Internship Assured - Advanced AI Training (Data Science, Machine Learning, Deep Learning & Computer Vision)

  • Course Duration 75 Hours
  • Course Mode Instructor Led Online Training
  • Date & Time 22-May-2025

About The Course

Welcome to the AI adventure where coffee meets code and neural networks dream of electric sheep! This hands-on, no-nonsense course takes you from "Hello World" in Python to deploying AI models that could practically run a startup. Whether you're untangling SQL joins like a data detective, teaching machines to predict house prices or diagnose diseases from MRI scans (yep, like a robot doctor), or building chatbots that could rival your nosy neighbor — this course has it all. And here’s the kicker: you’re not just learning in isolation — you’re backed by the big leagues! We’re proud partners of the NVIDIA Inception Program, Microsoft Solution Partner, and part of the AWS Partner Network — so expect cutting-edge tech, industry credibility, and cloud power at your fingertips. By the end, you’ll not only know AI—you’ll become the AI whisperer every company wants. Bonus: no prior mind-reading skills required, just curiosity and caffeine!

Key Features

Instructor-Led, Interactive Training

Live, expert-led sessions for hands-on learning

Lifetime Access to Recordings

Revisit recorded classes anytime, at your convenience

Assignments & Real-Time Projects

Apply skills through practical projects after every module

Lifetime Job Assistance

Ongoing support for AI and Data Science job opportunities

3 Years of Technical Support

24/7 query resolution and technical help for 3 years

Globally Recognized Certification

Certified by AiCouncil, backed by Microsoft & AWS

Highlights

Comprehensive Python programming with NumPy, Pandas, Matplotlib, Seaborn, and hands-on feature engineering techniques.

Exploratory Data Analysis (EDA) including data cleaning, handling missing values, outlier detection, correlation analysis, and descriptive statistics.

Machine Learning foundations including regression, classification, clustering, dimensionality reduction, and evaluation metrics like ROC-AUC and confusion matrix.

Deep dive into models like Naïve Bayes, SVM, Decision Trees, Random Forest, and Gradient Boosting, with mathematical intuition and practical implementation.

End-to-End Deep Learning with TensorFlow and Keras, covering ANN, CNN, backpropagation, regularization, and model tuning with real-world datasets.

Image recognition projects using CNNs, such as disease detection from MRI scans, and digit classification with MNIST using ANNs.

Generative AI & LLMs using transformers, ChatGPT API, RNN-LSTM, and prompt engineering for real-time text generation and chatbot development.

Flask-based ML deployment, REST APIs, Postman testing, and web interfaces using HTML and JavaScript for real-time prediction applications.

Hands-on recommendation engines & market basket analysis, using association rules and collaborative filtering techniques.

Capstone project integrating EDA, ML, Deep Learning & BI tools, solving a real-world business challenge from scratch.

Industry-grade project portfolio including customer churn prediction, price forecasting, product recommendation, and more.

Certification from AiCouncil & iHub Divyasampark IIT Roorkee under the Department of Science & Technology, Govt. of India.

Official Partnerships with NVIDIA Inception, Microsoft Solution Partner, and AWS Partner Network for cutting-edge tools, cloud credits, and innovation support.

Course Agenda

  • What is Data Science?
  • Key Roles and Responsibilities
  • Data Science Project Life Cycle
  • Business Intelligence vs Data Science
  • Introduction to Big Data, Hadoop, Python, R, and Machine Learning
  • Why Python for Data Science
  • Setting Up Your Python Environment
  • Libraries Overview: NumPy, Pandas, Matplotlib, Seaborn
  • Data Acquisition Techniques
  • Raw vs Processed Data
  • Data Cleaning and Transformation
  • Exploratory Data Analysis (EDA)
  • Data Visualization with Matplotlib and Seaborn
  • Data Wrangling in Python
  • Data Visualization Practice
  • What is Machine Learning?
  • Supervised vs Unsupervised Learning
  • Real-World Applications
  • Simple and Multiple Linear Regression
  • Assumptions Behind Regression Models
  • Evaluation Metrics: R², MSE
  • Train-Test Splits
  • Building and Evaluating Regression Models
  • Why Dimensions Matter
  • Curse of Dimensionality Explained
  • Principal Component Analysis (PCA): Theory and Implementation
  • Factor Analysis
  • Feature Scaling and Normalization
  • Applying PCA
  • Scaling Techniques
  • What is Classification?
  • Linear vs Logistic Regression
  • Math Behind Logistic Regression: Logit Function, Odds, Likelihood
  • Confusion Matrix, ROC, AUC
  • Evaluating Model Thresholds
  • Logistic Regression Model Building
  • Confusion Matrix and ROC Curve Analysis
  • Introduction to Tree-Based Models
  • Information Gain, Gini Index, Entropy
  • Overfitting and Pruning Techniques
  • Random Forests: Bagging, Feature Importance, Finding the Optimal Number of Trees
  • Decision Tree Construction
  • Random Forest Hyperparameter Tuning
  • Understanding Bagging
  • Random Forests Deep Dive
  • Boosting (Brief Introduction)
  • Introduction to Probabilistic Models
  • Naïve Bayes Classifier: Bayes Theorem and Applications
  • Support Vector Machines (SVM): Concepts and Kernel Tricks, Math Behind SVM
  • Naïve Bayes Model
  • SVM Classification Tasks
  • Clustering Techniques
  • Introduction to K-Means: Algorithm and Math Foundations
  • Dimensionality Reduction Revisited with PCA
  • K-Means Clustering
  • PCA on Real-World Data
  • What are Association Rules?
  • Market Basket Analysis
  • Apriori Algorithm
  • Building Recommendation Engines: Collaborative Filtering, Content-Based Filtering
  • Apriori Implementation
  • Market Basket Analysis
  • Simple Recommender System
  • Saving Models for Deployment
  • Flask Server Basics
  • Accessing Models via Postman
  • Creating a User Interface with HTML and JavaScript
  • Deploying Predictive Models for Real-Time Use
  • Building and Deploying Your First ML Web App
  • Deep Learning vs Traditional Machine Learning
  • AI, Neural Networks, and Their Impact
  • Applications and Challenges of Deep Learning
  • Introduction to Multi-Layered Architectures
  • Regularization Techniques
  • Activation Functions: ReLU, Softmax, Sigmoid, Tanh
  • Forward and Backward Propagation
  • Hyperparameter Tuning and Optimization
  • Perceptron Learning Rule
  • Training Methods and Stochastic Processes
  • Vanishing Gradient Problem
  • Transfer Learning and Dropout
  • L1 (Lasso) and L2 (Ridge) Regularization
  • Introduction to Keras API
  • Sequential vs Functional API
  • Composing and Training Complex Models
  • Using TensorBoard for Visualization
  • Customizing Training Processes
  • Build Deep Learning Models with TensorFlow and Keras
  • Understanding Convolutional Neural Networks (CNNs)
  • Pooling Layers, Feature Maps
  • Visualizing CNN Internals
  • Fine-Tuning and Transfer Learning
  • Introduction to Recurrent Neural Networks (RNNs)
  • Deploying CNNs Using TensorFlow
  • Working with Images and Videos
  • Image Feature Extraction and Engineering
  • Creating Image Feature Matrices for Deep Networks
  • Image Data Preprocessing
  • Building Image-Based Models
  • What is Generative AI?
  • Applications Across Industries
  • LLM Models (Large Language Models)
  • ChatGPT API Overview
  • Basics of Prompt Engineering
  • Generate Text with RNN-LSTM Architectures
  • End-to-End Project Demonstrations
  • Best Practices for Model Deployment
  • Tips for Portfolio Building
  • Course Wrap-Up and Next Steps

 

Projects

  • Skills Covered: Data cleaning, exploratory data analysis, feature engineering, pattern recognition.
  • Objective: Analyze applicant data to uncover patterns that help determine loan eligibility and reduce default risk for a finance company.
  • Tech Stack: Python (Pandas, Matplotlib, Seaborn), SQL, Power BI/Tableau.
  • Skills Covered: SQL queries, business intelligence dashboards, trend and outlier detection.
  • Objective: Analyze sales performance across regions and departments to derive actionable insights.
  • Tech Stack: SQL, Python (Pandas, Matplotlib), Tableau/Power BI.
  • Skills Covered: Data manipulation, time series visualization, real-time KPIs.
  • Objective: Track stock levels, sales, and restocking timelines to optimize inventory.
  • Tech Stack: Python (Plotly, Dash), Excel/CSV, Power BI.
  • Skills Covered: Linear regression, data preprocessing, model tuning.
  • Objective: Predict property prices based on location, square footage, and amenities.
  • Tech Stack: Python (Scikit-learn, Pandas, NumPy), Linear & Multiple Regression.
  • Skills Covered: Classification models, confusion matrix, ROC-AUC.
  • Objective: Identify customers likely to leave a telecom/subscription service.
  • Tech Stack: Python (Scikit-learn), Logistic Regression, Random Forest.
  • Skills Covered: Collaborative filtering, association rule mining.
  • Objective: Suggest products based on browsing/purchase history.
  • Tech Stack: Python (Surprise Library, Scikit-learn), Apriori Algorithm.
  • Skills Covered: Artificial neural networks, activation functions, dropout layers.
  • Objective: Recognize digits (0–9) from handwritten images.
  • Tech Stack: Python (TensorFlow, Keras), ANN, Softmax, ReLU.
  • Skills Covered: Image classification, convolutional layers, transfer learning.
  • Objective: Detect diseases (e.g., tumors) from brain MRI images.
  • Tech Stack: Python (TensorFlow, Keras, OpenCV), CNN, Flask.
  • Skills Covered: Prompt engineering, LLMs, ChatGPT API.
  • Objective: Build a conversational assistant for domain-specific queries.
  • Tech Stack: Python (OpenAI API, LangChain), Flask, HTML/JS.
  • Skills Covered: EDA, classification, clustering, forecasting, deployment.
  • Objective: Analyze sales, predict demand, and optimize retail performance.
  • Tech Stack: Python (Scikit-learn, XGBoost), SQL, Power BI, Flask.

 

Certification

Career Support

We have a dedicated team which is taking care of our learners learning objectives.

FAQ

There is no such prerequisite if you are enrolling for Master’s Course as everything will start from scratch. Whether you are a working IT professional or a fresher you will find a course well planned and designed to incorporate trainee from various professional backgrounds.
AI Council offers 24/7 query resolution, you can raise a ticket with a dedicated support team and expect a revert within 24 Hrs. Email support can resolve all your query but if still it wasn’t resolved then we can schedule one-on-one session with our instructor or dedicated team. You can even contact our support after completing the training as well. There are no limits on number of tickets raised.
AI council provide two different modes for training one can choose for instructor lead training or learning with prerecorded video on demand. We also offer faculty development programs for college and schools. apart from this corporate training for organization/companies to enhance and update technical skills of the employees. We have highly qualified trainers who are working in the training industry from a very long time and have delivered the sessions and training for top colleges/schools and companies.
We are providing a 24/7 assistance for the ease of the student. Any query can be raised through the interface itself as well as can be communicated through email also. If someone is facing difficulties with above methods mentioned above we can arrange a one on one session with the trainer to help you with difficulties faced in learning. You can raise the query throughout the total training period as well as after the completion of the training.
AI Council offers you the latest, appropriate and most importantly the real-world projects throughout your training period. This makes student to gain industry level experience and converting the learning’s into solution to create the projects. Each Training Module is having Task or projects designed for the students so that you can evaluate your learning’s. You will be working on projects related to different industries such as marketing, e-commerce, automation, sales etc.
Yes, we do provide the job assistance so that a learner can apply for a job directly after the completion of the training. We have tied-ups with companies so when required we refers our students to those companies for interviews. Our team will help you to build a good resume and will trained you for your job interview.
After the successful completion of the training program and the submission of assignments/quiz, projects you have to secure at least B grade in qualifying exam, AI Council certified certificate will be awarded to you. Every certificate will be having a unique number through which same can be verified on our site.
To be very professional and transparent No, we don’t guarantee the job. the job assistance will help to provide you an opportunity to grab a dream job. The selection totally depends upon the performance of the candidate in the interview and the demand of the recruiter.
Our most of the programs are having both the modes of training i.e. instructor led and self-paced. One can choose any of the modes depending upon their work schedule. We provide flexibility to choose the type of training modes. While registering for courses you will be asked to submit your preference to select any of the modes. If any of the course is not offered in both modes so you can check in which mode, the training is going on and then you can register for the same. In any case if you feel you need any other training mode you can contact our team.
Yes, definitely you can opt for multiple courses at a time. We provide flexible timings. If you are having a desire for learning different topics while continuing with your daily hectic schedule our course timing and modes will help you a lot to carry on the learning’s.
Whenever you are enrolling in any of the courses we will send the notification you on your contact details. You will be provided with unique registration id and after successful enrollment all of the courses will be added to your account profile on our website.AI Council provides lifetime access to course content whenever needed.
A Capstone project is an outcome of the culminating learning throughout the academic years. It is the final project that represents your knowledge, efforts in the field of educational learning. It can be chosen by the mentor or by the students to come with a solution.
Yes, for obtaining the certificate of diploma programmer you have to submit the capstone project.