For those of you who are new to Data Science and Machine learning algorithms, let me try to give you a brief overview. ML Algorithms can be categorized into three types: supervised learning, unsupervised learning, and reinforcement learning.

1. Supervised Learning:
- Definition: Algorithms learn from labeled training data, making predictions or decisions based on input-output pairs.
- Examples: Linear regression, decision trees, support vector machines (SVM), and neural networks.
- Applications: Email spam detection, image recognition, and medical diagnosis.

2. Unsupervised Learning:
- Definition: Algorithms analyze and group unlabeled data, identifying patterns and structures without prior knowledge of the outcomes.
- Examples: K-means clustering, hierarchical clustering, and principal component analysis (PCA).
- Applications: Customer segmentation, market basket analysis, and anomaly detection.

3. Reinforcement Learning:
- Definition: Algorithms learn by interacting with an environment, receiving rewards or penalties based on their actions, and optimizing for long-term goals.
- Examples: Q-learning, deep Q-networks (DQN), and policy gradient methods.
- Applications: Robotics, game playing (like AlphaGo), and self-driving cars.

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Credits: https://t.iss.one/datasciencefun

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Want to make a transition to a career in data?

Here is a 7-step plan for each data role

Data Scientist

Statistics and Math: Advanced statistics, linear algebra, calculus.
Machine Learning: Supervised and unsupervised learning algorithms.
xData Wrangling: Cleaning and transforming datasets.
Big Data: Hadoop, Spark, SQL/NoSQL databases.
Data Visualization: Matplotlib, Seaborn, D3.js.
Domain Knowledge: Industry-specific data science applications.

Data Analyst

Data Visualization: Tableau, Power BI, Excel for visualizations.
SQL: Querying and managing databases.
Statistics: Basic statistical analysis and probability.
Excel: Data manipulation and analysis.
Python/R: Programming for data analysis.
Data Cleaning: Techniques for data preprocessing.
Business Acumen: Understanding business context for insights.

Data Engineer

SQL/NoSQL Databases: MySQL, PostgreSQL, MongoDB, Cassandra.
ETL Tools: Apache NiFi, Talend, Informatica.
Big Data: Hadoop, Spark, Kafka.
Programming: Python, Java, Scala.
Data Warehousing: Redshift, BigQuery, Snowflake.
Cloud Platforms: AWS, GCP, Azure.
Data Modeling: Designing and implementing data models.

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ML Engineer/MLOps Engineer

ML Algorithms: Understanding various ML algorithms.
Model Deployment: Docker, Kubernetes, Flask.
Data Pipelines: Apache Airflow, Prefect.
DevOps: CI/CD, Git, Terraform.
Programming: Python, Java/C++.
Model Monitoring: Monitoring tools for ML models.
Cloud ML: AWS SageMaker, Google AI, Azure ML.

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AI Engineer

Deep Learning: Neural networks, CNNs, RNNs, transformers.
Programming: Python, TensorFlow, PyTorch, Keras.
NLP: NLTK, SpaCy, Hugging Face.
Computer Vision: OpenCV techniques.
Reinforcement Learning: RL algorithms and applications.
LLMs and Transformers: Advanced language models.
LangChain and RAG: Retrieval-augmented generation techniques.
Vector Databases: Managing embeddings and vectors.
AI Ethics: Ethical considerations and bias in AI.
R&D: Implementing AI research papers.

For each role except for data analyst where programming is not explicitly required, it’s important to learn a programming language like Python. Knowing SQL is equally as important for all roles.

Data science is the first role that embraces machine learning, and as you’re headging towards AI, you’ll see its subsets like deep learning, reinforcement learning, as well as computer vision and NLP.

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7 Free Kaggle Micro-Courses for Data Science Beginners with Certification

Python

https://www.kaggle.com/learn/python

Pandas

https://www.kaggle.com/learn/pandas

Data visualization

https://www.kaggle.com/learn/data-visualization

Intro to sql

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Advanced Sql

https://www.kaggle.com/learn/advanced-sql

Intro to ML

https://www.kaggle.com/learn/intro-to-machine-learning

Advanced ML

https://www.kaggle.com/learn/intermediate-machine-learning

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