Introduction To Python

• Why Python
• Application areas of python
• Python implementations
• Cpython
• Jython
• Ironpython
• 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, nonetype
• Str, list, tuple, range
• Dict, set, frozenset

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 frozenset

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 filehandling
• Understanding withblock

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, filedialogetc
• 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

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 numpy array
• Vector and matrix operations
• Array indexing and slicing

Pandas Package

Introduction to pandas

• Labeled and structured data
• Series and dataframe 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

Categorical Data Encoding

• Label Encoding
• One Hot Encoding
• Handling Date and Time

Data Visualization using matplotlib and seaborn packages

• Scatter plot, lineplot, bar plot
• 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

• Classification
• Regression

Unsupervised Learning

• clustering

KNN Classification

• Math behind KNN
• KNN implementation
• Understanding hyper parameters

Performance metrics

• Math behind KNN
• KNN implementation
• Understanding hyper parameters

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 squared error
• 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
• Tfidfvectorizer
• 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 open cv
• Building image data set
• Capturing video
• Face classification in video
• Creating model for gender prediction

Projects

• Two project using Python & ML