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.

Complete Roadmap to land a Data Scientist job in 2025

Phase 1: Build Foundations (3-6 months)

1. Learn Python programming basics
2. Understand statistics and mathematics concepts (linear algebra, calculus, probability)
3. Familiarize yourself with data visualization tools (Matplotlib, Seaborn)

Phase 2: Data Science Skills (6-9 months)

1. Master machine learning algorithms (scikit-learn, TensorFlow)
2. Learn data manipulation frameworks (Pandas, NumPy)
3. Study data visualization libraries (Plotly, Bokeh)
4. Understand database management systems (SQL, NoSQL)

Phase 3: Practice and Projects (3-6 months)

1. Work on personal projects (Kaggle competitions, datasets)
2. Participate in data science communities (GitHub, Reddit)
3. Build a portfolio showcasing skills

Phase 4: Job Preparation (1-3 months)

1. Update resume and online profiles (LinkedIn)
2. Practice whiteboarding and coding interviews
3. Prepare answers for common data science questions

Best Resources to learn Data Science 👇👇

Python Tutorial

Data Science Course by Kaggle

Machine Learning Course by Google

Best Data Science & Machine Learning Resources

Interview Process for Data Science Role at Amazon

Python Interview Resources

Join @free4unow_backup for more free courses

Like for more ❤️

ENJOY LEARNING👍👍

Skills for different sectors ✅

Python Operators ✅

Get all AI courses, tracks, certifications and projects for FREE this week 🚀

🔗 Registeration link👇 https://datacamp.pxf.io/6ygRrQ

Like for more ❤️

Master AI in 2025 – A Quick Roadmap 🚀

AI can be overwhelming, but following a structured path makes it easier. Here’s the roadmap:

1. Build Strong Foundations Learn Python, data structures, linear algebra, statistics & version control before diving into AI.

2. Work with Data Clean, preprocess & visualize datasets using Pandas, Seaborn, and Matplotlib for hands-on experience.

3. Master Machine Learning Understand supervised & unsupervised learning, regression, decision trees & implement models with Scikit-Learn.

4. Explore Deep Learning Learn neural networks, CNNs, RNNs, and Transformers using TensorFlow & PyTorch for AI applications.

5. Choose an AI Specialization Focus on NLP, computer vision, reinforcement learning, or AI in business and healthcare.

6. Learn Large Language Models (LLMs) Work with GPT, LLaMA, fine-tuning, Retrieval-Augmented Generation (RAG), and AI APIs.

7. Master AI Deployment & MLOps Deploy models using Flask, FastAPI, Docker, Kubernetes, and automate pipelines.

"I am an AI Tools & ChatGPT Expert, and my salary package is 42 LPA."

Sounds familiar? If you’ve been on YouTube recently, I’m sure you’ve seen this ad at least 100 times. Now, I have just one simple question – Can someone please tell me which companies are hiring for this role and paying 42 LPA? Because I’m also considering a career switch! 😂

See guys, learning how to use a few AI tools won't magically get you a 42 LPA job. Selling courses isn’t wrong, but selling them by giving false hopes is. Just because someone tells you that learning how to use a few AI tools will instantly land you a high-paying job doesn’t make it true.

So, a humble request – don’t fall for these unrealistic promises. Invest in courses only to upskill yourself, not with the expectation of overnight success.
If anyone actually finds this 42 LPA AI Tools & ChatGPT Expert job, please let me know. I’ll also update my resume! 🤣

Essential Data Analysis Techniques Every Analyst Should Know

1. Descriptive Statistics: Understanding measures of central tendency (mean, median, mode) and measures of spread (variance, standard deviation) to summarize data.

2. Data Cleaning: Techniques to handle missing values, outliers, and inconsistencies in data, ensuring that the data is accurate and reliable for analysis.

3. Exploratory Data Analysis (EDA): Using visualization tools like histograms, scatter plots, and box plots to uncover patterns, trends, and relationships in the data.

4. Hypothesis Testing: The process of making inferences about a population based on sample data, including understanding p-values, confidence intervals, and statistical significance.

5. Correlation and Regression Analysis: Techniques to measure the strength of relationships between variables and predict future outcomes based on existing data.

6. Time Series Analysis: Analyzing data collected over time to identify trends, seasonality, and cyclical patterns for forecasting purposes.

7. Clustering: Grouping similar data points together based on characteristics, useful in customer segmentation and market analysis.

8. Dimensionality Reduction: Techniques like PCA (Principal Component Analysis) to reduce the number of variables in a dataset while preserving as much information as possible.

9. ANOVA (Analysis of Variance): A statistical method used to compare the means of three or more samples, determining if at least one mean is different.

10. Machine Learning Integration: Applying machine learning algorithms to enhance data analysis, enabling predictions, and automation of tasks.

Like this post if you need more 👍❤️

Hope it helps :)

🚀 Here’s your step-by-step guide! From simple coding to hands-on projects and expert topics.

Machine Learning Cheatsheet 💪