Curriculum
12 Sections
51 Lessons
16 Weeks
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Fundamentals of Python
8
1.1
History & Background
1.2
Basic Syntax
1.3
Data Types: Lists, Tuples, Dictionaries etc.
1.4
Basic Operators
1.5
Control Flow, Functions, File I/O & Exceptions
1.6
Classes & Libraries
1.7
Functional Programming
1.8
Tools Covered: Python IDLE (for writing and running Python programs).
Python Programming for Mathematical and Scientific Computation
4
2.1
NumPy (package for fast numerical calculation)
2.2
Pandas (Tabular data manipulation package)
2.3
Plotting and Charting with Matplotlib, Seaborn and plotly (Data Visualization package)
2.4
Tools covered: NumPy, Pandas, Matplotlib, Seaborn, plotly
Exploratory Data Analysis
1
3.1
Learn to analyze, visualize, and summarize data to uncover patterns, insights, and relationships before modeling
Introduction to Machine Learning
5
4.1
Machine Learning Overview
4.2
Types of Machine Learning
4.3
Predictive modelling with scikit-learn (Machine Learning package)
4.4
Basics of building Machine Learning Models
4.5
Tools Covered: Python, NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn.
Building models with popular Supervised Learning algorithms
5
5.1
Linear Regression
5.2
Logistic Regression
5.3
Decision Tree
5.4
Neural Networks
5.5
Tools Covered: Python, NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn
Model Evaluation
6
6.1
Model Evaluation & Selection
6.2
Overfitting and Underfitting
6.3
Cross-Validation
6.4
Confusion Matrices & Advanced Evaluation
6.5
Precision-recall and ROC curves
6.6
Tools Covered: Python, scikit-learn, Matplotlib, and Seaborn.
Building models with popular Unsupervised Learning
3
7.1
PCA
7.2
Kmeans
7.3
Tools Covered: Python, scikit-learn, Matplotlib, and Seaborn.
Introduction to Deep Learning
5
8.1
Basics of Neural Networks
8.2
Building Neural Networks with tensorflow.keras
8.3
Neural Network for Classification
8.4
Neural Network for Regression
8.5
Tools Covered: Python and tensorflow.keras
Introduction to GenAI
5
9.1
Introduction to Generative AI
9.2
Key Characteristics of Large Language Models (LLMs)
9.3
Hallucination in Generative AI
9.4
Popular LLMs
9.5
Tools Covered: Python, OpenAI API, Hugging Face Transformers
DataBase
5
10.1
MySQL, PostgreSQL
10.2
Redis
10.3
Cassandra
10.4
Neo4J
10.5
MongoDB
Cloud
4
11.1
IBM Watson
11.2
Microsoft Azure
11.3
Amazon Al
11.4
Google Cloud ML
Internship - 1 Live Project, Internship duration -2 months
0
AI & Applied Data Science with Python(Level-1)
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