Key Features
Completion Certificate
Internship
Internship Certificate
7 Days Refund Policy
Expert Instructors
One-to-One Session
What Will You Learn?
Accelerate your learning journey with our comprehensive course designed to equip you with essential skills and practical knowledge in Data Science with Gen AI.
- Data Science Foundations
- Machine Learning Basics
- Deep Learning
- Generative AI Concepts
- OpenAI API Integration
- Building AI Models
Requirements
Before getting started with this course, it's beneficial to have the following:
- Laptop with good internet
- Passionate about learning
- No prior experience needed
- Willing to dedicate time
- Curious about emerging AI technologies
Course Completion
Yes
Curriculum
- Introduction to the Course and Dashboard Overview
- Data Industry Overview and Its Significance
- Lab Introduction for Practical Learning
- Overview of the Support System for Assistance
- Community and Networking Introduction
- Understanding Python and Comparing it with Other Languages
- Exploring Python Objects: Numbers, Booleans, and Strings
- Container Objects in Python: Lists, Tuples, and Dictionaries
- The Mutability of Objects in Python
- Understanding Operators: Arithmetic, Bitwise, Comparison, and Assignment Operators
- Operator Precedence and Associativity in Python
- Conditional Statements (If, Else, Elif)
- Loops: Understanding While and For Loops
- Break and Continue Statements, Using the Range Function
- Basic Data Structures in Python: Lists, Tuples, Sets, Dictionaries
- String Handling: Basic Operations and Methods
- Splitting and Joining Strings
- String Formatting Functions and String Interpolation
- List Methods and List Operations
- Working with Lists as Stacks and Queues
- List Comprehensions in Python
- Tuples, Sets, and Dictionary Methods
- Dictionary Comprehensions and Their Applications
- Understanding Dictionary View Objects
- Functions in Python: Basics and Parameter Passing
- Iterators in Python: How They Work
- Working with Generator Functions
- Understanding Lambda Functions and Their Uses
- Applying Map, Reduce, and Filter Functions
- OOPs Concepts: Introduction to Object-Oriented Programming
- Class Creation and Understanding Classes in Python
- Pillars of OOPs: Inheritance, Polymorphism, Encapsulation, and Abstraction
- Decorators: How They Work and Why We Use Them
- Class Methods, Static Methods, and Their Use Cases
- Special (Magic/Dunder) Methods in Python
- Property Decorators: Getters, Setters, and Delete Methods
- Working with Files: Reading and Writing Data
- Buffered Reading and Writing in Files
- Exploring Other File Methods
- Logging and Debugging: Tools for Error Handling
- Modules and Import Statements in Python
- Exception Handling with Try-Except
- Creating Custom Exception Classes
- Common Exceptions in Python
- Best Practices for Exception Handling
- Introduction to Multithreading and Its Benefits
- Understanding Multiprocessing in Python
- Introduction to Pandas Series
- Introduction to Pandas DataFrame
- Understanding Pandas Panel Data
- Basic Functionalities of Pandas
- Reading Data from Various File Formats
- Reindexing Data in Pandas
- Iteration in Pandas for Efficient Data Manipulation
- Sorting Data in Pandas
- Text Data Operations in Pandas and Customization
- Indexing and Selecting Data in Pandas
- Statistical Functions in Pandas
- Applying Window Functions in Pandas
- Working with Date and Time Data in Pandas
- Time Delta Operations in Pandas
- Handling Categorical Data in Pandas
- Data Visualization with Pandas
- Utilizing Pandas Tools for Data Analysis
- Introduction to Numpy Ndarray Objects
- Numpy Data Types and Their Importance
- Array Attributes in Numpy
- Numpy Array Creation Methods
- Creating Arrays from Existing Data in Numpy
- Working with Ranges and Numerical Arrays
- Indexing and Slicing in Numpy Arrays
- Advanced Indexing in Numpy
- Broadcasting in Numpy: What It Is and How It Works
- Iterating Over Arrays in Numpy
- Manipulating Numpy Arrays
- Binary Operations in Numpy Arrays
- String Operations in Numpy Arrays
- Performing Mathematical Operations with Numpy
- Arithmetic Functions in Numpy
- Statistical Operations in Numpy
- Sorting, Searching, and Counting in Numpy
- Numpy Byte Swapping: An Overview
- Copies and Views in Numpy
- Working with Matrices Using Numpy
- Linear Algebra Operations in Numpy
- SQL Query Fundamentals: SELECT, INSERT, UPDATE, DELETE
- SQL Functions and Stored Procedures
- Primary and Foreign Keys in SQL
- Window Functions and Their Applications
- Partitioning and Handling Joins in SQL
- Advanced Joins, Unions, Indexing, and CTE
- Using Triggers and Case Statements in SQL
- Understanding Normal Forms and Pivoting in Databases
- Overview of Basic Statistical Terms
- Types of Statistics and Data
- Levels of Measurement in Statistics
- Measures of Central Tendency: Mean, Median, Mode
- Measures of Dispersion: Variance, Standard Deviation
- Understanding Random Variables and Their Role
- Skewness and its Interpretation
- Covariance and Correlation Analysis
- Probability Density and Distribution Functions
- Types of Probability Distributions: Binomial, Poisson, Normal
- Normal Distribution and Its Significance
- Z-Stats and Their Application in Data Analysis
- Central Limit Theorem Explained
- Estimation Techniques in Statistics
- Hypothesis Testing Concepts
- Understanding Hypothesis Testing Mechanisms
- P-Value Interpretation in Hypothesis Testing
- T-Stats and Their Role in Statistical Inference
- Comparing T-Stats and Z-Stats
- When to Use T-Tests vs Z-Tests
- Types of Errors in Hypothesis Testing: Type 1 and Type 2
- Bayesian Statistics and Bayes Theorem
- Confidence Intervals and Their Importance
- Interpreting Confidence Levels and Margins of Error
- Chi-Square Tests and Applications in Data Science
- Handling Missing Data and Imbalanced Data
- Identifying and Dealing with Outliers
- Feature Scaling Techniques
- Data Encoding Methods
- Feature Selection Methods: Backward Elimination, Forward Elimination
- Recursive Feature Elimination Techniques
- Covariance and Correlation in Feature Engineering
- Variance Inflation Factor (VIF)
- Simple Linear Regression with Hands-on Implementation
- Multiple Linear Regression and Evaluation
- Performance Metrics: MSE, MAE, RMSE
- Logistic Regression and its Evaluation Metrics
- Support Vector Machines: Classifiers and Regressors
- Naive Bayes Classifier Implementation
- K-Nearest Neighbors (KNN) Classification and Regression
- Decision Trees and Random Forest Models
- Gradient Boosting and Adaboost Classifiers
- XGBoost Classifier and Regressor Implementations
- Introduction to Deep Learning
- Why Deep Learning Is Becoming Popular
- Perceptron Intuition
- Artificial Neural Network (ANN) Working
- Backpropagation in ANN
- Chain Rule of Derivatives
- Vanishing Gradient Problem
- Exploding Gradient Problem
- Different Activation Functions
- Different Types of Loss Functions
- Different Types of Optimizers
- Weight Initialization Techniques
- Dropout Layer
- Batch Normalization
- Visualization of Neural Networks
- Colab Pro Setup
- TensorFlow Installation 2.0
- TensorFlow 2.0 Neural Network Creation
- Netron (Model Visualizer)
- PyTorch Installation
- PyTorch Neural Network Creation
- Deep Learning with Computer Vision (CV)
- CNN Fundamentals
- CNN Explained in Detail - CNNexplainer, Tensor Space
- Various CNN-based Architectures
- Training CNN from Scratch
- Building Web Apps for CNN
- Various CNN Architectures with Research Papers and Mathematics
- Deep Learning with Natural Language Processing (NLP)
- Recurrent Neural Networks (RNNs)
- Long Short-Term Memory (LSTM)
- Bi-directional LSTM (BiLSTM)
- Stacked LSTM
- GRU Implementation
- Sequence-to-Sequence (Seq2Seq)
- Encoders and Decoders
- Attention Mechanism
- Attention Neural Networks
- Self-Attention
- Introduction to Transformers
- BERT Model
- GPT-2 Model
- Prompt Design: Various Techniques to Improve Output Quality
- Best Practices in OpenAI Prompt Engineering
- Understanding OpenAI's GPT-3 and GPT-4
- Common Use Cases in OpenAI: Chatbots, Summarizers, Sentiment Analysis
- How GPT-3 and GPT-4 Work
- Setting Up OpenAI API with LangChain
- LangChain: How It Works and Its Applications
- Creating OpenAI Chatbots with LangChain
- Managing Long-Form Content Generation in OpenAI with LangChain