Data Science & ML

• 1: Introduction to Data Science
• 2: Python Libraries
• 2.1: NumPy
• 2.2: Pandas
• 2.3: Matplotlib and Seaborn:
• 3: Data Analysis and Preprocessing
• 4: Machine Learning Fundamentals
• 5: Supervised Learning
• 6: Unsupervised Learning
• 7: Deep Learning
• 8: Natural Language Processing (NLP)
• 9: Computer Vision
• 10: Deploying Machine Learning Models

1 - Introduction to Data Science

What is data science? Data science workflow Python for data science

2 - Python Libraries

NumPy: numpy Arrays and numerical operations Pandas: pandas Data manipulation and analysis Matplotlib and Seaborn: visualization Data visualization

2.1 - NumPy

Arrays and numerical operations

2.2 - Pandas

Data manipulation and analysis

2.3 - Matplotlib and Seaborn:

Data visualization

3 - Data Analysis and Preprocessing

Data exploration and cleaning Feature engineering Handling missing data Scaling and normalization

4 - Machine Learning Fundamentals

Supervised learning Unsupervised learning Reinforcement learning Model evaluation and validation

5 - Supervised Learning

Linear and logistic regression Decision trees and random forests Support vector machines (SVMs) Naive Bayes

6 - Unsupervised Learning

Clustering (K-Means, hierarchical) Dimensionality reduction (PCA, t-SNE) Association rule mining

7 - Deep Learning

Neural networks Convolutional neural networks (CNNs) Recurrent neural networks (RNNs) TensorFlow and Keras PyTorch

8 - Natural Language Processing (NLP)

Text preprocessing and feature extraction Sentiment analysis Named entity recognition Language models

9 - Computer Vision

Image processing and feature extraction Object detection and recognition Semantic segmentation

10 - Deploying Machine Learning Models

Model serialization and packaging Serving models with Flask or FastAPI Containerization with Docker Cloud deployment (AWS, GCP, Azure)