Series

Terraform
- Let's try: Terraform part 1 - basic
- Let's try: Terraform part 2 - variables
- Let's try: Terraform part 3 - backend
- Let's try: Terraform part 4 - modules
- Let's try: Terraform part 5 - import
- Let's try: Terraform part 6 - CI/CD
- Let's try: Terraform part 7 - Locals, Data, and Output
- Let's try: Terraform part 8 - Conditions and Repetition
Apache Beam
- Let's try: Apache Beam part 1 - simple batch
- Let's try: Apache Beam part 2 - draw the graph
- Let's try: Apache Beam part 3 - my own functions
- Let's try: Apache Beam part 4 - live on Google Dataflow
- Let's try: Apache Beam part 5 - transform it with Beam functions
- Let's try: Apache Beam part 6 - instant IO
- Let's try: Apache Beam part 7 - custom IO
- Let's try: Apache Beam part 8 - Tags & Side inputs

Apache Airflow
Data Science
- Note of data science training EP 1: Intro – unboxing
- Note of data science training EP 2: Pandas & Matplotlib – from a thousand mile above
- Note of data science training EP 3: Matplotlib & Seaborn – Luxury visualization
- Note of data science training EP 4: Scikit-learn & Linear Regression – Linear trending
- Note of data science training EP 5: Logistic Regression & Dummy Classifier – Divide and Predict
- Note of data science training EP 6: Decision Tree – At a point of distraction
- Note of data science training EP 7: Metrics – It is qualified
- Note of data science training EP 8: Ensemble – Avenger's ensemble
- Note of data science training EP 9: NetworkX – Map of Marauder in real world
- Note of data science training EP 10: Cluster – collecting and clustering
- Note of data science training EP 11: NLP & Spacy – Languages are borderless
- Note of data science training EP 12: skimage – Look out carefully
- Note of data science training EP 13: Regularization – make it regular with Regularization
- Note of data science training EP 14 END – Data scientists did their mistakes