DS 360: Complete Data Science Program

Build a solid grasp of Python

"From writing clean code to using advanced features while sharpening your problem-solving skills."

Get started with Python by exploring popular tools like VS Code, PyCharm, and Jupyter Notebook

"Write your first script with confidence."

Write your first Python program.

"Get a clear feel for how Python code is structured and runs."

Learn how to store information using Variables.

"Name them in a way that keeps your code Clean, Clear, and Easy To Understand."

Dive into working with text in Python

"Learn how to Format, Slice and Use Built-In Tools to manipulate strings with ease."

Learn to work with lists

Python’s go-to tool for storing and managing groups of items

"So you can easily Create, Update and Organize your data."

Learn how tuples differ from lists

"Why they’re perfect for storing data that shouldn’t change."

Explore how sets make it easy

"Work with unique values and perform tasks like finding overlaps, differences or combining data."

Learn how to use key-value pairs with dictionaries

Perfect for organizing structured data like user Info, Settings OR Configurations."

Learn how to make your code smarter by using "if", "elif" and "else" to respond to different situations and conditions."

Learn how to automate repetitive tasks using Loops

Understand the differences between "for" and "while"

Get hands-on with Python’s built-in functions to handle numbers

"Whether you're doing math, rounding values or working with different types of numeric data."

Learn how to write your own functions to keep your code Clean, Reusable and Easier to manage.

Discover how to use Python’s built-in modules

"Organize your own code into reusable packages to keep things neat and efficient."

Learn how to spot and fix common coding errors like typos, indentation problems, and mismatched data types so your Python code runs smoothly.

Build smarter, cleaner code by creating lists in a single line using simple logic.

Learn how to Read, Write, and Manage text and data files just like it's done in real-world projects.

Learn hands-on debugging techniques to confidently Find, Track, and Fix errors in your code.

Grasp the core concepts of Object-Oriented Programming by creating Reusable code with Classes and Objects.

Explore anonymous functions and functional programming techniques to Write cleaner, Faster code.

Learn how to use regular expressions to Find, Extract, and Clean up text for tasks like validation and parsing.

Learn how to Install, Update, and Manage Python libraries easily using PIP, the Python Package Installer.

Learn how to handle Excel files with Python to automate real-world Data tasks and Workflows.

Learn to write Scalable, Memory-friendly code using custom iterators and lazy-loading generators.

Learn how to enhance functions on the fly using Decorators.

One of Python’s most powerful features.

Learn how to Save and Reload Python objects using serialization.

Perfect for model Storage and Caching.

Learn to work with JSON data to connect with APIs, Exchange information, and Build dynamic applications.

Learn how to tackle problems by thinking step by step

"Breaking big challenges into Clear, Manageable actions you can actually follow through"

Discover how to measure how well your code runs using Big O notation

"Start writing algorithms that are faster and use less memory."

Dive into key algorithms like Binary Search and Euclid’s Algorithm to see how logical thinking solves real-world problems.

Get a clear understanding of essential Data Structures and how they help store, access, and manage data across different computing tasks.

Learn how Python manages memory under the hood

What affects your code’s performance?

How to write smarter, memory-efficient programs?

Learn how to:

• Keep your code simple and readable

• Follow naming conventions

• Write DRY (Don't Repeat Yourself) code

• Use comments and create helpful documentation

Get comfortable with Excel’s layout and features while learning how it helps organize and make sense of data in tables

Learn how to format cells, use conditional formatting, and organize worksheets to make your data clear, readable, and easy to work with.

Quickly explore and compare large data sets by using Excel’s built-in sorting and filtering tools.

Learn to summarize your data using key Excel functions like "SUM", "AVERAGE", "MIN", "MAX", and "COUNT"

Understand how cell referencing works in Excel
 

Learn to use the "$ symbol" to lock cells in dynamic formulas.

Use Data Validation, Drop-down Lists, and Input Messages to control and guide what gets entered into your Excel cells.

Bring your data to life with Bar Charts, Line Graphs, and Pie Charts

Perfect for presentations and quick takeaways

Use Pivot Tables to quickly break down large data sets and uncover patterns and trends with just a few clicks

Learn to handle errors in your formulas using "IFERROR" and "ISERROR", and fix common issues in your calculations with confidence.

Get your spreadsheets print-ready by setting up Headers, Footers, Scaling, and Layout for clean, professional results

Master nested formulas and learn how to combine functions like "IF", "AND", "OR", "IFS", and "CHOOSE" to build powerful logic in your spreadsheets

Learn to search and pull data efficiently using powerful lookup functions like "VLOOKUP", "HLOOKUP", "INDEX", and "MATCH"

Clean and shape raw data with functions like "LEFT", "RIGHT," "MID", "LEN", "TRIM", "TEXT", "CONCAT" and more to make it ready for analysis

Build interactive reports using Multi-level Summaries, Calculated Fields, Slicers and Timelines for deeper insights.

Make smarter decisions with Excel’s logical and statistical tools like "COUNTIFS", "SUMIFS", "AVERAGEIFS", "RANK", and "PERCENTILE"

Explore your data with tools like Data Tables, Goal Seek and Scenario Manager to run "what-if" analyses and make informed predictions

Create Named Ranges and use Dynamic Data Ranges to make your formulas and dashboards more flexible and easier to manage

Learn to record and edit simple macros to automate routine tasks and boost your productivity in Excel.

Bring your reports to life by Combining charts, Slicers, and Formulas to build dynamic Interactive Dashboards

Import data from CSV files, websites, or databases

Learn how to export it safely for sharing and storage

Get a clear understanding of databases.

What they are, How they work, and How to set up a relational database system.

Learn to use Entity-Relationship Diagrams to turn real-world data into structured Tables, Fields, and Connections.

Eliminate redundancy and improve data integrity through "1NF", "2NF", "3NF", and understand when to use denormalized formats like Star Schema.

Understand the key principles that keep database transactions Reliable, Consistent, and Secure.

Learn how to use data types like numbers, Text, Dates, and Booleans to store information accurately and efficiently.

Learn how to create and manage database structures using SQL's Data Definition Language (DDL) commands like CREATE, ALTER, DROP, and TRUNCATE.

Learn how to Add, Change, Remove, and Fetch data using SQL's Data Manipulation Language commands like SELECT, INSERT, UPDATE, and DELETE.

Control who can access or modify your data by setting user permissions with SQL commands like "GRANT" and "REVOKE".

Keep your database safe by using transaction controls like "COMMIT", "ROLLBACK", and "SAVEPOINT" to manage and undo changes when needed.

Apply rules to your database tables and columns—like "PRIMARY KEY", "FOREIGN KEY", "NOT NULL", "CHECK", "UNIQUE", and "DEFAULT".

To keep your data accurate, valid, and well-structured.

Use operators in your queries to Compare values, Perform calculations, and Filter data logically.

Write smarter SQL queries using key clauses like "WHERE", "GROUP BY", "ORDER BY", "HAVING", and more to Filter, Sort, and Organize your data effectively.

Understand how to combine data from multiple tables using various join types:

• Inner Join

• Left Join

• Right Join

• Full Outer Join

• Equi Join

• Non-Equi Join

• Self Join

Use built-in SQL functions like "SQRT", "PI", "SQUARE", "ROUND" and "CEILING" to perform math operations in your analytical queries with ease

Learn how to convert or cast data types in SQL to keep your queries accurate and compatible across different data sets

Handle missing data more effectively using functions like "COALESCE", "NVL" and "NULLIF" to improve the logic and reliability of your queries

Write flexible, condition-based queries using "IF" and "CASE" expressions to handle different scenarios in your data

Work with date and time fields using functions like "NOW()", "DATEDIFF()", "DATE_FORMAT()" and more to extract insights and manage time-based data

Do calculations, Apply rounding and Transform numbers right inside your SQL queries to get cleaner, more useful results

Clean up, Format and Analyze text data with functions like "CONCAT", "SUBSTRING", "LOWER", "UPPER", "REPLACE" and more to make your queries sharper and more effective.

Learn how to nest one query inside another to create more flexible, powerful, and dynamic data operations.

Spot trends and patterns in your data using advanced window functions like "RANK()", "ROW_NUMBER()", "DENSE_RANK()" and More

Learn to connect SQL with Python using libraries like "sqlite3", "SQLAlchemy" and "Pandas"

So you can run queries, manipulate data, and build dynamic scripts or apps with ease.

Get started with Pandas for data analysis.

Learn how to Install it and Use its core tools to work with structured data efficiently.

Learn to create and work with Pandas Series.

An essential data structure for handling labeled One-dimensional data.

Dive into Pandas DataFrames.

Two-dimensional labeled data structures.

And learn how to Load, Slice, Index, and Explore data with ease.

Master how to combine datasets using Merging, Joining, and Concatenation techniques to prepare data for deeper analysis.

Use the "groupby()" method in Pandas to split your data into categories and run operations like Sum, Count, Mean, and more on each group.

Turn your data into pivot tables to Uncover trends, Spot patterns, and Create clean, Multi-level summaries with ease.

Learn how to handle date and time data in Pandas.

Filter by Time, Format dates, and Create time series for smarter analysis.

Put your skills into action by Cleaning, Reshaping, and Transforming real-world datasets using everything you've learned.

Explore how probability helps you Understand uncertainty, Make better predictions, and Think more analytically.

Get to know the basics of probability by exploring sample Spaces, Events, and How to calculate simple probabilities with easy-to-follow examples.

Explore the core rules that lay the foundation for all Probability Calculations and Reasoning.

Learn how to use Conditional probability and Bayes’ Theorem to update your understanding as new information comes in and make sense of complex event relationships.

Understand how random variables capture uncertainty, and explore tools like Probability Mass Functions (PMF) and Cumulative Distribution Functions (CDF) to describe their behavior.

Study key distributions and their applications:

• Bernoulli – Single trial outcomes

• Binomial – Repeated success/failure scenarios

• Geometric – Probability of first success

Learn how expectation and variance help you understand the Average outcome and How much your data can vary.

Explore widely used continuous distributions:

• Uniform Distribution

• Exponential Distribution

• Normal Distribution (Gaussian curve basics)

Learn how to draw random samples from distributions and simulate real-world scenarios using data.

Use Python’s NumPy library to simulate probability-based scenarios and explore statistical patterns using Random number generation.

Understand the difference between a sample and a population

Why smart sampling matters in real-world data analysis?

Explore the Central Limit Theorem and "Why it’s key to inferential statistics?" especially when building confidence intervals

Learn how the "chi-square distribution works"
"What makes it unique?" 
"How it’s used to test variance and analyze categorical data?"

Learn how to estimate population values using both single points and confidence ranges for more accurate analysis

Discover how Maximum Likelihood Estimation (MLE) helps identify the most likely parameter values from real data

Learn to build confidence intervals for a population mean using the t-distribution (perfect for when the standard deviation isn’t known)

Put theory into practice by using point and interval estimation on real-world examples to deepen your understanding

Grasp the basics of Hypothesis Testing

Learn how null and alternative hypotheses work and what p-values really mean

See how "One-Tailed and Two-Tailed Hypotheses" affect the type of test you choose and how you interpret the results in real-world scenarios

Understand Type I and Type II errors, StatisticalPower and How to draw reliable conclusions from your hypothesis tests?

Learn how to unpack and organize nested JSON data so it’s ready for Visual analysis and Reporting.

Learn How to style and format tables to make your data easier to read and More visually appealing in reports and dashboards.

Use histograms to visualize How your data is spread out and how often values occur.

Making patterns easy to spot.

Use box plots to explore How your data is distributed, Spot outliers, and See key stats like medians and quartiles.

Review essential chart types and plotting basics to Solidify your understanding before moving into Advanced visualizations.

Use pie and donut charts to clearly show How individual parts contribute to the whole in a visually engaging way.

Create stacked and percentage bar charts to easily Compare grouped data across different categories.

Use stacked area plots to visualize How data builds up over time and Tell clear, Compelling time series stories.

Use scatter plots to explore the relationship between Two variables and Uncover patterns, Clusters, and Correlations.

Use bar charts to Compare categories and Easily spot differences between groups.

Visualize the interaction between Two continuous variables to uncover trends, density patterns, and Correlations.

Use line plots to track How things change over time.

Like visualizing real-world trends such as COVID-19 case counts.

Get familiar with Power BI’s interface, Explore its key features and Understand how it supports business intelligence workflows.

Learn how to load datasets, manage tables, apply filters, and shape your data for visual reporting.

Build trend lines, area charts, scatter plots, ribbon visuals, and decomposition trees to uncover insights.

Create multi-page reports using drag-and-drop visuals, slicers, and drill-through functionality.

Build dynamic dashboards and publish them to the Power BI service for sharing and live updates.

Get familiar with Tableau’s layout and workspace and see how it empowers visual-first data analysis

Connect to different Data sources, Combine datasets, and Get your data ready for analysis using Tableau.

Build charts, maps and storyboards in Tableau using easy drag-and-drop tools to explore and understand your data

Learn to use Tableau’s built-in features like Trend lines, Forecasting and Clustering to uncover simple predictive insights from your data.

Create responsive dashboards in Tableau and share them with others by publishing to Tableau Public or Tableau Server.

Learn what machine learning is? How it stands apart from traditional programming? and Why it’s a key tool in today’s data-driven world?

Explore the three main types of machine learning:

• Supervised Learning – Learning from labeled data

• Unsupervised Learning – Discovering hidden patterns

• Reinforcement Learning – Learning through interaction and feedback

Discover How machine learning drives everyday innovations?

From Recommendations and Fraud detection to Chatbots, Forecasting and Medical diagnostics.

Explore the full machine learning pipeline.

From Collecting and Preparing data to Training models, Evaluating their performance and Deploying them into the real world.

Understand the key differences between structured data like Tables and Databases and Unstructured data such as Text, Images, and Audio.

Along with where each type is typically used.

Explore practical ways to collect data, including:

• APIs for automated data access

• Web Scraping for extracting data from websites

• IoT & Sensors for real-time data capture in physical environments

Learn the fundamentals of Ethical data collection.

Covering Privacy, Consent, Legal boundaries, and How to use data responsibly?

Learn How to clean your data by identifying missing values, Spotting outliers, and Getting your dataset ready for analysis.

Explore key preprocessing techniques like:

• Scaling numerical features (Min-Max, Standard Scaling)

• Normalization to bring consistency across data

• Encoding categorical variables into machine-readable form

Understand how to improve model performance by:

• Selecting relevant features using statistical and model-based techniques

• Creating new features from existing data to capture hidden patterns

Learn how to balance skewed datasets using:

• Undersampling & Oversampling

• SMOTE (Synthetic Minority Over-sampling Technique) for synthetic data generation

Use visual tools like Histograms, Scatter plots, Box plots, and Bar charts to Explore data distributions and Uncover key patterns.

Perform:

• Univariate Analysis – Understand single variable distributions

• Bivariate Analysis – Explore relationships between two variables

• Multivariate Analysis – Examine interactions across multiple features

Learn How to interpret the spread and skewness of data, Understand central tendencies, and Recognize both linear and non-linear relationships between variables.

Use Visualizations and Statistical methods to Uncover trends, Spot clusters, Detect anomalies, and Recognize seasonal patterns in your data.

Learn How to rank and evaluate the impact of different features on your target variable to make smarter model choices?

Harness the strength of multiple decision trees to make More accurate predictions and Minimize overfitting.

Understand the basics of Supervised learning, The kinds of problems it helps solve and The full workflow for building and Deploying models.

Learn How to explore and Explain relationships between numerical variables using Simple and Multiple linear regression models.

Use logistic regression to tackle classification problems.

Learn how to interpret Probability scores, Set decision thresholds and Evaluate model performance.

Build Easy-to-understand tree-based models for both regression and classification and learn how to interpret their splits and decision rules.

Use Support Vector Machines (SVM) To solve both linear and non-linear classification or regression tasks through margin-based learning.

Learn How probability-based classification using Bayes’ Theorem works.

Especially useful for Handling text and Categorical data.

Train powerful gradient-boosted models To achieve high performance on structured and tabular datasets.

Use distance-based techniques To classify or predict outcomes by analyzing the nearest data points in your training set.

Use ARIMA To model time series data and make accurate predictions based on trends and patterns over time.

Explore the purpose of unsupervised learning, How it differs from supervised learning and Where it’s commonly applied?

Like Customer segmentation, Anomaly detection, and Data compression.

Learn How to group data into clusters based on similarity using centroid-based methods for clear and meaningful segmentation?

Explore Agglomerative and Divisive clustering methods that build dendrograms to reveal hierarchical relationships and nested groupings within your data.

Use density-based clustering To discover groups of varying shapes and sizes in your data, while also spotting outliers that don’t belong to any cluster.

Simplify large datasets by reducing the number of variables, while keeping key patterns and variance intact.

Making visualization easier and models faster.

Learn How machines make decisions by exploring reward-based learning through real-time interaction and feedback.

Learn how to evaluate binary and multi-class classification models using:

• Accuracy – Overall correctness

• Precision & Recall – Focused on relevance and sensitivity

• F1 Score – Balance between precision and recall

• AUC-ROC Curve – Measures model's ability to distinguish between classes

Understand Why evaluating your model matters, How to choose the right metric and How these choices affect real-world performance.

Explore key metrics to assess the accuracy of regression predictions:

• R² Score (Coefficient of Determination) – Measures how well the model explains the variability

• MAE (Mean Absolute Error) – Average of absolute differences

• MSE (Mean Squared Error) – Average of squared differences

• RMSE (Root Mean Squared Error) – Interprets error in the same units as the target variable

Understand What hyperparameters are, How they influence machine learning models, and Why tuning them is key to getting the best performance.

Learn How to use random sampling to search hyperparameter combinations efficiently?

Saving Time and computational Cost.

Explore exhaustive hyperparameter tuning by testing every possible combination within A defined parameter grid to find the best model configuration.

Use probabilistic modeling to smartly explore the hyperparameter space.

Boosting performance with fewer, more effective iterations.

Master k-fold and stratified cross-validation techniques To test model stability and reduce the risk of overfitting.

Use early stopping to end training when the model stops improving.

Saving time and helping prevent overfitting.

Learn How to save and reload machine learning models using tools like joblib and pickle.

So you don’t have to retrain from scratch and can reproduce results anytime.

Understand What it takes to make a model production-ready.

Including How to package it, integrate it with APIs, and set up the right environment for deployment.

Explore MLflow to track experiments, Manage model versions and Simplify the deployment process with streamlined pipelines.

Implement monitoring techniques to Track model drift, prediction accuracy and system health after deployment using logs, alerts and dashboards.

Explore where traditional machine learning falls short

Why neural networks are designed to go further?

Understand how a Neuron works by starting with Linear Perceptrons and explore their structure, function and limitations

Learn how Multi-layer Neural Networks work by using activation functions like Sigmoid, Tanh, ReLU and Softmax to model complex patterns

Discover how models learn using Gradient Descent, Learning Rates and the Delta Rule especially when working with Non-Linear Activation Functions

Master Backpropagation, Train models with Stochastic and Mini-batch Methods and learn to split datasets effectively for solid evaluation

Learn regularization techniques and validation strategies to improve model generalization.

Set up PyTorch from scratch, Get hands-on with Tensors and Perform basic operations to start building deep learning models

Use PyTorch’s autograd to handle Backpropagation, Apply Gradient Descent and Build a complete model training workflow from start to finish

Use PyTorch to apply Linear and Logistic Regression Techniques for solving real-world prediction problems

Efficiently manage and prepare training data in PyTorch using Dataset, DataLoader and Transforms

Learn how Softmax, Cross-entropy Loss and Activation Functions work together to power neural network classification tasks

Explore how Convolutional Neural Networks (CNNs) are built.

What sets them apart from traditional neural networks?
Why they’re ideal for image-related tasks?

Discover how Image Filters or Kernels help extract features like Edges, Textures and Colors from visual data for deeper analysis

Learn how the convolution layer processes Input Images including RGB channels to create feature maps that highlight important patterns for deeper analysis.

See how Max Pooling and Average Pooling Shrink Feature Maps while keeping the most important details, making models faster and more efficient

Understand how Recurrent Reural Networks (RNNs) stand apart from feedforward models by remembering past inputs making them ideal for handling sequences like text or time series

Explore how RNNs help Predict words, Generate Text and Make sense of sentence structure in natural language processing tasks

Learn to build simple RNN models that can create new sequences like sentences or patterns by learning from existing data

Learn about the limitations of RNNs including Vanishing Gradients and their struggle to capture long-term dependencies in sequences

Explore how LSTM networks work by using memory cells and gates to retain important information over long sequences

"See how LSTMs power real-world applications like Sentiment Analysis, Stock Price Prediction, Speech Recognition and Language Translation by learning from sequences over time.

Understand the computational demands and training time of LSTMs and when newer architectures may offer better performance

Explore how Transformer Architecture transformed Natural Language Processing by allowing parallel processing and learning from long-range context more effectively than earlier models

Explore the Self-Attention Layer which is the Heart of Transformer Models that lets the network focus on the most relevant words or tokens in a sequence

Discover how the Encoder transforms input sequences into rich context-aware representations using stacked attention and feedforward layers

Learn how Decoders use Encoder Outputs and Attention Mechanisms to generate meaningful sequences for tasks like translation and summarization

Use Transformer Models to tackle sequence-based tasks like Text Summarization, Machine Translation and Question Answering with greater accuracy and efficiency

Learn how to fine-tune powerful Pre-Trained Models from libraries like Hugging Face Transformers for your own NLP tasks using minimal data and effort

Learn how to break down sentences and paragraphs into tokens "Words or Subwords that form the foundation of text analysis"

Clean and standardize text by Lowercasing, Removing Punctuation and Correcting Inconsistencies to prepare it for analysis

Improve your text data by removing stop words "Common terms like "the", "is"  and "and" that add little value to your models

Learn how reducing words to their base or root form helps cut down variation and boosts your model’s efficiency and accuracy

Turn text into fixed-length vectors using word frequency counts "A simple but effective way to prepare data for modeling"

Learn how to highlight meaningful words by measuring their importance in a document compared to the entire dataset

Explore embedding techniques like "Word2Vec" and "GloVe" that capture word meaning and context in dense vector spaces

Move beyond single words

Learn to create vector representations of whole sentences using advanced models for richer semantic understanding

Automatically assign text into categories such as Sentiment Analysis, Spam Detection or Topic Tagging using Pre-Trained Models.

Use Transformer Models to translate text across languages and making multilingual communication smooth and effortless

Extract accurate answers from a given text passage using models fine-tuned for context-aware Q&A tasks.

Create concise summaries of long texts like Articles or Conversations while keeping the key meaning intact

Use language models to generate Poems, Emails, Code, Stories or Scripts shaped by your input and the model’s capabilities

Learn to label important parts of images like Objects or Regions to train models for classification and detection tasks.

Boost model performance by expanding your dataset with techniques like Rotation, Flipping, Scaling, and Color Shifts

Learn to standardize Pixel Values and Fine-Tune Brightness and Contrast so your model sees images more clearly and consistently

Resize images to a consistent shape to ensure smooth and efficient processing by neural networks during training and inference

Refresh your understanding of CNN basics ike Convolution Layers, Pooling and Feature Extraction used in image classification and object detection

Learn how ResNet uses skip connections to overcome the vanishing gradient problem, making it possible to train very deep neural networks effectively

See how Inception modules improve accuracy by using parallel filters to capture features at multiple scales without adding heavy computation

Discover how MobileNets use depthwise separable convolutions to create lightweight, efficient models ideal for mobile and embedded devices

Explore how EfficientNet smartly balances depth, width and resolution to deliver top performance across different hardware setups

Learn how Faster R-CNN blends region proposal networks with CNNs to detect objects accurately striking a balance between speed and precision

Explore the YOLO (You Only Look Once) models that detect multiple objects in one swift pass delivering fast, real-time results with strong accuracy

Learn how Mask R-CNN builds on Faster R-CNN to perform instance segmentation detecting objects and outlining them with precise pixel-level masks

Dive into SSD (Single Shot Multibox Detector), an efficient model that handles object classification and localization in a single step ideal for real-time applications

Learn how Semantic Segmentation is used in data science to extract rich insights from images.

This module teaches you to build models that classify each pixel, turning visual data into structured, meaningful information

Get to know the core building blocks of AWS (Compute, Storage, Networking and Databases) and see how they work together to power cloud-based solutions.

Get hands-on with essential AWS tools:

• EC2 (Elastic Compute Cloud) for virtual servers

• S3 (Simple Storage Service) for secure object storage

• VPC (Virtual Private Cloud) for secure networking

• RDS (Relational Database Service) for managed databases

Learn how to set up and manage User Roles, Permissions and Policies to keep AWS resources secure and access well-controlled

Discover how to build and deploy cloud infrastructure using code making your environments consistent, repeatable, and easy to manage with version control

Learn to run code without managing servers using AWS Lambda. Explore how Lambda functions fit into event-driven workflows and support machine learning models

Learn how to Deploy, Manage and Scale Web Apps effortlessly with Elastic Beanstalk (AWS’s orchestration tool) that automates your infrastructure setup

Understand SageMaker’s ecosystem and how it streamlines the end-to-end machine learning workflow in the cloud.

Learn how to Upload, Clean and Preprocess data in SageMaker and choose suitable ML/DL algorithms for your task.

Train models using built-in or custom algorithms and evaluate performance with integrated tools and metrics.

Deploy ML models as scalable, real-time endpoints using SageMaker’s deployment services and monitor their performance.

Understand the building blocks of Deep Learning, including Neural Networks, Activation Functions, Loss functions and Key Architectures like CNNs and RNNs.

Learn how to build and train Deep Learning Models using SageMaker’s built-in tools and supported frameworks like TensorFlow and PyTorch.

Deploy trained models as real-time APIs using SageMaker endpoints, Enabling seamless integration with applications and services.

Implement IAM roles, Encryption and Data Protection Standards to ensure security and compliance with industry regulations.

Learn how to embed ML/DL model predictions into frontend or backend systems using APIs, Flask/Django apps or Lambda functions.

Understand how to host applications and services using AWS services like EC2, Elastic Beanstalk or SageMaker endpoints for efficient scaling and load handling.

Set up monitoring and logging tools like CloudWatch and SageMaker Model Monitor to track performance, usage, and anomalies in real time.

Learn how LLMs process and generate human-like text by leveraging deep learning and vast datasets.

Explore the structure and working principles behind OpenAI’s GPT-3 and ChatGPT, including transformer-based learning.

Discover how LLMs are used in Customer Support, Education, Content Creation, Legal Tech, Healthcare and More.

Get introduced to alternative LLMs such as Claude, Mistral, PaLM and how they differ in capability and use cases.

Learn to use and compare leading Generative AI tools and APIs:

• LLaMA (Meta)

• OpenAI (GPT family)

• Gemini (Google)

• Hugging Face Transformers

Learn the Fundamentals of Prompt Design and why it's crucial in harnessing the true potential of LLMs.

Gain insights into how LLMs interpret input and generate responses based on context and intent.

Understand how to assess and refine model responses for Accuracy, Tone and Relevance

Master techniques for Crafting Clear, Goal-Oriented Prompts for tasks like Summarization, Q&A, Translation and Coding

Explore Advanced Prompting Strategies like Temperature Tuning, System Instructions, Few-Shot Prompting and Formatting Constraints.

Understand "When and Why Fine-Tuning" is needed and explore different approaches to adapting LLMs for new tasks or domains.

Differentiate between Training a model for a specific function (e.g. classification) VS Adapting it for a particular field (e.g. legal, medical)

Explore Architectural Tweaks such as Adapter Layers, LoRA and Parameter-Efficient Tuning for fine-tuning large-scale models efficiently

Learn to Select, Prepare and Preprocess high-quality Datasets aligned with your model’s purpose and desired output style.

Implement End-to-End Fine-Tuning Workflows using PyTorch and Hugging Face Transformers  including Training Loops, Checkpointing and Logging.

Apply Hyperparameter Tuning, Learning Rate Scheduling, Batch Size Adjustments and other techniques to enhance fine-tuning results.