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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Revolutionising Businesses through AI with Khalifeh Al Jadda

Nitika Sharma
Nitika Sharma Last Updated : 11 Jan, 2024
4 min read

Before “data science” glimmered, Khalifeh Al Jadda was wrangling web clusters in 2005. This interview unearths his journey from academia’s trenches to building cutting-edge AI at Google Ads and revolutionizing businesses through AI. Let’s get to know his innovative spirit, groundbreaking projects, and insights on the future of search and empowering small businesses. But this isn’t just Khalifeh’s story; it’s a treasure map for aspiring data pioneers. Buckle up for practical advice on navigating this ever-evolving field and discover in our recent Leading with Data session how one man’s passion revolutionizes business, one byte at a time.

You can listen to this episode of Leading with Data on popular platforms like Spotify, Google Podcasts, and Apple. Pick your favorite to enjoy the insightful content!

Key Insights from our Conversation with Khalifeh Al Jadda

  • The transition from keyword-based to conversational search unlocks new possibilities in user intent understanding.
  • Building a successful data science team involves balancing skill sets, continuous motivation through challenging problems, and coaching for growth.
  • Lifelong learning and adaptability are crucial in the rapidly evolving field of data science.
  • The future of search engines will likely involve a multi-search architecture to cater to different user queries.
  • Real-time personalized recommendations using deep learning require innovative solutions for real-time inference at scale.

Join our upcoming Leading with Data sessions for insightful discussions with AI and Data Science leaders!

Now, let’s look at Khalifeh Al Jadda’s responses to the questions asked in the Leading with Data.

How did your journey in data science begin?

My journey with data science started before it was even called data science; it was known as data mining in 2005. I was doing my master’s degree in computer science, and data mining was a hot research topic. Despite the lack of abundant data and sophisticated tools, the field was ripe with creativity and potential. My master’s thesis focused on enhancing the quality of web clusters using implicit user feedback.

What was your transition into the industry like after academia?

After completing my Ph.D., where I worked on glycol-informatics and big data to scale up machine learning techniques, I joined CareerBuilder. It was the right time and place, with the company investing in data science. I was among the first two data scientists on semantic search and recommendation engines. Later, I moved to Home Depot to build their recommendation engine and oversee online data science solutions. Each step was about challenging myself and stepping out of my comfort zone.

How would you describe your approach to data science?

I would describe myself as creative, innovative, and always challenging the status quo. I don’t accept existing solutions as the best or final answer. I’m excited to wake up daily to tackle new, challenging problems that haven’t been solved optimally. AI has unlocked many opportunities, and it’s about thinking outside the box to see how AI can improve people’s lives and help businesses grow.

Can you share some impactful projects from your career?

One project I’m proud of is the graph-based recommendation engine at CareerBuilder, which was one of the first of its kind. At Home Depot, we worked on real-time personalized recommendations using deep learning, which was challenging due to the need for real-time inference at scale. Another significant project was the vector search for Home Depot, which involved generating embeddings and matching them in milliseconds.

What is your view on the future of search engines?

The future of search is conversational. With LLMs (Large Language Models), we can now unlock conversational search, where the search engine can have a back-and-forth conversation with the user to understand their intent more accurately. This will enable use cases like project shopping, where customers can describe their project and receive a list of all the items they need. I believe we’ll see a multi-search architecture with different engines for different types of queries.

How did you transition from an individual contributor to leading teams?

Building a team from scratch at Home Depot was about identifying the data types we worked with and finding people with the right skill sets. Keeping the team motivated involved challenging them with interesting problems and educating business partners on what data science can achieve. Coaching and helping team members become leaders was also a key part of the process.

What are you currently working on at Google?

At Google Ads, my team focuses on helping small and midsize businesses globally maximize their return on investment from Google Ads. We build models to identify customers with the highest potential to benefit from Google Ads and suggest the right products or offerings. In a way, we are revolutionizing businesses through AI and upcoming technologies. 

What advice would you give someone starting their career in data science?

Start as a generalist to gain experience across the data science spectrum before specializing. Keep learning continuously, as data science is a lifelong learning journey. Don’t get attached to a specific programming language as the industry evolves. Network with influencers and thought leaders, and dedicate daily time to stay updated on industry developments.

Summing Up

This interview takes you through the complex fabric of data science, following the incredible journey of an experienced professional who saw it grow from the nascent stages of data mining to the cutting edge of artificial intelligence-powered search engines and is now revolutionizing businesses through AI. Khalifeh provides insight into creating teams, inspiring them, and overcoming the difficulties of data science leadership as they go from being an individual contributor to a team leader. The expert’s current focus at Google Ads is on using cutting-edge models to enable businesses worldwide.

Stay tuned with us on Leading with Data for more engaging AI, data science, and Gen AI sessions.

Check out our upcoming sessions here.

Nitika Sharma
Nitika Sharma

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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