A Series of 6-weeks in-depth machine learning workshops!

Kick-start your Data Science journey by this 6-weeks 6-weekends long series of Machine Learning for Data Science workshops specifically designed to teach the foundation concepts behind the buzz that is Data Science today.

learn by doing

Hands-on in-lecture coding tasks and assignments developed, in the most in-demand programming language Python, categorically to solidify the concepts learned.

constructive feedback from instructors

Get your assignments and class tasks evaluated by instructors along with the feedback to improve your Data Science skills.

Starting from 08th FEBruary

EVERY SATURDAY - 10 AM TO 4 PM

Pre-requisites

PROFICIENCY IN LOGIC PROGRAMMING

Should be proficient in logic building skills & computer programming constructs like Variables, Conditionals, Loops, etc.

SOUND KNOWLEDGE OF OOP & DATA STRUCTURES

Sound familiarity with Object Oriented Programming concepts and be able to correctly use Data Structures in code e.g. Lists, Arrays, Dictionaries, etc.

FUNDAMENTAL MATHEMATICAL PROFICIENCY

Familiarity with Linear Algebra, Probability & Statistics, & Calculus.
"A hands-on perspective without mathematics" is a dangerous myth!

Workshops outline

DS Weekends by Pakistan.AI - Machine Learning for Data Science

Week 1 - Intro To Python for Data Science

  • Defining Python for Data Science

  • Python Data Structures

  • Functions & Classes

  • NumPy Basics

  • Advanced NumPy Computations

  • Pandas Basics

  • Advanced Data Manipulation with Pandas

Assignment # 1

Week 2 - Bayesian classification methods

  • Reviewing Prior & Posterior Probabilities

  • Naive Bayes Classification

  • Full Bayes Classification

  • Text Data Preprocessing - BoW, N-grams, Word Embeddings, Stop Words Removal

  • Splitting Data Into Train, Validation & Test Data Sets

  • Cross validation & Hyper Parameter Tuning for N - grams

  • Precision, Recall, Accuracy & F1 Score

Assignment # 2

Week 3 - K - Nearest NEIGHBOR classifier & Regressor

  • KNN Classifier

  • KNN Regressor

  • Distance Metrics For Numerical & Categorical Data

  • Feature Scaling

  • Effects of Distance Metrics on Decision Boundary

  • Concept of lazy learners in ML

  • Overfitting & Underfitting KNN Models

  • Cross validation & Hyper Parameter Tuning To Choose Optimal Value of K

  • Bias and Variance Trade-off

Assignment # 3

Week 4 - Decision Trees & Random forests

  • Measuring Entropy & Information Gain

  • Decision Tree Classifier For Numerical Data

  • Decision Tree Classifier For Categorical Data

  • Regression through "variance in reduction" in decision trees

  • Visualizing decision tree boundary

  • Pruning a tree

  • Decision Trees Overfitting & Underfitting

  • Cross validation & Hyper Parameter Tuning for Depth & Purity

  • Motivation for Random Forests Vs Decision Trees

  • Random Forest As Bagging Algorithm

  • Defining Random Patches

  • Random Forest Overfitting & Underfitting

  • Cross validation & Hyper Parameter Tuning for Number of Trees, Depth & Purity

Assignment # 4

week 5 - Perceptron, SVM & Logistic Regression

  • Perceptron as Linear Binary Classifier

  • Logistic Regression As Linear Binary Classifier

  • Cross Entropy Loss

  • SVM Motivation and Maximizing of Margin

  • Regularization Techniques To Avoid Over Fitting

Assignment # 5

Week 6 - Regression Models & project discussion

  • Classification VS Regression Models

  • Evaluation Metrics - Accuracy VS Loss Functions

  • Gradient Descent Algorithm

  • Linear Regression & Curve Fitting

  • Underfitting & Overfitting Regression Models

  • Cross validation & Hyper Parameter Tuning for Degree of Polynomial

  • Ridge & Lasso Regression (classical examples of regularized learning algorithms)

  • Feature Selection with Lasso Regression

  • Penalizing Weight To Counter Overfitting

Assignment # 6

Project Details

  • A note on ML best practices

  • Kaggle Project Discussion

  • Where To Go From Here!

Project

REGISTRATION process

  1. Register for DS-Weekends - Machine Learning for Data Science here.

  2. Keep an eye out for follow-up application process email from us. Complete the steps mentioned in that email.

  3. Wait for our selection confirmation email.

AISHA JAVED

Chapter Lead @
Pakistan.AI - Isb

AI Engineer @
DPS UnternehmerTUM GmbH

MUNEEBA SIRSHAR

Academic Trainer @ Pakistan.AI - Isb

D.S. Corporate Trainer @ AMZ Consulting Pty Ltd

ABEER NOOR

Academic Trainer @ Pakistan.AI - Isb

Software Engineer @ Emumba Pvt Ltd

SAAD ZIA

Academic Trainer @ Pakistan.AI - Isb

Research Scientist @ Automotive AI (GmbH)

Venue

frequently asked questions

Who should attend these workshops?

We encourage both professionals & students to apply who want to either kick-start their Data Science careers or compliment their existing roles with Data Science knowledge. The pre-requisites as mentioned above referring to Proficiency in Logic Programming, Sound Knowledge of OOP & Data Structures & Fundamental Mathematical Proficiency are bare minimum!

What are the timings and duration of the workshops?

The 6 weeks workshops will be held on Saturdays from 10 am to 4 pm starting from 08th February, 2020.

Will I receive a certificate after successfully completing this course?

Yes, on successful completion of this series of workshops, you will be given certificate of completion by Pakistan.AI