ML Mastery in 5 Days: A Comprehensive Roadmap with 50 Essential Topics!
Day 1:
Learn the basics of programming in Python. You can use online resources like Codecademy or Coursera to learn Python.
Install necessary libraries for machine learning like NumPy, Pandas, and Scikit-learn.
Understand the basics of data types and data structures in Python.
Day 2:
Learn the basics of statistics and probability. You can use online resources like Khan Academy or Coursera to learn these concepts.
Understand the different types of data distributions and probability distributions.
Day 3:
Learn the different types of machine learning algorithms like supervised learning, unsupervised learning, and reinforcement learning.
Understand the difference between regression and classification problems.
Learn about decision trees, random forests, and k-nearest neighbors algorithms.
Day 4:
Learn about neural networks and deep learning.
Understand the basics of how neural networks work, including forward and backpropagation.
Learn about different types of neural networks like convolutional neural networks and recurrent neural networks.
Day 5:
Practice implementing machine learning algorithms using Python and Scikit-learn.
Work on some real-world datasets and try to build models to predict outcomes.
Keep in mind that this is just a starting point, and mastering machine learning requires much more time and effort
50 Essential Topics To Master ML Are:
Linear regression
Logistic regression
k-Nearest Neighbors
Decision Trees
Random Forests
Naive Bayes
Support Vector Machines
Gradient Boosting Machines
Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Generative Adversarial Networks
Autoencoders
Principal Component Analysis
Independent Component Analysis
Singular Value Decomposition
Factorization Machines
Matrix Factorization
Bayesian Networks
Hidden Markov Models
Collaborative Filtering
Content-based Filtering
Association Rule Mining
Apriori Algorithm
K-Means Clustering
DBSCAN
Hierarchical Clustering
Gaussian Mixture Models
Spectral Clustering
Latent Dirichlet Allocation
t-SNE
Ridge Regression
Lasso Regression
Elastic Net
Decision Boundary Visualization
Outlier Detection
Model Selection Techniques
Cross-Validation
Hyperparameter Tuning
Ensemble Learning
Bagging
Boosting
Stacking
Bias-Variance Tradeoff
Overfitting and Underfitting
Regularization
Transfer Learning
Data Augmentation
Active Learning
Model Interpretation and Explainability