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.
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
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.
- Vectors, Operations on Matrix
- Reshaping Arrays
- Diagonal Operations, Trace
- Mean, Variance, and Standard Deviation
- Add, Subtract, Multiply, Dot, and Cross Product.
- Different ways to create DataFrame
- Series and DataFrames
- Slicing, Rows, and Columns
- Read, Write Operations with CSV files
- Handling Missing values
- GroupBy and Concatenation
One of the most popular data visualization libraries in Python is Matplotlib, which forms the foundation for other libraries like Seaborn and Plotly.
- Bar Chart, Pie Chart, Histogram, Scatter Plot
- Format Strings in Plots
- Label Parameters, Legend
- Wide Range of Plot Types
- Statistical Enhancements
- Categorical Data Visualization
- Customization and Theming
Additionally, you can learn Ploty and Tableau if you want.
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.
- 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
- 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
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:
- Linear Regression
- Logistic Regression
- Decision Tree
- Gradient Descent
- Random Forest
- Ridge and Lasso Regression
- Naive Bayes
- Support Vector Machine
- KMeans Clustering
- Principal Component Analysis
- Recommender systems
- Predictive Analytics
- Exploratory Data Analysis
Natural Language Processing (NLP) is of paramount importance for Machine Learning (ML) engineers for several reasons. NLP enables ML engineers to work with human language data, which is prevalent in various applications and industries.
- Handling Unstructured Text DataSentiment analysis
- Text Classification and Sentiment Analysis
- Named Entity Recognition (NER)
- Text preprocessing
- Text Generation and Language Translation
- Topic Modeling
- Machine Translation, BLEU Score
- Summarization, ROUGE Score
- Language Modeling, Perplexity
- Building a text classifier
- Speech Recognition
The best way to master deep learning algorithms is to work with TensorFlow or PyTorch.
- Neural networks basics
- Activation functions
- Backpropagation algorithm
- Popular deep learning frameworks: TensorFlow or PyTorch
- Convolutional Neural Networks (CNN) for computer vision
- Recurrent Neural Networks (RNN) for sequential data
- Generative Adversarial Networks (GAN) for data generation
Computer vision is a fascinating field that involves teaching computers to understand and interpret visual information from images and videos, just like the human visual system does.
- Working with OpenCV
- Understanding Pretrained models like AlexNet, ImageNet, ResNet.
- Neural Networks
- Building a perceptron
- Building a single-layer neural network
- Building a deep neural network
- Recurrent neural network for sequential data analysis
- Image Content Analysis
- Operating on images using OpenCV-Python
- Detecting edges
You can master any one of the cloud services providers from AWS, GCP, and Azure. You can switch easily once you understand one of them. We will focus on AWS - Amazon Web Services first
- Working with Deep Learning on AWS
- Amazon Rekognition - Image Applications
- Amazon Textract - Extract Text
- Amazon Transcribe - Speech to Text
- AWS Polly - Voice Analysis
- Amazon Lex - Natural Language Understanding
- Amazon SageMaker - Building and deploying models
- Deploy ML models using Flask
Git and GitHub are essential tools in the field of Machine Learning (ML) for version control, collaboration, and sharing ML projects with the community.
- Understanding Git
- Commands and How to commit your first code?
- How to use GitHub?
- How to make your first open-source contribution?
- How to work with a team? - Part 1
- How to create your stunning GitHub profile?
- How to build your own viral repository?
- Building a personal landing page for your Portfolio for FREE
- How to grow followers on GitHub?
- How to work with a team? Part 2 - issues, milestone and projects
