Creating a simple Machine Learning Roadmap can help to systematically learn the necessary concepts and skills, from the basic basics to the realm of advanced topics.
Here's a step-by-step guide:

1. Introduction to Machine Learning
– Learn basic concepts: Understand what machine learning is, different types of machine learning (supervised, unsupervised, reinforcement learning) and key terms (model, algorithm, function, label, training, testing, etc.).
2. Mathematics for machine learning
– Linear algebra: vectors, matrices, matrix multiplication, eigenvalues, eigenvectors.
– Calculus: derivatives, partial derivatives, gradients.
– Probability theory and statistics: probability distributions, Bayes' theorem, expectation, variance, hypothesis testing.

3 Programming skills
– Python: Focus on libraries used in machine learning, such as NumPy, pandas, Matplotlib and Scikit-Learn.

4. Exploratory Data Analysis (EDA)
– Data cleaning: dealing with missing data, data normalization and standardization.
– Data visualization: plot data using libraries such as Matplotlib and Seaborn.

5. Supervised learning
– Linear Regression: Understand simple and multiple linear regression.
– Classification algorithms: logistic regression, K-nearest neighbors, decision trees, support vector machines.
– Evaluation metrics: accuracy, precision, recall, F1 score, ROC-AUC.

6. Unsupervised learning
– Clustering: K-means, hierarchical clustering, DBSCAN.
– Dimensionality reduction: PCA, t-SNE.

7. Model evaluation and improvement
– Cross-validation: k-fold cross-validation, hyperparameter tuning.
– Regularization: regularization L1 and L2.

8. Advanced topics
– Neural Networks and Deep Learning: Basics of Neural Networks, Backpropagation, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs).
– Natural Language Processing (NLP): text preprocessing, sentiment analysis, sequence models.

9. Practical application and projects
– Build projects: Apply your knowledge by building projects. Start with simple projects, such as predicting house prices, and then move on to more complex projects, such as image classification or sentiment analysis.
– Kaggle Competitions: Compete in Kaggle Competitions to test your skills and learn from others.
– Portfolio: Create a portfolio of your projects to showcase your skills to potential employers.

10. Stay informed and keep learning
– Blogs and magazines: Follow machine learning blogs, research papers and magazines to stay up to date with the latest trends.
– Communities: Join machine learning communities like Kaggle, Reddit's r/MachineLearning, and Stack Overflow.

By following this roadmap, you can systematically build a solid foundation in machine learning and develop the skills needed to tackle real-world problems.

#datascience #dataanalysis #coding #100daysofcode #developer #fullstack #fypシ #codenewbie #webdeveloper #softwareengineer #softwaredeveloper #webdevelopment #pynade #javascript #frontend #backend #java #python #machinelearning #tiktok #programming

Please take the opportunity to connect and share this video with your friends and family if you find it useful.