DATA SCIENCE & ML USING PYTHON free videos and free material uploaded by ducatittrainingschool staff .
Syllabus / What will i learn?
Introduction To Python
- Why Python
- Application areas of python
- Python implementations
- Cpython
- Jython
- Iron python
- Pypy
- Python versions
- Installing python
- Python interpreter architecture
- Python byte code compiler
- Python virtual machine(pvm)
Writing and Executing First Python Program
- Using interactive mode
- Using script mode
- General text editor and command window
- Idle editor and idle shell
- Understanding print() function
- How to compile python program explicitly
Python Language Fundamentals
- Character set
- Keywords
- Comments
- Variables
- Literals
- Operators
- Reading input from console
- Parsing string to int, float
Python Conditional Statements
- If statement
- If else statement
- If elif statement
- If elif else statement
- Nested if statement
Looping Statements
- While loop
- For loop
- Nested loops
- Pass, break and continue keywords
Standard Data Types
- Int, float, complex, bool, none type
- Str, list, tuple, range
- Dict, set, frozen set
String Handling
- What is string
- String representations
- Unicode string
- String functions, methods
- String indexing and slicing
- String formatting
Python List
- Creating and accessing lists
- Indexing and slicing lists
- List methods
- Nested lists
- List comprehension
Python Tuple
- Creating tuple
- Accessing tuple
- Immutability of tuple
Python Set
- How to create a set
- Iteration over sets
- Python set methods
- Python frozen set
Python Dictionary
- Creating a dictionary
- Dictionary methods
- Accessing values from dictionary
- Updating dictionary
- Iterating dictionary
- Dictionary comprehension
Python Functions
- Defining a function
- Calling a function
- Types of functions
- Function arguments
- Positional arguments, keyword arguments
- Default arguments, non-default arguments
- Arbitrary arguments, keyword arbitrary arguments
- Function return statement
- Nested function
- Function as argument
- Function as return statement
- Decorator function
- Closure
- Map(), filter(), reduce(), any()functions
- Anonymous or lambda function
Modules & Packages
- Why modules
- Script v/s module
- Importing module
- Standard v/s third party modules
- Why packages
- Understanding pip utility
File I/O
- Introduction to file handling
- File modes
- Functions and methods related to file handling
- Understanding with block
Object Oriented Programming
- Procedural v/s object oriented programming
- OOP principles
- Defining a class &object creation
- Object attributes
- Inheritance
- Encapsulation
- Polymorphism
Exception Handling
- Difference between syntax errors and exceptions
- Keywords used in exception handling
- try, except, finally, raise, assert
- Types of except blocks
Regular Expressions(Regex)
- Need of regular expressions
- Re module
- Functions /methods related to regex
- Meta characters &special sequences
GUI Programming
- Introduction to tkinter programming
- Tkinter widgets
- Tk, label, Entry, Textbox, Button
- Frame, messagebox, file dialog etc
- Layout managers
- Event handling
- Displaying image
Multi-Threading Programming
- Multi-processing v/s Multi-threading
- Need of threads
- Creating child threads
- Functions /methods related to threads
- Thread synchronization and locking
Introduction to Database
- Database Concepts
- What is Database Package?
- Understanding Data Storage
- Relational Database (RDBMS)Concept
SQL (Structured Query Language)
- SQL basics
- DML, DDL & DQL
- DDL: create, alter, drop
- SQL constraints:
- Not null, unique,
- Primary & foreign key, composite key
- Check, default
- DML: insert, update, delete and merge
- DQL : select
- Select distinct
- SQL where
- SQL operators
- SQL like
- SQL orderby
- SQL aliases
- SQL views
- SQL joins
- Inner join
- Left (outer) join
- Right (outer) join
- Full (outer) join
- Mysql functions
- String functions
- Char_length
- Concat
- Lower
- Reverse
- Upper
- Numeric functions
- Max, min, sum
- Avg, count, abs
- Date functions
- Curdate
- Curtime
- Now
Statistics, Probability &Analytics:
Introduction to Statistics
- Sample or population
- Measures of central tendency
- Arithmetic mean
- Harmonic mean
- Geometric mean
- Mode
- Quartile
First quartile- Second quartile(median)
- Third quartile
- Standard deviation
Probability Distributions
- Introduction to probability
- Conditional probability
- Normal distribution
- Uniform distribution
- Exponential distribution
- Right & left skewed distribution
- Random distribution
- Central limit theorem
Hypothesis Testing
- Normality test
- Mean test
- T-test
- Z-test
- ANOVA test
- Chi square test
- Correlation and covariance
Numpy Package
- Difference between list and numpyarray
- Vector and matrixoperations
- Array indexing and slicing
Panda Package
Introduction to pandas
- Labeled and structured data
- Series and data frame objects
How to load datasets
- From excel
- From csv
- From html table
Accessing data from Data Frame
- at &iat
- loc&iloc
- head() & tail()
Exploratory Data Analysis (EDA)
- describe()
- groupby()
- crosstab()
- boolean slicing /query()
Data Manipulation & Cleaning
- Map(), apply()
- Combining data frames
- Adding/removing rows &columns
- Sorting data
- Handling missing values
- Handling duplicacy
- Handling data error
Handling Date and Time
Data Visualization using matplotlib and seaborn packages
- Scatter plot, lineplot, barplot
- Histogram, pie chart,
- Jointplot, pairplot, heatmap
- Outlier detection using boxplot
Machine Learning:
Introduction To Machine Learning
- Traditional v/s Machine Learning Programming
- Real life examples based on ML
- Steps of ML Programming
- Data Preprocessing revised
- Terminology related to ML
Supervised Learning
Unsupervised Learning
Clustering
KNN Classification
- Math behind KNN
- KNN implementation
- Understanding hyperparameters
Performance metrics
- Math behind KNN
- KNN implementation
- Understanding hyperparameters
Regression
- Math behind regression
- Simple linear regression
- Multiple linear regression
- Polynomial regression
- Boston price prediction
- Cost or loss functions
- Mean absolute error
- Mean squared error
- Root mean squarederror
- Least square error
- Regularization
Logistic Regression for classification
- Theory of logistic regression
- Binary and multiclass classification
- Implementing titanic dataset
- Implementing iris dataset
- Sigmoid and softmax functions
Support Vector Machines
- Theory of SVM
- SVM Implementation
- kernel, gamma, alpha
Decision Tree Classification
- Theory of decision tree
- Node splitting
- Implementation with iris dataset
- Visualizing tree
Ensemble Learning
- Random forest
- Bagging and boosting
- Voting classifier
Model Selection Techniques
- Cross validation
- Grid and random search for hyper parameter tuning
Recommendation System
- Content based technique
- Collaborative filtering technique
- Evaluating similarity based on correlation
- Classification-based recommendations
Clustering
- K-means clustering
- Hierarchical clustering
- Elbow technique
- Silhouette coefficient
- Dendogram
Text Analysis
- Install nltk
- Tokenize words
- Tokenizing sentences
- Stop words customization
- Stemming and lemmatization
- Feature extraction
- Sentiment analysis
- Count vectorizer
- Tfidf vectorizer
- Naive bayes algorithms
Dimensionality Reduction
- Principal component analysis(pca)
Open CV
- Reading images
- Understanding gray scale image
- Resizing image
- Understanding haar classifiers
- Face, eyes classification
- How to use webcam in opencv
- Building image dataset
- Capturing video
- Face classification in video
- Creating model for gender prediction
Tableau
Tableau - Home
- Tableau -overview
- Tableau - environment setup
- Tableau - get started
- Tableau -navigation
- Tableau - design flow
- Tableau - filetypes
- Tableau - datatypes
- Tableau - show me
- Tableau - data terminology
Tableau - Data Sources
- Tableau - custom data view
- Tableau - data sources
- Tableau - extracting data
- Tableau - fields operations
- Tableau - editing metadata
- Tableau - data joining
- Tableau - data blending
Tableau – Work Sheet
- Tableau - add worksheets
- Tableau - rename worksheet
- Tableau - save &delete worksheet
- Tableau - reorder worksheet
- Tableau - paged workbook
Tableau – Calculation
- Tableau -operators
- Tableau -functions
- Tableau - numeric calculations
- Tableau - string calculations
- Tableau - date calculations
- Tableau - table calculations
- Tableau - lod expressions
Tableau – Sorting & Filter
- Tableau - basic sorting
- Tableau - basic filters
- Tableau - quick filters
- Tableau - context filters
- Tableau - condition filters
- Tableau - top filters
- Tableau - filter operations
Tableau - Charts
- Tableau - bar chart
- Tableau - line chart
- Tableau - pie chart
- Tableau -crosstab
- Tableau - scatterplot
- Tableau - bubble chart
- Tableau - bullet graph
- Tableau - boxplot
- Tableau - tree map
- Tableau - bump chart
- Tableau - gantt chart
- Tableau -histogram
- Tableau - motion charts
- Tableau - waterfall charts
- Tableau –dashboard
Projects
One project using python &sql- One project using python &ml
- One dashboard usingtableau
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