Master Data Science, Analytics & AI: From Code to Cloud Deployment

  • Course Duration 120 Hours (6 Months)
  • Course Mode Instructor Led Online Training
  • Date & Time August 22, 2025 || August 26, 2025

About The Course

 This isn't just another course; it's your direct path to becoming an in-demand Data Scientist and ML Developer. We go beyond theory, immersing you in real-world Python programming, data analysis, machine learning, deep learning, and cutting-edge generative AI. You'll get hands-on with tools like Pandas, Matplotlib, Seaborn, Power BI, Flask, and master the art of prompt engineering.

We're proudly partnered with the NVIDIA Inception Program, Microsoft Solutions Partner, and AWS Partner Network, ensuring you learn with industry-leading tools and gain unparalleled credibility. This program culminates in an impressive project portfolio that showcases your ability to solve complex data challenges from start to finish. Complete your journey and earn globally recognized certifications (Microsoft and AWS-backed), alongside lifetime job assistance and 3 years of dedicated tech support. If you're ready to build, deploy, and innovate with AI, this program is your launchpad.

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.

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

Course Agenda

  • Basic Overview: Variables, Data Types, Conditional Statements, Loops
  • Python Libraries for Data Science: Pandas, NumPy, Matplotlib, Seaborn
  • Data Manipulation & Feature Engineering Essentials: Data Manipulation, Feature Scaling and Transformation, Visualization, Statistics
  • Data Manipulation
  • Feature Scaling and Transformation
  • Visualization
  • Basic Statistical Analysis
  • Introduction to EDA: 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
  • Core EDA Techniques: Descriptive Statistics, Data Visualization, Handling Missing Data, Identifying Outliers, Correlation Analysis
  • Data Extraction, Wrangling, and Visualization: Data Acquisition Techniques, Raw vs Processed Data, Data Cleaning and Transformation
  • Applying EDA Techniques
  • Interpreting Results
  • Introduction to Machine Learning: What is Machine Learning?, Supervised vs Unsupervised Learning, Real-World Applications
  • Supervised Learning Core Concepts: Supervised Learning, K-Fold Cross-Validation, Confusion Matrix, ROC Curve
  • Regression Algorithms: Linear Regression, Polynomial Regression, Assumptions Behind Regression Models, Evaluation Metrics: R², MSE, Train-Test Splits
  • Classification Algorithms: Logistic Regression, Decision Trees, k-Nearest Neighbors (k-NN), What is Classification?, Linear vs Logistic Regression, Math Behind Logistic Regression: Logit Function, Odds, Likelihood, Evaluating Model Thresholds
  • Implementing and Evaluating Regression Models
  • Implementing and Evaluating Classification Models
  • Introduction to Probabilistic Classifiers: Introduction to Probabilistic Models, Understanding Naïve Bayes, Math behind Bayes Theorem
  • Support Vector Machines (SVM): Introduction to SVM, Concepts and Advantages, Linear and Nonlinear SVM Classifiers, Kernel Functions, Classification and Regression, Math Behind SVM
  • Implementing Naïve Bayes Classifiers
  • Implementing SVM Classifiers
  • Introduction to Ensemble Methods: Introduction to Ensemble Methods, Concepts and Benefits
  • Types of Ensemble Methods: Bagging, Boosting, Stacking, Random Forests Deep Dive
  • Implementing Ensemble Algorithms
  • Introduction to Unsupervised Learning: Introduction to Unsupervised Learning, Concepts and Applications
  • Clustering Techniques: K-means Clustering, Hierarchical Clustering, Clustering Techniques, Introduction to K-Means: Algorithm and Math Foundations
  • Dimensionality Reduction: Dimensionality Reduction, PCA (Principal Component Analysis), Factor Analysis, Feature Scaling and Normalization, Why Dimensions Matter, Curse of Dimensionality Explained
  • Implementing Clustering and Dimensionality Reduction
  • Introduction to Time Series: What is Time Series Data?, Components of Time Series, Applications of Time Series
  • Time Series Preprocessing and Visualization: Handling Date and Time Data, Resampling and Aggregation, Time Series Visualization
  • Traditional Time Series Models: Stationarity, Autocorrelation and Partial Autocorrelation Functions (ACF/PACF), AR (Autoregressive) Models, MA (Moving Average) Models, ARMA (Autoregressive Moving Average) Models, ARIMA (Autoregressive Integrated Moving Average) Models, SARIMA (Seasonal ARIMA) Models
  • Advanced Time Series Concepts (Brief Overview): Prophet (Facebook's Forecasting Tool), State Space Models (Brief)
  • Time Series Data Preparation and Visualization
  • Implementing and Evaluating ARIMA/SARIMA Models
  • Deep Learning vs Traditional Machine Learning: Deep Learning vs Traditional Machine Learning, AI, Neural Networks, and Their Impact, Applications and Challenges of Deep Learning
  • Building Multi-Layered Neural Networks: 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
  • Keras and TensorFlow for Deep Learning: 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
  • CNNs and Computer Vision: Understanding Convolutional Neural Networks (CNNs), Pooling Layers, Feature Maps, Visualizing CNN Internals, Fine-Tuning and Transfer Learning
  • Computer Vision and OpenCV: Working with Images and Videos, Image Feature Extraction and Engineering, Creating Image Feature Matrices for Deep Networks
  • Deploying CNNs Using TensorFlow
  • Image Data Preprocessing
  • Building Image-Based Models
  • Introduction to RNNs: Recurrent Neural Networks (RNNs), Vanishing/Exploding Gradients in RNNs, Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU)
  • Language Modeling: What is Language Modeling?, Sequence-to-Sequence Models, Word Embeddings, Text Preprocessing for NLP
  • Applications of RNNs: Text Generation, Machine Translation, Sentiment Analysis, Named Entity Recognition (NER)
  • Building and Training RNN/LSTM Models
  • Text Generation
  • Generative AI: Introduction, Types of Generative AI, Generative Adversarial Networks (GAN), Transformers, Hugging Face Transformers, BERT, GPT-3, T5, Large Language Models (LLM), 10+ AI Tools Covered
  • Prompt Engineering: Introduction, Prompt Design, Zero-shot, One-shot, Few-shot prompts, Chain of Thought (CoT) Prompting, Role-Specific Prompting, Tree of Thoughts (ToT) Prompting, Application-Specific Prompts, Prompt Optimization, APIs for Custom Integration, Response Evaluation, Iterative Testing
  • Fine-tuning and Experimenting with LLMs
  • Prompt Optimization
  • Introduction to Flask for Web App Development
  • Building RESTful APIs with Flask
  • Deployment of Machine Learning Models as REST APIs
  • Developing and Deploying ML Models
  • Introduction to Cloud for ML: Why Cloud for ML Deployment?, Overview of Major Cloud Providers, Infrastructure as Code (IaC) (Brief)
  • Key Cloud Services for ML Deployment: Virtual Machines (VMs) / Compute Instances, Containerization with Docker, Orchestration with Kubernetes (Brief), Serverless Functions, Managed ML Services, API Gateways, Load Balancers, Monitoring and Logging
  • Cloud Storage for ML Artifacts: Object Storage, Database Services
  • Deploying a Flask ML API on a Cloud VM
  • Containerizing an ML Model with Docker
  • MS Excel: Statistical Functions, Logical Functions, Mathematical Functions, Lookup Functions, Worksheets, Formatting, Formulas, Sorting, Filtering, Date and Time, Charts, Dashboards, What-if Analysis, Printing, Keyboard Shortcuts
  • MySQL: Introduction to MySQL, SQL Commands, Data Types, Constraints, Operators, Clause, SQL Statement Fundamentals, Group By Statements, Window Functions, Aggregate Functions, Joins, CTE Table, Sub-Query, Index, Advanced SQL Commands, Creating Databases and Tables, Conditional Expressions and Procedures
  • Excel Data Analysis
  • SQL Querying
  • Introduction to Power BI: Introduction to Power BI, Data Preparation, Modelling and Visualization, Power BI Dashboard and Data Transformations
  • Advanced Power BI Concepts: M Query and Hierarchies, DAX Essentials, Slicers, Filters, Drill Down Reports, Power BI Query, Q & A, and Data Insights, Power BI Settings, Administration, and Direct Connectivity, Embedded Power BI API and Power BI Mobile, Power BI Advanced and Power BI Premium
  • Creating Power BI Dashboards
  • Performing Data Transformations
  • Using DAX for Advanced Data Analysis
  • Connecting Power BI to Various Data Sources
  • Project Planning: Project Planning, Data Acquisition
  • Model Building & Deployment: Model Building, Development, Training, Evaluation, Presentation, Demonstration
  • End-to-End Data Science Project
  • Guest Lectures by renowned and eminent AI experts
  • Real-World Case Studies and Applications
  • Troubleshooting issues
  • Problem Solving
  • Interactive Sessions
  • Interactive Problem-Solving Sessions
  • 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, data manipulation, business intelligence dashboards, trend and outlier detection.
  • Objective: Perform comprehensive analysis of Walmart’s sales data, identifying trends and sales drivers 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 Key Performance Indicators (KPIs).
  • Objective: Track stock levels, sales, and restocking timelines to optimize inventory efficiency and minimize stockouts.
  • Tech Stack: Python (Plotly, Dash), Excel/CSV, Power BI.
  • Skills Covered: Regression modeling (Linear & Multiple Regression), data preprocessing, feature selection, model tuning, data visualization.
  • Objective: Develop a prediction engine to determine house selling prices based on location, size, market trends, and amenities.
  • Tech Stack: Python (Scikit-learn, NumPy, Pandas).
  • Skills Covered: Classification models (Logistic Regression, Decision Trees, Random Forest), predictive analytics, confusion matrix, ROC-AUC analysis, business strategy.
  • Objective: Build a model to predict customer churn for a subscription-based or telecom service, helping businesses improve retention strategies.
  • Tech Stack: Python (Scikit-learn).
  • Skills Covered: Recommendation systems basics, collaborative filtering, association rule mining, user behavior analytics.
  • Objective: Create a system that suggests products based on purchase history and Browse behavior, enhancing user experience.
  • Tech Stack: Python (Surprise Library, Scikit-learn), Apriori Algorithm.
  • Skills Covered: Artificial Neural Networks (ANN), activation functions (Softmax, ReLU), dropout layers, image classification.
  • Objective: Develop and train a neural network to accurately recognize digits (0–9) from handwritten images using the MNIST dataset.
  • Tech Stack: Python (TensorFlow, Keras).
  • Skills Covered: Image classification with Convolutional Neural Networks (CNN), convolutional layers, pooling layers, transfer learning.
  • Objective: Build a CNN model to detect diseases (e.g., tumors or anomalies) from brain MRI images, showcasing real-world medical imaging applications.
  • Tech Stack: Python (TensorFlow, Keras, OpenCV).
  • Skills Covered: Prompt engineering, Large Language Models (LLMs), ChatGPT API, natural language processing (NLP), conversational AI.
  • Objective: Develop a conversational assistant capable of engaging in meaningful dialogues and providing accurate responses based on given information.
  • Tech Stack: Python (OpenAI API, LangChain), Flask (for deployment).
  • Skills Covered: End-to-end data science project lifecycle, EDA, classification, clustering, time series forecasting, optimization strategies, model deployment, AI-driven insights.
  • Objective: Create a comprehensive solution for retail store performance improvement by analyzing sales, inventory, and customer behavior, predicting demand, and optimizing operations.
  • Tech Stack: Python (Scikit-learn, XGBoost, ARIMA/Prophet), SQL, Power BI/Tableau, Flask (for deployment).
  • Skills Covered: Natural Language Processing (NLP), text embedding, Recurrent Neural Networks (RNNs) or Transformers, collaborative filtering, user profiling, model deployment.
  • Objective: Build an advanced recommendation system that provides personalized news articles to users based on their reading history, preferences, and the content of the articles.
  • Tech Stack: Python (TensorFlow, Keras, Hugging Face Transformers, NLTK/SpaCy), Flask (for deployment), SQL/NoSQL (for user data/article storage).

 

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.