The Certificate Program in Machine Learning & Deep Learning is an advanced, industry-focused course designed to help learners master AI-powered predictive modeling and intelligent automation using cutting-edge machine learning algorithms and deep learning architectures.
This program integrates the core principles of Machine Learning, Deep Learning, and Artificial Intelligence to build real-world expertise in developing intelligent systems and automating data-driven decision-making.
From understanding supervised and unsupervised learning techniques to designing and training neural networks, you’ll gain hands-on experience with tools and frameworks like Python, TensorFlow, Keras, PyTorch, Scikit-learn, OpenCV, and cloud-based AI platforms. The course follows a project-based approach, enabling you to implement models for tasks such as image classification, natural language processing, and predictive analytics.
By the end of this Machine Learning & Deep Learning certification, you’ll be capable of building, training, and deploying AI models that can learn from data, recognize complex patterns, and make intelligent predictions to drive innovation across industries.
- Must hold a graduate degree in any discipline.
- Basic computer literacy and basic proficiency in English are required.
- No prior experience in data analytics or programming is necessary.
- A laptop or desktop with a stable internet connection.
- Enthusiasm to learn data analytics and AI applications through guided projects.
- Familiarity with Excel or basic statistics is an added advantage(Not Mandatory).
- Commitment to complete all live sessions, assignments, and real-time projects.
- Students and Fresh Graduates aspiring to enter the data analytics or business intelligence domain.
- Working professionals seeking to upgrade their skills for career advancement in data domain.
- Entrepreneurs and decision-makers looking to use AI-powered insights to drive business growth.
Whether you’re a beginner or looking for career transition or domain change, this course ensures you gain both technical expertise and strategic understanding to thrive in the field of data analytics and AI.
What You’ll Get?
Attend interactive live classes led by industry experts and never miss a session with access to high-quality recordings. Learn at your own pace while revisiting key concepts anytime, ensuring a complete and flexible learning experience.
Work on real-world projects under the mentorship of experienced professionals. Learn to implement Agile methodology, collaborate in sprints, and apply practical solutions — building skills that employers value and boosting your confidence to handle real industry challenges.
Our dedicated placement support ensures you never stop until you secure your dream job. From interview prep to company shortlisting, we guide you every step of the way, giving you the tools and confidence to succeed in the competitive job market.
Craft a professional, standout resume with personalized guidance from experts. Learn how to highlight your skills, projects, and achievements effectively — increasing your chances of catching recruiters’ attention without much stress and landing interviews.
Practice with mock interviews designed to simulate real industry scenarios. Receive constructive feedback and gain confidence, then get opportunities to attend live interviews with top companies — ensuring you’re fully prepared to excel.
Get unlimited access to all course materials, including notes, recordings, and resources. Revisit lessons anytime, stay updated with new content, and continue learning even after completing your course.
Join a vibrant community of learners, mentors, and industry experts. Collaborate, share knowledge, seek guidance, and grow together — because learning is better when it’s supported by a strong, like-minded network.
Absolutely! This Machine Learning & Deep Learning course for beginners starts from the fundamentals — Python programming, data preprocessing, and linear regression — before advancing to complex neural network architectures. Even if you come from a non-technical background, our step-by-step approach, real-world projects, and expert mentorship will help you gain confidence in building and deploying AI models.
No prior coding or advanced math knowledge is required! We teach core concepts like linear algebra, statistics, and Python programming from scratch. You’ll learn through practical examples using frameworks such as TensorFlow, Keras, and PyTorch. The goal is to help you understand how algorithms work and apply them effectively — not just memorize formulas.
Yes! Once you complete the course, our career acceleration team helps you with resume building, mock interviews, and job referrals. We actively connect our learners with hiring partners in AI, Data Science, and ML roles. Many of our graduates have landed positions as Machine Learning Engineers, AI Analysts, and Data Scientists in top companies.
You’ll work on hands-on ML & DL projects including image classification, sentiment analysis, object detection, and predictive analytics. Each project is designed to simulate real-world problems from domains like healthcare, finance, and retail. By the end of the program, you’ll have a portfolio of AI projects to showcase during interviews and in your GitHub profile.
Yes! Once you enroll, you’ll receive lifetime access to all study materials, recorded sessions, and future course updates. As the field of AI evolves, new modules on Generative AI, Transformers, and Large Language Models will be added — so you can continue upgrading your skills without additional cost.
Master the future of Artificial Intelligence with our comprehensive Machine Learning and Deep Learning course. This program is designed to help you build intelligent models using Python, TensorFlow, and PyTorch, from foundational algorithms to advanced neural networks and computer vision applications. Whether you’re a beginner or an experienced professional, you’ll learn to train, evaluate, and deploy real-world AI models across industries like healthcare, finance, and automation. Gain hands-on experience through projects in predictive analytics, image recognition, and natural language processing (NLP), and prepare to become a certified Machine Learning Engineer or AI Specialist ready for global opportunities.
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Module 1: Python
Dive into the world of Python, the most versatile and beginner-friendly programming language. Perfect for beginners and non-tech professionals aiming to build a strong foundation.
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Lecture 1.1: Introduction to Python and Its Advantages
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Lecture 1.2: Introduction To IDEs
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Lecture 1.3: Why Python ?
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Lecture 1.4: Variable And Its Rules
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Lecture 1.5: Operators
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Lecture 1.6: Data Types
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Lecture 1.7: Conditional Statements and Loops
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Lecture 1.8: Functions And Parameters
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Lecture 1.9: Exception Handling and File Handling
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Lecture 1.10: Regular Expressions
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Lecture 1.11: Numpy
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Lecture 1.12: Pandas
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Lecture 1.13: Matplotlib
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Lecture 1.14: Seaborn
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Module 2: Statistics
Master the fundamentals of statistics to analyze and interpret data effectively. Learn essential concepts and techniques that form the backbone of data-driven decision-making.
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Lecture 2.1: Introduction To Statistics
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Lecture 2.2: Data Collection, Data Preparation and Inference
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Lecture 2.3: Sampling, Types Of Sampling and Variance in sample
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Lecture 2.4: Data Types
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Lecture 2.5: Descriptive Statistics
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Lecture 2.6: Data Distributions
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Lecture 2.7: Probability Theory
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Lecture 2.8: Central Limit Theorem
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Lecture 2.9: Confidence Interval
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Lecture 2.10: Hypothesis Testing
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Lecture 2.11: Correlation and Covariance
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Lecture 2.12: Data Preparation
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Module 3: Stuctured Query Language(SQL)
Master the essentials of SQL to efficiently manage and query databases. Learn to extract, analyze, and manipulate data to make informed, data-driven decisions.
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Lecture 3.1: Introduction to Database
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Lecture 3.2: Understanding The Architecture of the Database
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Lecture 3.3: Data Types In SQL
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Lecture 3.4: Command Types in SQL
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Lecture 3.5: Operators in SQL
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Lecture 3.6: Constraints in SQL
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Lecture 3.7: Joins
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Lecture 3.8: Sub Queries
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Lecture 3.9: Index, Views and Union
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Lecture 3.10: Common Table Expression
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Lecture 3.11: Window Functions
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Lecture 3.12: Triggers
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Module 4: Power BI
Master Power BI to transform raw data into interactive dashboards and insightful visualizations. Gain hands-on experience building real-world reports that drive smart business decisions.
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Lecture 4.1: Prepare The Data
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Lecture 4.2: Get Or Connect To Data
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Lecture 4.3: Profile and Clean The Data
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Lecture 4.4: Transform and Load The Data
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Lecture 4.5: Model The Data
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Lecture 4.6: Design And Implement The Data Model
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Lecture 4.7: Create Model Calculation Using Data Model
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Lecture 4.8: Optimize Model Performance
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Lecture 4.9: Visualize And Analyze The Data
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Lecture 4.10: Enhance Reports For Usability And Storytelling
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Lecture 4.11: Identify Patterns And Trends
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Lecture 4.12: Manage And Secure Power BI
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Lecture 4.13: Create And Manage Workspaces and Assets
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Lecture 4.14: Secure and Govern Power BI Items
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Module 5: Machine Learning And Deep Learning
Learn the mathematical foundations, supervised and unsupervised algorithms, natural language processing, time series forecasting, and deep learning architectures — from basics to advanced neural networks — through hands-on projects and real-world case studies.
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Lecture 5.1:Mathematical Foundations
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Lecture 5.2: Hierarchical Clustering
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Lecture 5.3:k-Means Clustering
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Lecture 5.4:Dimension Reduction Technique - PCA, SVD
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Lecture 5.5:Association Rules
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Lecture 5.6:Recommendation Engine
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Lecture 5.7:Network Analytics
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Lecture 5.8:Linear Regression
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Lecture 5.9:Logistic Regression
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Lecture 5.10:Lasso And Ridge Regression
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Lecture 5.11:K-NN Classification
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Lecture 5.12:Naïve Bayes
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Lecture 5.13:Ensemble Techniques
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Lecture 5.14:Support Vector Machine
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Lecture 5.15:Basics Of NLP
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Lecture 5.16:Text Classification
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Lecture 5.17:Sentiment Analysis
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Lecture 5.18:Named Entity Recognition
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Lecture 5.19:Text Summarization - Basics
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Lecture 5.20:Topic Modeling
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Lecture 5.21:Survival analytics
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Lecture 5.22:Time Series Analysis - Forecasting
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Lecture 5.23:Basics Of Nueral Network
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Lecture 5.24:Artificial Nueral Network
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Lecture 5.25:Convolutional Nueral Network
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Lecture 5.26:Recurrent Nueral Network
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Lecture 5.27:Hyper Parameter Tuning in Neural Network
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Module 6: Advanced Excel
Unlock the full potential of Excel with advanced formulas, pivot tables, and automation techniques.
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Lecture 6.1: Introduction to Excel & Interface Overview
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Lecture 6.2: Data Entry, Formatting, and Validation
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Lecture 6.3: Formulas & Basic Functions (SUM, AVERAGE, COUNT, etc.)
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Lecture 6.4: Advanced Functions (VLOOKUP, HLOOKUP, INDEX, MATCH)
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Lecture 6.5: Logical Functions (IF, AND, OR, Nested IF)
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Lecture 6.6: Text Functions (LEFT, RIGHT, MID, CONCATENATE, TEXT)
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Lecture 6.7: Date & Time Functions (TODAY, NOW, DATE, YEAR, MONTH)
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Lecture 6.8: Data Sorting, Filtering & Custom Views
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Lecture 6.9: Conditional Formatting & Highlighting Data
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Lecture 6.10: Data Validation & Drop-Down Lists
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Lecture 6.11: Pivot Tables & Pivot Charts
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Lecture 6.12: Charts & Visualizations (Column, Line, Pie, Combo)
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Lecture 6.13: Advanced Data Analysis with Power Query
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Lecture 6.14: Advanced Data Analysis with Power Pivot & DAX
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Lecture 6.15: Scenario Analysis, Goal Seek, and What-If Analysis
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Lecture 6.16: Macros & Introduction to VBA for Automation
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Lecture 6.17: Best Practices in Excel for Data Cleaning and Reporting
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Module 7:PySpark
Learn to process massive datasets efficiently using PySpark and unlock the power of distributed computing
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Lecture 7.1: Introduction to PySpark & Spark Ecosystem Overview
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Lecture 7.2: RDDs (Resilient Distributed Datasets) Basics and Operations
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Lecture 7.3: DataFrames & Spark SQL Fundamentals
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Lecture 7.4: Transformations & Actions in PySpark
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Lecture 7.5: Handling Structured & Semi-Structured Data (CSV, JSON, Parquet)
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Lecture 7.6: PySpark Functions (UDFs, Built-in Functions, Aggregations)
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Lecture 7.7: Data Cleaning & Preprocessing with PySpark
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Lecture 7.8: Joins, Unions, and Merging DataFrames
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Lecture 7.9: GroupBy, Aggregations, and Window Functions
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Lecture 7.10: Working with Spark Streaming & Real-Time Data
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Lecture 7.11: Machine Learning with PySpark MLlib
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Lecture 7.12: Performance Tuning & Optimization (Caching, Partitioning)
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Lecture 7.13: PySpark GraphFrames for Graph Analytics
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Lecture 7.14: Best Practices for Big Data Processing in PySpark
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Module 8:Machine Laerning in Cloud
Explore cloud-based analytics platforms to analyze and visualize data from anywhere, anytime. Learn to leverage scalable cloud solutions for faster and smarter business decisions.
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Lecture 8.1:Introduction to Cloud Computing and ML-as-a-Service
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Lecture 8.2:Overview of AWS, Azure, and GCP for Machine Learning
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Lecture 8.3:Cloud Storage and Data Management for ML (S3, GCS, Blob Storage)
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Lecture 8.4:Cloud Databases and Warehouses for ML (RDS, BigQuery, Redshift, Synapse)
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Lecture 8.5:Cloud-Based Machine Learning Platforms (SageMaker, Vertex AI, Azure ML Studio)
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Lecture 8.6:Building, Training, and Deploying ML Models in the Cloud
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Lecture 8.7:AutoML Services in Cloud (AWS AutoPilot, Vertex AutoML, Azure AutoML)
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Lecture 8.8:Serverless Machine Learning (AWS Lambda, Cloud Functions)
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Lecture 8.9:MLOps in Cloud (CI/CD, Model Monitoring, Retraining Pipelines)
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Lecture 8.10:Distributed Training and GPU/TPU Integration
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Lecture 8.11:Model Versioning and Experiment Tracking in Cloud ML Workflows
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Lecture 8.12:Real-time ML Inference and API Deployment
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Lecture 8.13:Best Practices and Case Studies in Cloud-based Machine Learning
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Module 9: Prompt Engieering
Apply your learning in a real-world project to solve business challenges using analytics and AI tools. Showcase your skills to potential employers with a fully functional project.
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Lecture 9.1: Introduction to Prompt Engineering and its Role in Data Analytics
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Lecture 9.2: Understanding Large Language Models (LLMs) like GPT and Gemini
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Lecture 9.3: Types of Prompts – Zero-Shot, One-Shot, and Few-Shot Learning
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Lecture 9.4: Crafting Effective Prompts for Data Exploration and Insights
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Lecture 9.5: Techniques to Improve Model Responses: Role, Context, and Constraints
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Lecture 9.6: Chain-of-Thought and Step-by-Step Reasoning Prompts
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Lecture 9.7: Prompt Templates for Data Cleaning, Summarization, and Insights Extraction
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Lecture 9.8: Prompt Engineering for SQL Query Generation and Automation
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Lecture 9.9: Evaluating and Refining Prompts for Accuracy and Relevance
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Lecture 9.10: Integrating Prompt Engineering with Big Data Tools and Workflows
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Lecture 9.11: Ethical Considerations and Bias Awareness in Prompt Design
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Lecture 9.12: Hands-on Practice: Building Custom Prompts for Analytical Tasks
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Module 10: Tableau
Master the art of visual storytelling with Tableau and turn complex data into insightful, interactive dashboards.
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Lecture 10.1: Introduction to Tableau
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Lecture 10.2: Tableau Workflow
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Lecture 10.3: Connection and Data Source Setup
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Lecture 10.4: Understand Changes to Data
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Lecture 10.5: Data Simplification and Sorting
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Lecture 10.6: Data Organization
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Lecture 10.7: Specific Values
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Lecture 10.8: Generate Grand Totals, Subtotals, and Changing Aggregation
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Lecture 10.9: Build Highlight Tables
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Lecture 10.10: Data by Date
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Lecture 10.11: Multiple Measures in a View
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Lecture 10.12: Measure Values and Names
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Lecture 10.13: Relationships Between Numerical Values
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Lecture 10.14: Geographic Data Mapping
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Lecture 10.15: Data Customization
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Lecture 10.16: Quick Table Calculations
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Lecture 10.17: View Sharing
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Lecture :Value Added Sessions
At our institute, we believe true learning goes beyond the classroom. Our Value Added Sessions are designed to help students understand how real-world systems and technologies work together. These sessions run parallel to the main course, bridging the gap between theory and industry application.
Through interactive workshops, live demos, and hands-on exercises, learners gain practical exposure to emerging tools, frameworks, and workflows used by professionals. Whether it’s cloud integration, AI model deployment, or end-to-end project execution, these sessions ensure our students develop a complete and job-ready skill set.
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Module 1: Flask API, FAST API
Learn how to build and deploy lightweight, scalable web APIs using Flask. Understand API integration, request handling, and connecting machine learning models to real-world applications. Explore asynchronous programming, automatic documentation, and rapid model deployment techniques.
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Module 2:Docker Basics
Get hands-on with Docker to containerize applications for seamless deployment. Learn to build, manage, and run containers that make your projects portable and production-ready.
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Module 3:Kubernets Basics
Understand the fundamentals of Kubernetes for container orchestration. Learn to automate deployment, scaling, and management of applications in a cloud-native environment.
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Module 4:HuggingFace
Discover how to use Hugging Face for state-of-the-art NLP and GenAI models. Learn to fine-tune transformers and integrate pre-trained models into your projects.
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Module 5:Web Scrapping Tools
Explore Scrapy for large-scale, automated data extraction from websites. Learn advanced crawling, data pipelines, and handling dynamic content efficiently.
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Module 6:OpenAI API
Unlock the potential of Generative AI with the OpenAI API. Learn how to build intelligent applications using GPT models for chatbots, content creation, and automation.
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Module 7:MLOps Fundamentals
Gain foundational knowledge in DevOps to streamline development and operations. Understand CI/CD pipelines, version control, and automated deployment practices.
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Module 8:Advancements in Gen AI
Stay ahead with insights into the latest breakthroughs in Generative AI. Learn about multimodal models, AI agents, and real-world GenAI applications shaping the future.
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Module 9:Introduction To Quantum Computing
Explore how quantum computing intersects with machine learning. Understand quantum algorithms, qubits, and how they revolutionize computational speed and AI problem-solving.
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Module 10: Capstone Project
Apply your learning in a real-world project to solve business challenges using analytics and AI tools. Showcase your skills to potential employers with a fully functional project.
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Module 11: Live Project
Work on live datasets under expert guidance to gain industry-ready experience. Build confidence by implementing end-to-end solutions that mirror real business scenarios.
Hi! I’m Divya Sharma, a Data Scientist at Microsoft with expertise in building AI-driven solutions that solve real-world business challenges. I’m passionate about helping learners master the complete data science lifecycle — from data preprocessing to deploying machine learning models in production. My sessions emphasize hands-on learning, critical thinking, and industry-ready project experience that prepares you to thrive in the world of Artificial Intelligence.
The course content is extremely well-structured and practical. I went from having zero knowledge to confidently working with real-world datasets. The mentors are super supportive and explain every concept clearly!
I loved how interactive and hands-on the learning experience was! The live projects and case studies really helped me understand how data analytics works in the industry. Highly recommend it to anyone starting out in data analytics!
The trainers have deep industry knowledge and make even complex topics feel simple. The course gave me confidence to apply for data analyst roles and even build my own mini projects. Truly worth every minute!