Udacity

Goal - "learn Free Udacity courses"

Course - Intro to HTML and CSS

Day 1 (30/05/2018)

• Learned the basic HTML tags.
• completed the HTML tags and problem set.
• completed lesson 1 and lesson 2.

Day 2 (31/05/2018)

• Learned about CSS.
• completed lesson 3.

Day 3 (01/06/2018)

• completed the CSS problem set.
• HTML and CSS Course completed.

Course - Introduction to Python

Day 4 (02/06/2018)

• Started with Python introduction.
• learned basics of python.
  • operators
  • data types
  • string methods
  • sets
  • tuples
  • Dictonary
• completed lesson 1.

Day 5 (03/06/2018)

• learned coditional statements.
  • if
  • elif

Day 6 (04/06/2018)

• learned while loop.
• completed the practice quiz.

Day 7 (05/06/2018)

• learned continue,break usage.
• learned zip, enumerate functions.
• learned comprehensions.
• completed lesson 2.

Day 8 (06/06/2018)

• learned functions.
• learned variable scopes.
• learned lambda expressions.
• learned iterators and generators.
• completed lesson 3.

Day 9 (08/06/2018)

• Python development environment setup.
• learned to get input from user.
• did simple python codes.

Day 10 (09/06/2018)

• Completed few exercise in FCC.

Day 11 (10/06/2018)

• Learned exception handling.
• Learned to handle files.
• Learned to import modules.
• Learned to use python standard libraries.
• completed the python introduction course.

Course - Programming Foundations with Python

Day 12 (11/06/2018)

• Course Introduction.
• Completed Lesson 1 in new course.

Day 13 (12/06/2018)

• Created a simple program helps you to take a break from your computer screen by openinng your favourite youtube video.
• take_a_break

Day 14 (13/06/2018)

• created secret_message program.
• completed lesson 2.

Day 15 (14/06/2018)

• Created the drawRectangle programe.
• Completed lesson 3.
• Understand classes and objects in python.
• Created send_sms project using twilo.
• Completed lesson 4.

Day 16 (15/06/2018)

• Created the Profanity_checker programe.
• Completed lesson 5.
• Learned OOP concepts like class, object, constructor etc.
• Created movies website project.
• Completed lesson 6.

Day 17 (16/06/2018)

• Understandig class variables.
• Understanding inheritance.
• Learned method overriding.
• Completed lesson 7.
• Course completed.

Course CS101 - Intro to Computer Science

• Learned basic programming fundamentals like expressions, variables.
• Took the initial step to build a search engine.
• Completed lesson 1.

Day 18 (17/06/2018)

• Solved problems.
• Completed Lesson 2,3,4,5,6,7.

Day 19 (18/06/2018)

• Learning to approch a complex problem in python.

Day 20 (19/06/2018)

• Completed DaysBetweenDays problem using simple TDD approch.
• Completed lesson 8,9,10.

Day 21 (20/06/2018)

• Learned about lists and list functions.
• Completed the webcrawler.
• Completed lesson 11.

Day 22 (21/06/2018)

• Solved problem set.
• Completed lesson 12,13,14.
• Created new data structure for index.
• Completed the webcrawler with indexing.

Day 23 (22/06/2018)

• Learned about internet.
• Completed lesson 15.

Day 24 (23/06/2018)

• Solved problem sets.
• Completed lesson 16,17,18.
• Learned hash data structure.

Day 25 (24/06/2018)

• Learned more about hash tables.
• updated the code for hash table.

Day 26 (25/06/2018)

• Learning Dictionary.

Day 27 (26/06/2018)

• Learned ABBY OCR (out of scope 😛 )

Day 28 (27/06/2018)

• Updated the crawler with Dictionary data structure.
• Completed Lesson 18.
• Solving problem set.

Day 29 (28/06/2018)

• Completed UiPath Certification (out of scope 😛 )
• Understanding recursive function.

Day 30 (29/06/2018)

• Learned recursive function.
• Wrote palindromeand fibonacci series using recursive functions.

Day 31 (30/06/2018)

• Created fibonacci problem with loop.

Day 32 (01/07/2018)

• Completed lesson 22.
• Completed the Search Engine

Course CS215 - Intro to Algorithms

Day 33 (02/07/2018)

• Created naive, russian peasants algorithms.
• Learned to measure the execution time of a simple algorithm.
• Learned to calculate the correctness of an algorithm.

Day 34 (03/07/2018)

• Solving problem set.
• Created graph.

Course AI Programming with Python Nanodegree Program

Day 35 (04/07/2018)

• Re-collecting python and understanding the lab workspace.
• Started classifiying images lab.

Day 36 (05/07/2018)

• Setup working enviornment in PC for Image classifier project.
• Completed Task-1 in project.

Day 37 (06/07/2018)

• Completed Task-2 and Task-3 in project.

Day 38 (07/07/2018)

• Working on Task-4.

Day 39 (08/07/2018)

• Completed task-4.

Day 40 (10/07/2018)

• Completed Task - 5, Task - 6.

Day 41 (11/07/2018)

• Completed lab.

Day 42 (12/07/2018)

• Learned Conda commands
  • conda create
  • activate/deactivate
  • conda list
  • conda install
  • conda env export > environment.yaml
  • conda env create -f environment.yaml
  • conda env remove -n env_name
  • pip freeze > requirements.txt

Day 43 (13/07/2018)

• Learning Jupyter notebooks.

Day 44 (14/07/2018)

• Completed Jupyter notebooks.

Day 45 (15/07/2018)

• Started numpy.

Day 46 (16/07/2018)

• Learned to create ndArray.
• Learned usefull NumPy funnctions.
  • np.zeros()
  • np.ones()
  • np.full()
  • np.eye()
  • np.diag()
  • np.arange()
  • np.linspace()
  • np.reshape()
  • np.random.random()
  • np.random.randint()
  • np.random.normal()

Day 47 (17/07/2018)

• Learned array functions.
  • Accessing array elements.
  • Modify array values.
  • Delete array values.
  • Append array values.
  • Insert values to array.
  • vstack and hstack.

Day 48 (18/07/2018)

• Learned nd array slicinng.
  • nd array slicing using index.
  • np.copy()
  • np.diag()
  • np.unique()

Day 49 (06/08/2018)

• Learned boolean operations, set functions and sorting.

  • Boolean indexing (x[x>10])
  • Set funnctions
    • np.intersect1d(x, y)
    • np.setdiff1d(x, y)
    • np.union1d(x, y)
  • Sort Functions
    • np.sort(x)
    • np.sort(x,axis=0)
    • np.sort(x,axis=1)

• Learned Arithmetic operations and numpy broadcasting functions.

Day 50 (15/08/2018)

• Completed Numpy.
• Completed the miniproject in Numpy

Day 51 (16/08/2018)

• Learning Pandas.
• Learned Panda Series datatype.
• Learned pandas functions,
  • create Series pd.Series(data=[30, 6, 'Yes', 'No'], index=['eggs', 'apple', 'milk', 'bread']).
  • get shape groceries.shape.
  • get dimension groceries.ndim.
  • get size groceries.ndim.
  • get index groceries.index.
  • get values groceries.values.
  • check index in series 'banana' in groceries.
• Learned to access and modify the series elements,
  • By index label - groceries['bread'], groceries['bread','apple'].
  • By numerical indices - groceries[-1], groceries[[1, 0, -1]].
  • By loc() function - groceries.loc[['eggs','apples']].
  • By iloc() function - groceries.iloc[[1,-1]].
  • change values of the element - groceries['eggs'] = 2.
  • remove element from series - groceries.drop('apple',inplace=True).
• Arithmetic Operations on Pandas Series.

Day 52 (18/08/2018)

• Learned to create pandas data frames.

  • items = {
      'Bob': pd.Series([245, 25, 55],index=['bike', 'pants', 'watch']),
      'Alice': pd.Series([40, 110, 500, 45],index=['book', 'glassess', 'bike', 'pants'])
    }
    shopping_carts = pd.DataFrame(items)
    shopping_carts

  • get the index of data frame shopping_carts.index.
  • get the column names of data frame shopping_carts.columns.
  • get the values of data frame shopping_carts.values.
  • get the shape of data frame shopping_carts.shape.
  • get the dimension of the data frame shopping_carts.ndim.
  • get the size of data frame shopping_carts.size.
  • create filtered data frame alice_sel_shopping_cart = pd.DataFrame(items, index=['glassess', 'bike'], columns=['Alice'])alice_sel_shopping_cart.
  • create custom index stored_items = pd.DataFrame(items, index=['store 1', 'store 2']).

• Learned to accessing Elements in pandas data frames

  • access elements using index stored_items[['bikes']].
  • creating new columns with arithmetic operations stored_items['suits'] = stored_items['shirts'] + stored_items['pants'].
  • insert new column stored_items.insert(5, 'shoes',[8, 5, 0]).
  • remove columns using pop stored_items.pop('new_watches').
  • rename columns stored_items = stored_items.rename(columns={'bikes':'hats'}).
  • set index to data frame stored_items = stored_items.set_index('pants').

• Dealing with NaN values

  • get the count of NaN values store_items.interpolate(method='linear', axis=0).
  • remove NaN values store_items.dropna(axis=1) #inplace = true will effect the actual dataframe.
  • fill NaN values with a value store_items.fillna(0).
  • fill NaN values with forward and backward values (ffill, bfill) store_items.fillna(method='ffill', axis=0).
  • fill NaN values with interpolation store_items.interpolate(method='linear', axis=1).

• Loading Data into a Pandas DataFrame

  • Load data from csv pd.read_csv('./goog-1.csv').
  • list the top rows google_stock.head().
  • list the last lines google_stock.tail().
  • check for NaN values google_stock.isnull().any().
  • get the data description google_stock['Adj Close'].describe().
  • get the correlation of data google_stock.corr()
  • groupby to group the data data.groupby(['Year','Department'])['Salary'].sum().

Day 53 (19/08/2018)

• Completed pandas mini project.
• Started Matplotlib.
• Learned to create bar charts.

Day 54 (20/08/2018)

• Learned about absolute and relative frequecy.

Day 55 (21/08/2018)

• Learned to create pie charts.
• Learned to create histograms
• Learned to identify the outliers from histogram and how to identify the data innterval values.
• Learned to scales and transformations.

Day 56 (22/08/2018)

• Data Vizualization

  • for quantitative variable, the common plot type is histogram.
  • for qualitative variable, the common plot type is bar chart.
  • completed DV for univarient vizualization.

• Learned a glimpse of Kernel Density Estimation.

• Bivarient vizualization.

  • used to look at relationship.
  • 3 major bivarient plot.
    1. Scatter plots for quantitative variables vs. qualitative variables.
    2. Violin plots for quantitative variables vs. qualitative variables.
    3. Clustered bar charts for qualitative variables vs. quantitative variables.

• Scatter plot

  • Overplotting
    • where a plot is created with too many overlapping points.
    • resolve with
      • Sampling - ploting few numbers of data.
      • Transperecy - making point as transperent.
      • Jitter - adds a small amount of random noise to the position of each point.(used for discrete points).

• Heat maps or 2D histogram

  • for quantitative variables vs. quantitative variables.
  • good for discrete variable vs discrete variable.
  • good alternative to transperency for a lot of data.
  • bin size is important.

Day 57 (23/08/2018)

• Violin plots
  • Violin plots for quantitative variable vs qualitative variable.
• Learned about box plot.

Day 58 (25/08/2018)

• Clustered Bar charts
  • for qualitative variable vs quantitative variable.

Day 59 (26/08/2018)

• Faceting Plots
  • multiple copies of the same type of plot visualized on different subsets of the data.
• Line Plots
  • Ploting the relationship between two quantitative variable, one on each axis.
  • Emphasize relative change.
  • Emphasize trends across X-values.
• completed data vizualization.

Day 60 (28/08/2018)

• Linear algebra essential.
• Learned about vectors.
• Learned about vector addition and multiplication.

Day 61 (29/08/2018)

• Learned linear combinations.

Day 62 (30/08/2018)

• Learning matrix.