Repository to share progress of 100daysofmlcode challenge by Siraj Raval. I have taken up this challenge with a bit of a twist. Since i am fairly new to DS/ML and have dedicated more than 5+ hours in studying per day .So, rather than spending 1 hour per day learning ML i will use that 1 hour to implement the topics i have learned that day in Jupyter Notebook or doing something related to DS/ML (reading,watching conference videos,tutorials,answering quora questions etc).
Note: For people asking me about Datacamp courses on slack, you can have 2 months free access on Datacamp courses using your microsoft account. Sign-in your account and go to Visual Studio Benifits, select activate under datacamp and fill the form.
- Pick an industry: Housing
- Find a problem: Predict the final price of each home in test set.
- Locate a Dataset: Boston Housing
- Apply AI to Data: Linear Regression in sklearn
- Create a Solution: Solution
- Pick an industry: Shipbuilding
- Find a problem: Predict survival of passenger(s)
- Locate a Dataset: Titanic
- Apply AI to Data: Logistic Regression in sklearn
- Create a Solution: Solution
- Pick an industry: Housing
- Find a problem: Predict the final price of each home in test set.
- Locate a Dataset: Boston Housing
- Apply AI to Data: Linear Regression in Tensorflow
- Create a Solution: Solution
- Pick an industry: Health Care
- Find a problem: Predict breast cancer class.
- Locate a Dataset: Wisconsin Breast Cancer
- Apply AI to Data: KNN in sklearn
- Create a Solution: Solution
No code today. Binge watch 3Blue1Brown complete neural netweork playlist.
- Pick an industry: Banking
- Find a problem: Analyze Lending Club's issued loans.
- Locate a Dataset: Cleaned and Reduced Loan Data
- Apply AI to Data: Decision Tree and Random Forests in sklearn
- Create a Solution: Solution
- Pick an industry: Agriculture
- Find a problem: Predict type of iris plant.
- Locate a Dataset: IRIS Seaborn
- Apply AI to Data: SVM in sklearn
- Create a Solution: Solution
Started learning R. Completed course Introduction to R at DataCamp.
Completed chapter 1 exercises('Introduction to Data') in Datacamp Data Analysis and Statistical Inference(FREE) based on the book i am reading OpenIntro Statistics(FREE).
Completed chapter 3 exercises('Foundations for inference: Sampling distributions') and ('Foundations for inference: confidence intervals') in Datacamp Data Analysis and Statistical Inference(FREE) based on the book i am reading OpenIntro Statistics(FREE).
- Pick an industry: Medicine
- Find a problem: Predict diabetes of patients.
- Locate a Dataset: Diabetes
- Apply AI to Data: Logistic Regression in sklearn
- Create a Solution: Solution
Ditched R, I hate it's syntax and simplicity 😜. Completed module 1 and 2 of course Deep Learning Fundamentals at Cognitive Class
Completed module 3 and 4 of course Deep Learning Fundamentals at Cognitive Class
Completed chapter 1 and 2 of Statistical Thinking in Python (Part 1 - Datacamp)
Completed Statistical Thinking in Python (Part 1 - Datacamp)
Completed chapter 1 of book Deep Learning by Ian Goodfellow(FREE)
Completed chapter 4 from the book i am reading OpenIntro Statistics(FREE).Taking some time off from algorithms.Learning the detailed math behind all the algorithms worked on above.
Completed chapter 2 from the book i am reading An Introduction to Statistical Learning with Applications in R by James, G.Pre-work for learning math behind ML Algorithms.
Completed chapter 3 from the book i am reading An Introduction to Statistical Learning with Applications in R by James, G.Pre-work for learning math behind ML Algorithms.
Understanding the math behind Linear Regression from:
- Chapter 7 of OpenIntro theory only.
- Linear Regression Algorithm in Python | Edureka with code and explaination.
Understanding the math behind Logistic Regression from:
- Chapter 8 of OpenIntro theory only.
- Logistic Regression - The Math of Intelligence (Week 2)| Siraj The Unicorn with code and explaination.
Understanding the math behind Naive Bayes from:
- Probability Theory - The Math of Intelligence #6 | Siraj The Unicorn with code and explaination.
- Naive Bayes Classifier in Python | Edureka with code and explaination.
Understanding the math behind K-Mean Clustering and Random Forests chapters 7 and 8:
- Learning Predictive Analytics with Python By Ashish Kumar PACKT with code and explaination.
Understanding the math behind SVM's chapter 9:
- Learning Predictive Analytics with Python By Ashish Kumar PACKT with code and explaination.
Started coursera course Introduction to Probability and Data by Duke University.
Completed coursera course Introduction to Probability and Data by Duke University.
Understanding how backpropogation algorithm works in calculating gradient of loss function with code and explaination.
Understanding Loss Functions and different Optimization Algorithms with code and explaination.
Watching videos i missed from Metis - Demystifying Data Science conference.
Watching videos i missed from Metis - Demystifying Data Science conference.
STILL WATCHING videos i missed from Metis - Demystifying Data Science conference.SO MANY! 😅😅😅
Just heard about a new course on DataCamp on Analyzing Police Activity with pandas. Will start on day 33 after revision of pre-requisites today.
Pre-Requisites:
Completed 2 of 3 pre-requisites for Analyzing Police Activity with pandas.
Completed 3 of 3 pre-requisites for Analyzing Police Activity with pandas.
Started Analyzing Police Activity with pandas.
Took a long break due to studies, less motivation and learning web development.
- Pick an industry: Economic Statistics
- Find a problem: Income Class Prediction using TensorFlow.
- Locate a Dataset: California Census Data
- Apply AI to Data: Artificial Neural Networks
- Create a Solution: Kaggle Notebook
Completed DataCamp course on Intro to SQL for Data Science as a pre-requisite for starting Joining Data in PostgreSQL.
Completed DataCamp course on Joining Data in PostgreSQL.
Follow along Taxi Trip Duration challenge on kaggle using XGBoost Taxi Trip Duration Kaggle Challenge (LIVE).
Completed Taxi Trip Duration challenge on kaggle using XGBoost Taxi Trip Duration Kaggle Challenge (LIVE).
Completed Sections 1-10 Advanced Machine Learning & Data Analysis Projects Bootcamp
Started following Become an AI and Machine Learning Specialist: Part I.
- Artificial Intelligence Foundations: Thinking Machines (COMPLETED ON DAY 46)
- Introducing core concepts of recommendation systems (COMPLETED ON DAY 47-50)
- The Essential Elements of Predictive Analytics and Data Mining (COMPLETED ON DAY 51-54)
- Machine Learning & AI Foundations: Value Estimations (COMPLETED ON DAY 55-57)
- Machine Learning & AI Foundations: Recommendations