Syntax and Semantics of Python programming
Python History
Versions of Python

o Simple
o Open Source
o High Level Programming
o Portable
o Object and Procedure Oriented
o Easy to Maintain

o The Input() function
o The print() function
n use of % percent operator
n use of .format()

o What is a Variable
o Assign Values to variable
o Typecasting
o Data Types in Python
n Numeric
n String
n Boolean
n Compound
n List
n Tuple
n Set
n Frozen Set
n Dictionary

o Types of Operators
n Arithmetic Operators
n Comparison Operators
n Assignment Operators
n Logical Operators
n Bitwise Operators
n Identity Operators
n Membership Operators
n Operators Associativity
n Operator Precedence
n BEDMAS n Arithmetic Operators
n Comparison Operators
n Assignment Operators
n Logical Operators
n Bitwise Operators
n Identity Operators
n Membership Operators
n Operators Associativity
n Operator Precedence
n BEDMAS 

o The if Statement
o if – else statement
o The elif Statement
o Nested if-else ladder

o use of while loop
o use of for loop
o range() function
o arange() function
o The break Statement
o The continue Statement
o The pass statement

o Define a function
o calling a function
o Types of function
o UDF
o Function Arguments
o Functions Parameters
o Anonymous Function
o Global and Local Variable
o lambda
o map
o reduce
o filter
o Mathematical Function
o Trigonometric Function
o Random Function

o creating strings
o difference between “” & ‘ ‘
o creating multiline comment
o basic string operations
o creating slices in strings
o String Built-in Functions
n capitalize()
n upper()
n lower()
n isalnum()
n isalpha()
n isnumeric()
n isdecimal()
n islower()
n isupper()

o Access List items
o Change List items
o Add List Items
o Remove List Items
o Loop List
o List Comprehension
o Sort List
o Copy List
o Join List
o List built-in Method
n append()
n count()
n extend()
n reverse()
n sort()

o Access Tuples
o Update Tuples
o Unpack Tuples
o Loop Tuple
o Join Tuples
o Tuple built-in Methods
n index()
n count()

o Create Sets
o Access Set items
o Add Set items
o Remove Set items
o Loop Sets
o Join Sets

o Create Dictionaries
o Access Dictionary items
o Change Dictionary items
o Add Dictionary items
o Remove Dictionary items
o Loop Dictionaries
o Copy Dictionaries
o Nested Dictionaries
o Dictionary built-in Methods
n keys()
n values()
n items()
n get()

o Date Module
o Time Module
o os Module
o The import statement
o The from… import Statement

o Read files
o Write/ Create files
o Delete files
o Rename files

o Error in Python Program
o Syntax error
o Exception
o Types of Exception
o Handling Exception in Python
o Raising Exception
o User Defined Exception

o Match function
o Search function
o Matching VS Searching
o Modifiers
o Patterns

o Introduction
o Widgets
o Basic Widgets
o Top level Widgets
o Geometry Management
o Binding Functions
o Working with Images in Tkinter

o Class
o Object
o Inheritance
o Overloading
o Overriding

o Creating arrays
o Array indexing
o Array slicing
o Numpy Data Types
o Copy vs View
o Array Shape
o Array reshape
o Iterating
o Join
o Search
o Filter
o Split
o Sort

o Pandas Series
o Pandas DataFrame
o Read CSV
o Read JSON
o Cleaning Data
o Missing Value Handling
o Optimizing Data Format
o Redundancy Minimization
o The corr() function
o Plotting Graphs in pandas

● Text to Columns
● Concatenate
● Right with Concatenate
● Absolute Cell Reference
● Data Validation
● Time and Date Calculations
● Conditional Formatting
● Exploring Styles and clear formatting
● Using Conditional Formatting to hide details using the IF function
● pivot table
● pivot chart
● Slicers
● creating charts

● Introduction to Power BI
● Report Visualization and Properties
● Chart and Map Report Properties
● Hierarchies and Drilldown Reports
● Power Query & M Language
● DAX EXPRESSIONS – Level 1
● DAX EXPRESSIONS – Level 2
● Power BI Cloud Operations
● Improving Power BI Reports
● Insights and Subscriptions
● Power BI Integration Elements

o Introduction
o MySQL Data Types
o Creating Databases in MySQL
o Some Useful Operations on MySQL Databases
o Creating Tables in MySQL
o MYSQL Table Commands
o Using ALTER command in MySQL
o Using DESCRIBE in MySQL
o Using TRUNCATE in MySQL
o Using DROP in MySQL
o ALTER Command in MySQL
o Sample Queries in MySQL
o Constraints in MySQL
o Using INSERT command in MySQL
o Using UPDATE command in MySQL
o Using DELETE command in MySQL
o SELECT Queries in MySQL
o Using REPLACE command in MySQL
o JOINS in MYSQL
o RIGHT JOINS in MySQL
o LEFT JOINS in MySQL
o INNER JOINS in MySQL
o LEFT JOINS vs. RIGHT JOIN in MySQL
o Primary Keys in MySQL
o FOREIGN KEYS in MySQL

o Updates to Notebook Zip
o Jupyter Notebooks
o Use of Google Colab

o Introduction to Seaborn
o Distribution Plots
o Categorical Plots
o Matrix Plots
o Grids
o Regression Plots
o Style and Color

o Welcome to the Data Visualization Section
o Introduction to Matplotlib
o Matplotlib Part 1
o Matplotlib Part 2
o Matplotlib Part 3

Probability distribution
Normal distribution
Poisson’s distribution
Descriptive Statistics
Inferential Statistics
Bayes’ theorem
Central limit theorem
Hypothesis testing
One Sample T-Test
Anova and Chi-Square

o Link for ISLR
o Supervised Learning Overview
o Evaluating Performance – Classification
o Evaluating Performance – Regression Error
o Machine Learning with Python
o Bias-Variance Trade-Off
o Overfitting and Under-fitting
o Leave-p-out Cross-Validation(LpOCV)
o Leave-One-out Cross-Validation(LOOCV)
o K-fold Cross-Validation
o Max Voting
o Averaging
o Weighted Average
o Bootstrap Aggregation(Bagging)
o Boosting

o Optimization
o Maxima and Minima
o Steps to find Maxima and minima
o Saddle point
o Cost function (Loss Function)
o Optimization Strategies
o Learning Rate and Its Importance
o Optimization Strategies-Gradient Descent
o Step to Calculate Gradient Descents
o Identifying Gradient Descents performance
o Gradient Descents for Machine Learning
o Procedure for Batch Gradient Descent
o Procedure for Stochastic Gradient Descent
o Difference Between Batch and Stochastic Gradient Descent
o Disadvantage of Gradient Descent
o Momentum
o Nesterov Accelerated Gradient
o Adaptive Gradient Procedure-Adagrad
o Advantage and Disadvantage of Adagrad
o Root Mean Squared Propagation-RMSprop
o Adaptive Moment Estimation Procedure-ADAM

o Introduction
o Modelling Process
o Data Representation
o Feature Extraction
o Estimator API
o Conventions
o Linear Modeling
o Extended Linear Modeling
o Stochastic Gradient Descent
o Support Vector Machines
o Anomaly Detection
o K-Nearest Neighbors
o KNN Learning
o Classification with Naïve Bayes
o Decision Trees
o Randomized Decision Trees
o Boosting Methods
o Clustering Methods
o Clustering Method Evaluation
o Principal Component Analysis
o Dimensionality Reduction using PCA
o Algorithms
n Linear Regression
n Logistic Regression
n Support Vector Machine
n Naïve Bayes (Gaussian)
n SGD
n KNN
n Decision Tree
n Random Forest
n Gradient Boosting
n Xgboost
n K- Means Clustering
n Apriori

o Whats is Machine learning
o Machine Learning Basic
o Types of Learning
o Problem Types
o Challenges Motivating Deep Learning
o Deep Learning(DL)
o History of Deep Learning
o Applications of Deep learning
o Need for Deep Learning?
o Why Deep learning is called ‘Deep’?
o Misconceptions about Deep Learning
o Deep Learning Architecture
o What is Artificial Neural Networks?
o Perceptrons
o Simple Neuron /Node
o How does it work?
o Deep Learning Neural Network
o Gradient Descent
o Non-Linear Activation Function
o What if Linear Activation Function
o Deep Auto-encoders
o Drop out
o Improving DNN Performance
o Deep Learning Libraries

o TensorFlow Usage
o Companies Using TensorFlow
o TensorFlow in Real-Time Applications
o How to install TensorFlow.?
o Getting Started With TensorFlow
o Tensors
o Tensors Properties
o TensorFlow Data Types
o Tensor Operation – Common Operation
o Constants
o Variables
o Placeholders
o Session
o Interactive Sessions
o Loss Functions
o Optimizers
o Layers
o Benefits of Estimators
o Data Flow Graphs
o Computational Graph
o Symbols and Meanings
o Symbols and Meanings
o How TensorFlow Works?
o TensorBoard
o Convolutional Neural Networks
o CNN Layers
o Convolutional Neural Networks with AI
o Deep Convolutional Neural Networks
o Recurrent Neural Network
o RNN Architecture
o Long Short Term Memory
o Long Short Term Memory Architecture

o Keras
o Advantages of Keras
o What is Tensor?
o Why Keras?
o Salient Features of Keras
o Keras vs TensorFlow: How Do They Compare?
o How to install Keras?
o Pre-Processing
o Types of Pre-Processing
o Layers in Keras
o Activation Function
o Loss Function
o Metrics
o Composing Models In Keras
o Sequential Model
o Model with Functional
o Keras with GPUs
o Keras With Multiple GPUs

● Artificial Intelligence
● An Introduction to Artificial Intelligence
● History of Artificial Intelligence
● Future and Market Trends in Artificial Intelligence
● Intelligent Agents – Perceive-Reason-Act Loop
● Search and Symbolic Search
● Constraint-based Reasoning
● Simple Adversarial Search (Game-Playing)
● Neural Networks and Perceptrons
● Understanding Feedforward Networks
● Exploring Backpropagation

● Deep Networks/Deep Learning
● Knowledge-based Reasoning
● First-order Logic and Theorem
● Rules and Rule-based Reasoning
● Studying Blackboard Systems
● Structured Knowledge: Frames, Cyc, Conceptual Dependency
● Description Logic
● Reasoning with Uncertainty
● Probability & Certainty-Factors
● What are Bayesian Networks?
● Studying Neural Elements
● Convolutional Networks
● Recurrent Networks
● Long Short-Term Memory (LSTM) Networks

● Natural Language Processing
● Natural Language Processing in Python
● Natural Language Processing in R
● Studying Deep Learning
● Artificial Neural Networks
● ANN Intuition
● Plan of Attack
● Studying the Neuron
● The Activation Function
● Working of Neural Networks
● Exploring Gradient Descent
● Stochastic Gradient Descent
● Exploring Backpropagation

● Understanding Artificial Neural Network
● Building an ANN
● Building Problem Description
● Evaluation the ANN
● Improving the ANN
● Tuning the ANN
● Conventional Neural Networks
● CNN Intuition
● Convolution Operation
● ReLU Layer
● Pooling and Flattening
● Full Connection
● Softmax and Cross-Entropy
● Building a CNN
● Evaluating the CNN
● Improving the CNN
● Tuning the CNN

● Recurrent Neural Network
● RNN Intuition
● The Vanishing Gradient Problem
● LSTMs and LSTM Variations
● Practical Intuition
● Building an RNN
● Evaluating the RNN
● Improving the RNN
● Tuning the RNN

● Self-Organizing Maps
● K-Mean Clustering Technique
● SOMs Network Architecture
● Working of Self-Organizing Maps
● How Self Organizing Maps work
● Practical Implementation of SOMs

● Energy-Based Models (EBM)
● Restricted Boltzmann Machine
● Exploring Contrastive Divergence
● Deep Belief Networks
● Deep Boltzmann Machines

● AutoEncoders: An Overview
● AutoEncoders Intuition
● Plan of Attack
● Training an AutoEncoder
● Overcomplete hidden layers
● Sparse Autoencoders
● Denoising Autoencoders
● Contractive Autoencoders
● Stacked Autoencoders
● Deep Autoencoders

● Dimensionality Reduction
● Principal Component Analysis (PCA)
● PCA in Python
● Linear Discriminant Analysis (LDA)
● LDA in Python
● Kernel PCA
● Kernel PCA in Python

● K-Fold Cross Validation in Python
● Grid Search in Python
● XGBoost
● XGBoost in Python

● Snake Game
● Simple Calculator
● Typing Speed Test
● Memory Puzzle
● Password Generator
● Currency Converter
● Countdown Clock and Timer

● Iris Flowers Classification
● Cartoonify Image
● Loan Default Prediction
● Real Estate Price Prediction
● Stock Price Prediction
● Titanic Survival Classification
● Twitter Sentiment Analysis by tweepy

● Human Face Detection
● Image Classification with CIFAR-10
● Breast Cancer Classification
● Music Genre Classification
● Chatbot using Deep Learning
● Image Caption Generation
● Coloring Old B &W Images

● Fake News Detection
● Color Detection using openCV
● Gender and Age Detection (CNN)
● Uber Data Analysis
● Credit Card Fraud Detection
● Movie Recommender System

● Lane Line Detection Project Code
● Image Classification
● Blur the Face
● Create your own emoji with Python