AirBnB Challenge Project Time Series

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PhillyTalent Survey Part II | What most candidates' big challenges were in Data Science in 2019?

PhillyTalent Survey | What are the top channels for landing a job offer in Data Science in 2019?

PhillyTalent TechnologyStack Update | Adding Scala3

How to setup and login to your WorkSpace with PhillyTalent

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Hands on Machine Learning Event Oct 2019 | PhillyTalent - Raw Video

Teaser Oct29th PhillyTalent DS Portfolio Competition Review

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PhillyTalent Demo + Q&A

PhillyTalent Expert Kaggle presentation Teaser Video | Firas Baba July Cohort

Data Competitions: Hands On Machine Learning June 25th

SOFT - Vi Introduction: What Makes a Serious Developer?

SOFT - Building a RESTful APIs Django by Example Video

Kaggle Hands On Machine Learning with PhillyTalent May 2019

DT - How Does Multiple IF Conditions Work in Action?

DT - How Does Decision Trees Manage Linearly Non Separable Data

DT - Understand the Algorithm: Constructing a Decision Tree First Split

DT - w How Does DT Construct a Decision Tree 2nd Split

SOFT - Hands-On Dockerize a Simple Hello World Web Application

PhillyTalent: Today's Education System is Wrong about Jobs.

TF - How to Tune the Learning Rate in Parameter Hyperspace? (Advanced)

TF - What is Momentum and Learning Rate Decay in SGD Models? (Advanced)

TF - How to Perform a Proper Validation and Test Set Split?

TF - Data Leakage Image Classification (Advanced)

TF - Supervised Classification

TF - Why Need For Cross Entropy?

TF - Loss Evaluation & Minimizing Cross Entropy (Advanced)

TF - What Does Numberical Stability Mean? (Advanced)

TF - Why Need to Normalized Training Data and Initial Values? (Advanced)

TF - How Does One Hot Encoding Enable Us to Exactly Say Dog versus Cat?

TF How Does SoftMax Function Help Train Your Logistic Classifier

SVM - The Curse of Overfitting with Complex Models Such as SVMs

SVM - Overfitting SVM C Parameter with Train Data (Advanced)

SVM - Kernel, Gamma and C Value (Part II)

SVM - Hands-on with SVM Kernels for Your Data Competition (Part II)

SVM - So What is the Famous Kernel Trick?

SVM - More Practice On Making New Features Motivation Answer

SVM - More Practice On Making New Features Motivation Question

SVM - Separating with the New Feature: What are Linear Seperable Datasets?

SVM - Separating with the New Feature: What is Linearly Separable Dataset?

SVM - Visualizing the New Feature

SVM - Nonlinear Data Case

SVM - Building A New Feature for Nonlinear Data Example

SVM - The Case of Nonlinear Data

SVM - Coding Up the SVM Model with Two Lines of Code

SVM - How Does One Use Google to Start Coding Up an SVM?

SVM - SVM Decision Boundary Case Example

SVM - Starting with Google: SVM in SKlearn

SVM - SVM Outlier Case: Adding More Complexity to Solution

SVM - SVM Kernels: What's Tunning?

SVM - SVM Responsivenes to Outliers

SVM - SVM Solution to Outliers and Uncleaned data

SVM - The Solution to SVMs and Problematic Data Distributions

SVM - SVMs and Problematic Data Distributions

SVM - Question: About Margins

SVM - Robustness of a Solution from SVM: Practice with Margins Solution

SVM - How to Choose Between the Separating Lines Solutions