Header Fragment
Logo

A career growth machine

Home All Students
AI Resume & Interview
Intelligent AI Resume Builder Manage Jobs Manage your candidature for Job Match Resume against Job Setup Interview for a Job Generate Full Study Guide for a Job
Certifications Training
Books
All Books Books By Categories
Interview Plans Contact Us
FAQ
Recruiter FAQ Learner FAQ
  
× Login Plans Home All Students
AI Resume & Interview
Intelligent AI Resume Builder Manage Jobs Manage your candidature for Job Match Resume against Job Setup Interview for a Job Generate Full Study Guide for a Job
Certifications Training
Books
All Books Books By Categories
Interview Contact Us
FAQ
Recruiter FAQ Learner FAQ
$9.99 or 999 INR only

Don't Miss Out!

On QuickTechie, you can:

Don't leave yet — start building your career today on QuickTechie.com!

Unlimited Learning, One Price
$299 / INR 23,999

All Content for $99 / INR 7,999

Offer valid for the next 3 days.

Subscribe

Chapter 4: Supervised Learning-Structured Data-Binary classification
Chapter 5: Supervised Learning-Structured Data-Multi-class classification
Chapter 7: Supervised Learning-Unstructured Data-Image classification
Chapter 8: Supervised Learning-Unstructured Data-Segmentation
Chapter 13: machine learning lifecycle.-Feature engineering
Chapter 16: machine learning lifecycle.-Model monitoring and evaluation (e.g., model explainability, precision, recall, accuracy, confusion matrix)
Chapter 17: machine learning lifecycle.-Model versioning
Chapter 20: Z and T tests
Chapter 27: Build a data science pipeline.-Python User-Defined Functions (UDFs)
Chapter 28: Build a data science pipeline.-Python User-Defined Table Functions (UDTFs)
Chapter 29: Build a data science pipeline.-Python stored procedures
Chapter 30: Build a data science pipeline.-Integration with machine learning platforms (e.g., connectors, ML partners, etc.)
Chapter 31: Use Snowpark for Python and SQL-Aggregate
Chapter 34: Use Snowpark for Python and SQL-Remove duplicates
Chapter 38: Use Snowpark for Python and SQL-Sampling data
Chapter 40: Snowpark and SQL-Connect external machine learning platforms and/or notebooks (e.g., Jupyter)
Chapter 41: Use Snowflake native statistical functions to analyze and calculate descriptive data statistics.-Window Functions
Chapter 43: Use Snowflake native statistical functions to analyze and calculate descriptive data statistics.-VARIANCE
Chapter 44: Use Snowflake native statistical functions to analyze and calculate descriptive data statistics.-TOPn
Chapter 45: Use Snowflake native statistical functions to analyze and calculate descriptive data statistics.-Approximation/High Performing function
Chapter 47: Linear Regression-Verify the dependencies on dependent and independent variables
Chapter 52: Data Transformations-Derived features (e.g., average spend)
Chapter 53: Binarizing data-Binning continuous data into intervals
Chapter 55: Binarizing data-One hot encoding
Chapter 56: Statistical summaries-Snowsight with SQL
Chapter 57: Statistical summaries-Streamlit
Chapter 58: Statistical summaries-Interpret open-source graph libraries
Chapter 59: Statistical summaries-Identify data outliers
Chapter 60: Common types of visualization formats-Bar charts
Chapter 61: Common types of visualization formats-Scatterplots
Chapter 64: Connecting Python to Snowflake-Python connector with Pandas support
Chapter 65: Connecting Python to Snowflake-Spark connector
Chapter 66: Snowflake Best Practices-One platform, one copy of data, many workloads
Chapter 67: Snowflake Best Practices-Enrich datasets using the Snowflake Marketplace
Chapter 68: Snowflake Best Practices-External tables
Chapter 69: Snowflake Best Practices-External functions
Chapter 70: Snowflake Best Practices-Zero-copy cloning for training snapshots
Chapter 73: Optimization metric selection (e.g., log loss, AUC, RMSE)
Chapter 75: Partitioning-Train validation hold-out
Chapter 80: ROC curve/confusion matrix-Calculate the expected payout of the model
Chapter 82: Residuals plot-Interpret graphics with context
Chapter 85: Model Development-Interpret a model.-Partial dependence plots
Chapter 88: Model Use an external hosted model-Pre-built models
Chapter 89: Model Deploy a model in Snowflake-Vectorized/Scalar Python User Defined Functions (UDFs)
Chapter 90: Model Deploy a model in Snowflake-Pre-built models
Chapter 92: Model Deploy a model in Snowflake-Stage commands
Chapter 93: Model Metrics for model evaluation-Data drift /Model decay-Data distribution comparisons
Chapter 94: Model Do the data making predictions look similar to the training data?
Chapter 95: Model Do the same data points give the same predictions once a model is deployed?
Chapter 96: Model Area under the curve
Chapter 97: Model Accuracy, precision, recall
Chapter 98: Model User defined functions (UDFs)
Chapter 99: Streams and tasks
Chapter 100: Metadata tagging

Combo Packages at a Discount: Get one that best fits your learning needs.