#DataAnalytics #Python #SQL #RProgramming #DataScience #MachineLearning #DeepLearning #Statistics #DataVisualization #PowerBI #Tableau #LinearRegression #Probability #DataWrangling #Excel #AI #ArtificialIntelligence #BigData #DataAnalysis #NeuralNetworks #GAN #LearnDataScience #LLM #RAG #Mathematics #PythonProgramming  #Keras

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This book is for readers looking to learn new #machinelearning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different #algorithms create the models they do and the advantages and disadvantages of each one.

This book will be most helpful for those with practice in basic modeling. It does not review best practices—such as feature engineering or balancing response variables—or discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.

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https://t.me/CodeProgrammer ✅

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1. Master the fundamentals of Statistics

Understand probability, distributions, and hypothesis testing

Differentiate between descriptive vs inferential statistics

Learn various sampling techniques

2. Get hands-on with Python & SQL

Work with data structures, pandas, numpy, and matplotlib

Practice writing optimized SQL queries

Master joins, filters, groupings, and window functions

3. Build real-world projects

Construct end-to-end data pipelines

Develop predictive models with machine learning

Create business-focused dashboards

4. Practice case study interviews

Learn to break down ambiguous business problems

Ask clarifying questions to gather requirements

Think aloud and structure your answers logically

5. Mock interviews with feedback

Use platforms like Pramp or connect with peers

Record and review your answers for improvement

Gather feedback on your explanation and presence

6. Revise machine learning concepts

Understand supervised vs unsupervised learning

Grasp overfitting, underfitting, and bias-variance tradeoff

Know how to evaluate models (precision, recall, F1-score, AUC, etc.)

7. Brush up on system design (if applicable)

Learn how to design scalable data pipelines

Compare real-time vs batch processing

Familiarize with tools: Apache Spark, Kafka, Airflow

8. Strengthen storytelling with data

Apply the STAR method in behavioral questions

Simplify complex technical topics

Emphasize business impact and insight-driven decisions

9. Customize your resume and portfolio

Tailor your resume for each job role

Include links to projects or GitHub profiles

Match your skills to job descriptions

10. Stay consistent and track progress

Set clear weekly goals

Monitor covered topics and completed tasks

Reflect regularly and adapt your plan as needed

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