What You Will Learn

This course covers essential topics in Artificial Intelligence (AI) and Machine Learning (ML) over three months. You will progress from basic concepts to advanced techniques.

Core Topics:

Month 1 – Python & Data Foundations:

• Learn Python programming essentials, including libraries like NumPy and Pandas.

• Practice data visualization using Matplotlib, Seaborn, and Plotly.

• Understand statistics and probability relevant to AI.

• Complete a mini project on Exploratory Data Analysis (EDA).

Month 2 – Core Machine Learning:

• Explore supervised learning techniques like regression, decision trees, and random forests.

• Study unsupervised learning methods such as clustering, PCA, and t-SNE.

• Learn feature engineering and model evaluation techniques.

• Work on a mini project involving customer segmentation and predictive modeling.

Month 3 – Advanced AI & Deployment:

• Delve into deep learning concepts like neural networks and convolutional neural networks (CNNs).

• Learn about Natural Language Processing (NLP) and transformers.

• Understand model deployment using Flask, FastAPI, and Docker.

• Complete a capstone project where you design and deploy an end-to-end AI application.

Skills You Will Practice:

• Programming in Python

• Data analysis and visualization

• Applying machine learning algorithms

• Building deep learning models

• Deploying AI solutions as web APIs

Tools and Techniques Covered:

• Development tools: Python, Jupyter, VS Code

• Data handling tools: Pandas, NumPy, Scikit-learn

• Visualization tools: Matplotlib, Seaborn, Plotly

• Deep learning frameworks: TensorFlow, Keras, PyTorch

• NLP tools: NLTK, Hugging Face Transformers

• Deployment technologies: Flask, FastAPI, Docker

This structured approach ensures a clear learning progression from foundational concepts to complex applications in AI and ML.