## LEARN PYTHON AND LEARNING

1. PYTHON BASICS

•Data types (strings,integers, floats, etc.). •Variables

•Control flow (if/elsetSatements, loops) •Functions

•Modules and packages

• Input/output

2. MATHEMATICS

•Linear algebra (vectors, matrices,a mtrix multiplication)

• Calculus(differentiation,nitegration) Probability and statistics

•Optimization

3. DATA STRUCTURES

• Lists, tuples, and dictionaries

• Sets and frozen sets

• Stacks and queues

• Linked lists

• Trees (binary trees, AVL trees, binary search trees)

• Graphs (directed and undirected graphs, adjacency matrix, adjacency list)

4. LIBRARIES

•NumPy: for numerical computations and array

manipulation

•Pandas: for data analysisn and manipulation

• Matplotlib: for data visualization

•Scikit-learn: for machine learning algorithms and tools

• TensorFlow or PyTorch: for deep learning

5. DATA

•PREPROCESSING

•Handling missing a Value scaling and normalization

• Encoding categorical data

6. ML ALGORITHMS

• Linear regression

• Logistic regression

• Decision trees

• Random forests

• Support vector machines • Naive Bayes

•K-nearest neighbors

• Clustering (K-means, hierarchical clustering)

• Dimensionality reduction (PCA)

• Feature selection and engineering

7. MODEL EVALUATION

•Accuracy, precision, recall,F1 score

• Confusion matrix

• Cross-validation

•Bias-variance tradeoff

8. DEEP LEARNING

• Neural networks

• Convolutional neural networks (CNNs)

• Recurrent neural networks (RNNs)

• Generative adversarial networks (GANs)

9. PROJECTS

•Develop a machine learning project from scratch

• Use real datasets for training and testing

Evaluate the performance of the models Optimize the

models for better performance Deploy the models in production

## Comments

## Post a Comment