  • Apply clustering techniques to find patterns in unlabeled data
  • Use dimensionality reduction to simplify complex datasets
  • Diagnose and address overfitting/underfitting through tuning
Detailed Topics
  • Clustering: K-Means and hierarchical clustering, choosing cluster count (elbow method)
  • Dimensionality reduction: Principal Component Analysis (PCA) concepts & use cases
  • Introduction to anomaly/outlier detection
  • Bias-variance tradeoff, overfitting vs underfitting, regularization basics
  • Model evaluation deep-dive: ROC-AUC curve, precision-recall tradeoff
  • Hyperparameter tuning basics using Grid Search / Randomized Search
Tools & Technologies
scikit-learn Pandas Matplotlib / Seaborn
Module Outcome: Learners can segment a customer dataset using K-Means, reduce its dimensions with PCA, and explain a model's bias-variance behavior.
6

Introduction to Deep Learning & Neural Networks

Week 5
7 hrs
Learning Objectives
  • Understand the building blocks of neural networks
  • Differentiate between CNNs and RNNs and their typical use cases
  • Build and train a simple neural network
Detailed Topics
  • From a single perceptron to multi-layer networks: weights, biases, activations (ReLU, Sigmoid, Softmax)
  • Forward propagation, loss functions, and an intuitive view of backpropagation & gradient descent
  • Neural network architecture: input/hidden/output layers; epochs, batch size, learning rate
  • Introduction to Convolutional Neural Networks (CNNs) for image tasks
  • Introduction to Recurrent Neural Networks (RNNs) for sequential/time-series data
  • Hands-on: training a simple image or tabular-data classifier using Keras/TensorFlow on Colab
Tools & Technologies
TensorFlow / Keras PyTorch (alternative) Google Colab
Module Outcome: Learners can build, train, and evaluate a simple feed-forward or CNN model on a sample dataset (e.g., MNIST) and explain each layer's role.
7

NLP, Generative AI & Prompt Engineering

Week 5–6
7 hrs
Learning Objectives
  • Apply core Natural Language Processing techniques to text data
  • Understand how modern generative AI (LLMs) work at a conceptual level
  • Write effective, safe prompts for AI productivity tools
Detailed Topics
  • Text preprocessing: tokenization, stopword removal, stemming, lemmatization
  • Text representation: Bag-of-Words and TF-IDF; a basic sentiment analysis project
  • Conceptual introduction to transformers, embeddings, and LLMs such as GPT and BERT
  • Prompt engineering: role prompting, few-shot examples, chain-of-thought, iterative refinement
  • Using ChatGPT/Claude/Gemini responsibly for research, writing, coding help & summarization
  • Understanding hallucinations, bias, and limits of generative AI; verifying AI output
Tools & Technologies
NLTK / spaCy ChatGPT / Claude / Gemini Hugging Face (intro)
Module Outcome: Learners can perform a basic sentiment-analysis task on text data and demonstrate 3–4 prompt-engineering techniques on a real task.
8

Automation Fundamentals & RPA (Robotic Process Automation)

Week 6–7
7 hrs
Learning Objectives
  • Distinguish automation, RPA, and AI-driven automation
  • Identify processes suitable for automation
  • Design and run a basic RPA bot
Detailed Topics
  • Automation vs RPA vs AI-driven/intelligent automation — where each fits in a business
  • Identifying automatable processes: repetitive, rule-based, high-volume tasks
  • RPA architecture: recorder, workflows, variables, arguments, control flow
  • Building a bot: UI automation basics, Excel automation, email/file-handling automation
  • Exception handling, logging, and basic bot reliability practices
  • Hands-on lab: automate a repetitive task — extract data from Excel, generate a report/email
Tools & Technologies
UiPath Studio (Community Edition) Microsoft Power Automate Desktop
Module Outcome: Learners can independently design, build, and run a working RPA bot that automates a real multi-step office task end-to-end.
9

No-Code/Low-Code & AI Productivity Tools

Week 7
6 hrs
Learning Objectives
  • Automate cross-application workflows without writing code
  • Use AI copilots and low-code tools to boost everyday productivity
  • Understand how automation tools connect to APIs at a conceptual level
Detailed Topics
  • App-to-app automation with Zapier/Make: triggers, actions, multi-step scenarios
  • Microsoft 365/Google Workspace automation: Excel Power Query, macros, intro to Power Apps
  • AI copilots in daily work: Microsoft Copilot / Google Gemini for docs, sheets & email drafting
  • Building a simple FAQ chatbot using a no-code/low-code platform
  • Conceptual introduction to APIs and where AI/ML models fit into automation tools
  • Hands-on lab: connect two or more apps into one automated workflow
Tools & Technologies
Zapier / Make Power Automate Excel Power Query Microsoft Copilot / Google Gemini
Module Outcome: Learners can build and demonstrate one working automated workflow that connects at least two applications without writing code.
10

Capstone Project: Applied AI/ML & Automation

Week 8–9
10 hrs
Learning Objectives
  • Integrate skills from Modules 1–9 into one applied, end-to-end project
  • Practice project planning, documentation, and presentation
  • Produce a portfolio-ready artifact for job applications
Detailed Topics
  • Project selection from a curated list (churn prediction, resume-screening automation, FAQ chatbot, sales-dashboard automation, review sentiment analysis)
  • Project planning: scope, success criteria, and a simple timeline
  • Data collection/preparation or workflow design, depending on project type
  • Model building or automation implementation using tools from earlier modules
  • Documentation: problem statement, approach, results, learnings write-up
  • Final presentation and demo, with peer and instructor feedback
Tools & Technologies
Learner's choice — Modules 2–9 toolset
Module Outcome: Each learner delivers one complete, documented project with a working demo, suitable for inclusion in a resume/portfolio.
11

Resume Building, LinkedIn & Personal Branding

Week 9–10
5 hrs
Learning Objectives
  • Create an ATS-friendly, achievement-oriented resume
  • Build a professional, recruiter-ready LinkedIn profile
  • Establish a basic personal brand and portfolio presence
Detailed Topics
  • Resume structure: contact info, summary, skills, projects, education, ATS formatting
  • Writing achievement-based bullet points (action-verb + task + result)
  • Tailoring a resume to specific job descriptions using keyword matching
  • LinkedIn profile optimization: headline, About section, skills, endorsements, featured projects
  • Building a simple portfolio: GitHub repo structure or a one-page personal site
  • Networking basics: connecting with recruiters/alumni, informational outreach messages
Tools & Technologies
MS Word / Canva templates LinkedIn GitHub
Module Outcome: Each learner leaves with a finalized, ATS-friendly resume, an optimized LinkedIn profile, and a shareable portfolio link.
12

Interview Preparation: Technical, HR & Mock Interviews

Week 10–11
8 hrs
Learning Objectives
  • Confidently answer common AI/ML and automation technical questions
  • Handle HR and behavioral questions using a structured method
  • Perform under real interview conditions and act on feedback
Detailed Topics
  • Common technical questions: AI/ML concepts, Python basics, SQL fundamentals, automation/RPA scenarios
  • Aptitude and logical reasoning practice for entry-level screening rounds
  • Group discussion techniques and case-study style problem solving
  • HR round prep: "Tell me about yourself", strengths/weaknesses, fresher-specific & salary questions
  • Behavioral questions using the STAR method, built from capstone project experience
  • Two mock interviews (technical + HR) with structured, actionable feedback
  • Workplace readiness: professional etiquette, communication basics, first-90-days expectations
Tools & Technologies
Curated question banks Mock-interview role-play Video self-review (optional)
Module Outcome: Each learner completes one mock technical interview and one mock HR interview, receives written feedback, and can articulate their capstone project confidently in under 3 minutes.
Outcomes

What you'll walk away with

✅

Skills You'll Gain

  • Ability to explain and apply core AI/ML concepts with Python and scikit-learn/TensorFlow
  • Confidence building and evaluating supervised, unsupervised & basic neural network models
  • Practical, responsible use of generative AI tools and effective prompt writing
  • Hands-on RPA bot-building and no-code automated workflow skills
  • A completed capstone project, ATS-friendly resume, and interview-ready presentation skills
🎯

Career Paths This Opens

  • Data Analyst
  • Machine Learning Engineer (Entry-Level)
  • AI Engineer
  • RPA / Automation Developer
  • Entry-level roles across IT services & Data/AI/ML teams

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