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Home
Courses
Data-Science-AI
Curriculum
11 Sections
112 Lessons
16 Weeks
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Introduction to Python
10
1.1
Why Python for DataScience
1.2
Data Types in Python
1.3
DataTypes -Detailed Study – Methods -Functions
1.4
Conditional Statements -Loops in Python
1.5
File operations in Python
1.6
Python OS Module
1.7
Python Logging
1.8
Exception Handling
1.9
Functions -Class
1.10
RegEX – Regular Expressions
Pandas
12
2.1
Data Frame – Series of Data
2.2
Installing and importing Pandas
2.3
Creating and Inspecting Data
2.4
Creating DataFrames from Dictionary from other sources
2.5
Indexing and Selection
2.6
Data Cleaning and Preprocessing
2.7
Data Transformation
2.8
Handling Categorical Data
2.9
Grouping and Aggregation
2.10
Reshaping and Pivoting
2.11
Working with Dates and Times
2.12
Combining Datasets
Numpy
10
3.1
Introduction to NumPy
3.2
Array Creation
3.3
Array Attributes and Properties
3.4
Data Types and Casting
3.5
Array Indexing and Slicing
3.6
Array Manipulation
3.7
Mathematical and Statistical Operations
3.8
Linear Algebra with NumPy
3.9
Random Number Generation
3.10
Input/Output with NumPy
MatPlotLib
10
4.1
Introduction to Matplotlib
4.2
Basic Plotting with Pyplot
4.3
Understanding Figure and Axes
4.4
Common Plot Types for Data Science
4.5
Customizing Plots
4.6
Working with Subplots
4.7
Styling and Themes
4.8
Plotting with Pandas + Matplotlib
4.9
Handling Categorical and Time-Series Data
4.10
Saving and Exporting Figures
Seaborn
10
5.1
Introduction to Seaborn
5.2
Seaborn vs. Matplotlib: When to Use Which
5.3
Built-in Datasets and Plot Styling
5.4
Categorical Plots (Comparing groups)
5.5
Relational Plots (Relationships between variables)
5.6
Distribution Plots (Univariate & Bivariate)
5.7
Regression and Correlation Visualization
5.8
Matrix and Grid Plots
5.9
Multi-Plot Figures with Figure-Level Functions
5.10
Customization and Integration
Scikit-Learn
10
6.1
Introduction to scikit-learn
6.2
Data Representation in scikit-learn
6.3
Train-Test Splitting and Cross-Validation
6.4
Preprocessing and Feature Engineering
6.5
Supervised Learning – Regression
6.6
Supervised Learning – Classification
6.7
Model Selection and Hyperparameter Tuning
6.8
Unsupervised Learning
6.9
Model Evaluation Deep Dive
6.10
Pipelines and Production Readiness
Machine Learning
10
7.1
Introduction to Machine Learning
7.2
The Machine Learning Project Lifecycle
7.3
Data Preprocessing & Feature Engineering
7.4
Model Evaluation & Validation
7.5
Supervised Learning Algorithms
7.6
Unsupervised Learning
7.7
Model Selection & Hyperparameter Tuning
7.8
Introduction to Model Interpretability
7.9
Handling Imbalanced Data (Classification)
7.10
Introduction to Ethical ML & Responsible AI
Deep Learning
10
8.1
Introduction to Deep Learning
8.2
Neural Network Fundamentals
8.3
Building & Training Models with TensorFlow/Keras
8.4
Data Preprocessing for Deep Learning
8.5
Model Evaluation & Diagnostics
8.6
Convolutional Neural Networks (CNNs) – For Image Data
8.7
Recurrent Neural Networks (RNNs) – For Sequential Data
8.8
Embeddings & Introduction to NLP with DL
8.9
Introduction to Transformers & Modern Architectures (Conceptual)
8.10
Generative Deep Learning (Overview & Projects)
Generative AI
10
9.1
Introduction to Generative AI
9.2
Foundations: Probability, Latent Space & Representation Learning
9.3
Autoencoders (AEs) and Variational Autoencoders (VAEs)
9.4
Generative Adversarial Networks (GANs)
9.5
Sequence Generation with RNNs and LSTMs
9.6
Transformers and Large Language Models (LLMs)
9.7
Foundation Models & Retrieval-Augmented Generation (RAG)
9.8
Diffusion Models (Modern Image/Video Generation)
9.9
Evaluation of Generative Models
9.10
Ethical, Societal, and Safety Considerations
Fundamentals of Data Science & AI
Explore core concepts in data science and the basics of AI models like machine learning and deep learning.
12
10.1
Introduction to Data Science
10.2
What is Artificial Intelligence?
10.3
Introduction to Machine Learning
10.4
Supervised vs. Unsupervised Learning
10.5
Data Collection and Preparation
10.6
Feature Engineering Basics
10.7
Understanding Deep Learning
10.8
Training and Evaluating Models
10.9
Use Cases of ML and DL
10.10
Ethics in Data Science and AI
10.11
Foundations of Data Science Quiz
0 Questions
10.12
AI and ML Concepts Quiz
0 Questions
Generative AI and Next-Gen Applications
Dive into generative AI, including models like ChatGPT, and learn real-world applications and future trends in AI.
12
11.1
What is Generative AI?
11.2
Exploring Language Models
11.3
Generative AI for Images and Art
11.4
Hands-On with AI Text Generation
11.5
Introduction to GANs
11.6
Safe Use of Generative AI Tools
11.7
Applications in Business and Science
11.8
Future of Generative Technologies
11.9
Common Challenges and Limitations
11.10
Careers in AI and Data Science
11.11
Generative AI Concepts Quiz
0 Questions
11.12
Responsible AI and Applications Quiz
0 Questions
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