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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A Full Stack ML (Machine Learning) Roadmap involves learning the necessary skills and technologies to become proficient in all aspects of machine learning, including data collection and preprocessing, model development, deployment, and maintenance.
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# Advanced Machine Learning Engineer Roadmap A Full Stack ML (Machine Learning) Roadmap involves learning the necessary skills and technologies to become proficient in all aspects of machine learning, including data collection and preprocessing, model development, deployment, and maintenance.  Below is a comprehensive roadmap that outlines the key steps and topics you should cover on your journey to becoming a Full Stack ML engineer. Keep in mind that this is a high-level roadmap, and you can customize it based on your interests and goals. # 1. Python Programming Python is widely considered the best programming language for machine learning. It has gained immense popularity in the field of data science and machine learning. - Python basics, Variables, Operators, Conditional Statements - List and Strings - Dictionary, Tuple, Set - While Loop, Nested Loops, Loop Else - For Loop, Break, and Continue statements - Functions, Return Statement, Recursion - File Handling, Exception Handling - Object-Oriented Programming # 2. Data Analysis NumPy and Pandas are two essential Python libraries that provide tools for handling and manipulating large datasets efficiently. NumPy is primarily used for numerical computations, while Pandas is built on top of NumPy and offers high-level data structures and functions designed to simplify data analysis tasks. ### Numpy - Vectors, Operations on Matrix - Reshaping Arrays - Diagonal Operations, Trace - Mean, Variance, and Standard Deviation - Add, Subtract, Multiply, Dot, and Cross Product. ### Pandas - Different ways to create DataFrame - Series and DataFrames - Slicing, Rows, and Columns - Read, Write Operations with CSV files - Handling Missing values - GroupBy and Concatenation # 3. Data Visualization One of the most popular data visualization libraries in Python is Matplotlib, which forms the foundation for other libraries like Seaborn and Plotly. ### Matplotlib - Bar Chart, Pie Chart, Histogram, Scatter Plot - Format Strings in Plots - Label Parameters, Legend ### Seaborn - Wide Range of Plot Types - Statistical Enhancements - Categorical Data Visualization - Customization and Theming Additionally, you can learn Ploty and **Tableau** if you want. # 4. Statistics Statistics for machine learning come as a significant tool that studies this data for recognizing certain patterns. It helps you find unseen patterns by providing a proper direction for utilizing, analyzing, and presenting the raw data that is successfully implemented in fields like computer vision and speech analysis. ### Descriptive Statistics - Continuous and Discrete Functions - Probability Distribution - Gaussian Normal Distribution - Measure of Frequency and Central Tendency - Measure of Dispersion - Skewness and Kurtosis - Normality Test - Regression Analysis - Linear and Non-Linear Relationship with Regression - ANOVA - Homoscedasticity - Goodness of Fit ### Inferential Statistics - t-Test, z-Test - Hypothesis Testing - Type I and Type II errors - One-way and Two way ANOVA - Chi-Square Test - Implementation of continuous and categorical data # 5. Machine Learning To become proficient in machine learning algorithms, the most effective approach is to utilize the Scikit-Learn framework. Scikit-Learn provides a wealth of pre-defined algorithms that can be easily implemented by creating class objects. Familiarizing yourself with these algorithms is essential, especially those falling under the categories of Supervised and Unsupervised Machine Learning: 1. Linear Regression 2. Logistic Regression 3. Decision Tree 4. Gradient Descent 5. Random Forest 6. Ridge and Lasso Regression 7. Naive Bayes 8. Support Vector Machine 9. KMeans Clustering ### Other important things to know - Principal Component Analysis - Recommender systems
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matched fp:5ca94d84ac8cb2b2, topic:deep-learning, topic:neural-network, topic:pytorch
matched fp:5ca94d84ac8cb2b2, topic:computer-vision, topic:yolo, topic:opencv
matched fp:5ca94d84ac8cb2b2, topic:nlp, topic:natural-language-processing