Are you looking to become a machine learning engineer?

I created a free and comprehensive roadmap. Let's go through this post and explore what you need to know to become an expert machine learning engineer:

Math & Statistics

Just like most other data roles, machine learning engineering starts with strong foundations from math, precisely linear algebra, probability and statistics.

Here are the probability units you will need to focus on:

Basic probability concepts statistics
Inferential statistics
Regression analysis
Experimental design and A/B testing Bayesian statistics
Calculus
Linear algebra

Python:

You can choose Python, R, Julia, or any other language, but Python is the most versatile and flexible language for machine learning.

Variables, data types, and basic operations
Control flow statements (e.g., if-else, loops)
Functions and modules
Error handling and exceptions
Basic data structures (e.g., lists, dictionaries, tuples)
Object-oriented programming concepts
Basic work with APIs
Detailed data structures and algorithmic thinking

Machine Learning Prerequisites:

Exploratory Data Analysis (EDA) with NumPy and Pandas
Basic data visualization techniques to visualize the variables and features.
Feature extraction
Feature engineering
Different types of encoding data

Machine Learning Fundamentals

Using scikit-learn library in combination with other Python libraries for:

Supervised Learning: (Linear Regression, K-Nearest Neighbors, Decision Trees)
Unsupervised Learning: (K-Means Clustering, Principal Component Analysis, Hierarchical Clustering)
Reinforcement Learning: (Q-Learning, Deep Q Network, Policy Gradients)

Solving two types of problems:
Regression
Classification

Neural Networks:
Neural networks are like computer brains that learn from examples, made up of layers of "neurons" that handle data. They learn without explicit instructions.

Types of Neural Networks:

Feedforward Neural Networks: Simplest form, with straight connections and no loops.
Convolutional Neural Networks (CNNs): Great for images, learning visual patterns.
Recurrent Neural Networks (RNNs): Good for sequences like text or time series, because they remember past information.

In Python, it’s the best to use TensorFlow and Keras libraries, as well as PyTorch, for deeper and more complex neural network systems.

Deep Learning:

Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled.

Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Long Short-Term Memory Networks (LSTMs)
Generative Adversarial Networks (GANs)
Autoencoders
Deep Belief Networks (DBNs)
Transformer Models

Machine Learning Project Deployment

Machine learning engineers should also be able to dive into MLOps and project deployment. Here are the things that you should be familiar or skilled at:

Version Control for Data and Models
Automated Testing and Continuous Integration (CI)
Continuous Delivery and Deployment (CD)
Monitoring and Logging
Experiment Tracking and Management
Feature Stores
Data Pipeline and Workflow Orchestration
Infrastructure as Code (IaC)
Model Serving and APIs

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Q. Explain the data preprocessing steps in data analysis.

Ans. Data preprocessing transforms the data into a format that is more easily and effectively processed in data mining, machine learning and other data science tasks.
1. Data profiling.
2. Data cleansing.
3. Data reduction.
4. Data transformation.
5. Data enrichment.
6. Data validation.

Q. What Are the Three Stages of Building a Model in Machine Learning?

Ans. The three stages of building a machine learning model are:

Model Building: Choosing a suitable algorithm for the model and train it according to the requirement

Model Testing: Checking the accuracy of the model through the test data

Applying the Model: Making the required changes after testing and use the final model for real-time projects


Q. What are the subsets of SQL?

Ans. The following are the four significant subsets of the SQL:

Data definition language (DDL): It defines the data structure that consists of commands like CREATE, ALTER, DROP, etc.

Data manipulation language (DML): It is used to manipulate existing data in the database. The commands in this category are SELECT, UPDATE, INSERT, etc.

Data control language (DCL): It controls access to the data stored in the database. The commands in this category include GRANT and REVOKE.

Transaction Control Language (TCL): It is used to deal with the transaction operations in the database. The commands in this category are COMMIT, ROLLBACK, SET TRANSACTION, SAVEPOINT, etc.


Q. What is a Parameter in Tableau? Give an Example.

Ans. A parameter is a dynamic value that a customer could select, and you can use it to replace constant values in calculations, filters, and reference lines.
For example, when creating a filter to show the top 10 products based on total profit instead of the fixed value, you can update the filter to show the top 10, 20, or 30 products using a parameter.

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2. What are your roles and responsibilities of your project?
3. Difference between Import Mode and Direct Mode?
4. What kind of projects have you worked on Domain?
5. How do you handle complex data transformations in Power Query? Can you provide an example of a challenging transformation you implemented?
6. What challenges you faced while doing a projects?
7. Types of Refreshes in Power BI?
8. What is DAX in Power BI?
9. How do you perform data cleansing and transformation in Power BI?
10. How do you connect to data sources in Power BI?
11. What are the components in Power BI?
12. What is Power Pivot will do in Power BI?
13. Write a query to fetch top 5 employees having highest salary?
14. Write a query to find 2nd highest salary from employee table?
15. Difference between Rank function & Dense Rank function in SQL?
16. Difference between Power BI Desktop & Power BI Service?
17. How will you optimize Power BI reports?
18. What are the difficulties you have faced when doing a projects?
19. How can you optimize a SQL query?
20. What is Indexes?
21. How ETL process happen in Power BI?
22. What is difference between Star schema & Snowflake schema and how will know when to use which schemas respectively?
23. How will you perform filtering & it's types?
24. What is Bookmarks?
25. Difference between Drilldown and Drill through in Power BI?
26. Difference between Calculated column and measure?
27. Difference between Slicer and Filter?
28. What is a use Pandas, Matplotlib, seaborn Libraries?
29. Difference between Sum and SumX?
30. Do you have any questions?

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Machine Learning Algorithms every data scientist should know:

📌 Supervised Learning:

🔹 Regression
∟ Linear Regression
∟ Ridge & Lasso Regression
∟ Polynomial Regression

🔹 Classification
∟ Logistic Regression
∟ K-Nearest Neighbors (KNN)
∟ Decision Tree
∟ Random Forest
∟ Support Vector Machine (SVM)
∟ Naive Bayes
∟ Gradient Boosting (XGBoost, LightGBM, CatBoost)


📌 Unsupervised Learning:

🔹 Clustering
∟ K-Means
∟ Hierarchical Clustering
∟ DBSCAN

🔹 Dimensionality Reduction
∟ PCA (Principal Component Analysis)
∟ t-SNE
∟ LDA (Linear Discriminant Analysis)


📌 Reinforcement Learning (Basics):
∟ Q-Learning
∟ Deep Q Network (DQN)


📌 Ensemble Techniques:
∟ Bagging (Random Forest)
∟ Boosting (XGBoost, AdaBoost, Gradient Boosting)
∟ Stacking

Don’t forget to learn model evaluation metrics: accuracy, precision, recall, F1-score, AUC-ROC, confusion matrix, etc.

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A-Z of essential data science concepts

A: Algorithm - A set of rules or instructions for solving a problem or completing a task.
B: Big Data - Large and complex datasets that traditional data processing applications are unable to handle efficiently.
C: Classification - A type of machine learning task that involves assigning labels to instances based on their characteristics.
D: Data Mining - The process of discovering patterns and extracting useful information from large datasets.
E: Ensemble Learning - A machine learning technique that combines multiple models to improve predictive performance.
F: Feature Engineering - The process of selecting, extracting, and transforming features from raw data to improve model performance.
G: Gradient Descent - An optimization algorithm used to minimize the error of a model by adjusting its parameters iteratively.
H: Hypothesis Testing - A statistical method used to make inferences about a population based on sample data.
I: Imputation - The process of replacing missing values in a dataset with estimated values.
J: Joint Probability - The probability of the intersection of two or more events occurring simultaneously.
K: K-Means Clustering - A popular unsupervised machine learning algorithm used for clustering data points into groups.
L: Logistic Regression - A statistical model used for binary classification tasks.
M: Machine Learning - A subset of artificial intelligence that enables systems to learn from data and improve performance over time.
N: Neural Network - A computer system inspired by the structure of the human brain, used for various machine learning tasks.
O: Outlier Detection - The process of identifying observations in a dataset that significantly deviate from the rest of the data points.
P: Precision and Recall - Evaluation metrics used to assess the performance of classification models.
Q: Quantitative Analysis - The process of using mathematical and statistical methods to analyze and interpret data.
R: Regression Analysis - A statistical technique used to model the relationship between a dependent variable and one or more independent variables.
S: Support Vector Machine - A supervised machine learning algorithm used for classification and regression tasks.
T: Time Series Analysis - The study of data collected over time to detect patterns, trends, and seasonal variations.
U: Unsupervised Learning - Machine learning techniques used to identify patterns and relationships in data without labeled outcomes.
V: Validation - The process of assessing the performance and generalization of a machine learning model using independent datasets.
W: Weka - A popular open-source software tool used for data mining and machine learning tasks.
X: XGBoost - An optimized implementation of gradient boosting that is widely used for classification and regression tasks.
Y: Yarn - A resource manager used in Apache Hadoop for managing resources across distributed clusters.
Z: Zero-Inflated Model - A statistical model used to analyze data with excess zeros, commonly found in count data.

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