End To End Artificial Intelligence Course

In this Introduction, you will view the course syllabus to learn what will be taught in this course. You will hear from data science professionals to discover what data science is, what data scientists do, and what tools and algorithms data scientists use on a daily basis. Finally, you will find out why data science is considered the sexiest job in the 21st century.

• Introduction of python and comparison with other programming languages
• Installation of Anaconda Distribution and other python IDE
• Python Objects, Number & Booleans, Strings, Container objects, Mutability of objects
• Operators – Arithmetic, Bitwise, comparison and Assignment operators, Operators Precedence and
• Conditions(If else,if-elif-else)
• Loops(While ,for)
• Break and Continue statements
• Range functions

• String object basics
• String methods
• Splitting and Joining strings
• String format functions
• List object basics
• List methods
• List as Stack and Queues
• List comprehensions

• Tuples, Sets, Dictionary object basics, Dictionary
• Object Methods, Dictionary View Objects, Functions basics, Parameter passing, Iterators
• Generator Functions
• Lambda functions
• Map, Reduce, Filter functions

• Creating classes and Objects
• Inheritance, Multiple Inheritance
• Working with files
• Reading and writing files
• Buffered read and write
• Other File methods

• Using standard module
• Creating new modules
• Exceptions Handling with Try-except

• Number Guessing
• Hangman
• Python Story Generator
• Calculator
• Tic-Tac-Toe
• Plagiarism Checker

• Matplotlib
• Seaborn

• Account creation
• Pushing Projects
• Pulling Projects
• ReadME File

• Python Pandas – Series
• Python Pandas – DataFrame
• Python Pandas – Panel
• Python Pandas – Basic Functionality
• Descriptive Statistics
• Function Application
• Python Pandas – Reindexing
• Python Pandas – Iteration
• Python Pandas – Sorting
• Working with Text Data
• Options & Customization
• Indexing & Selecting Data
• Statistical Functions
• Python Pandas – Window Functions
• Python Pandas – Date Functionality
• Python Pandas – Timedelta
• Python Pandas – Categorical Data
• Python Pandas – Visualization
• Python Pandas – IO Tools

• NumPy – Ndarray Object
• NumPy – Data Types
• NumPy – Array Attributes
• NumPy – Array Creation Routines
• Array From Numerical Ranges
• NumPy – Indexing & Slicing
• NumPy – Advanced Indexing
• NumPy – Broadcasting
• NumPy – Iterating Over Array
• NumPy – Array Manipulation
• NumPy – Binary Operators
• NumPy – String Functions
• NumPy – Mathematical Functions
• NumPy – Arithmetic Operations
• NumPy – Statistical Functions
• Sort, Search & Counting Functions
• NumPy – Copies & Views
• NumPy – Matrix Library
• NumPy – Linear Algebra

• Flask
• Flask introduction
• Flask application
• Open link flask
• App routing flask
• URL building flask
• Http methods flask
• Templates flask
• Flask project: portfolio
• Postman with flask

• Descriptive and inferential Statistics
• Sampling Methods
• Types of Variables
• Independent and dependent variables
• Variable Measurement Scales
• Frequency Distribution and Cumulative Frequency Distribution
• Bar Graphs and Pie Charts
• Histograms and stem & leaf plots
• Arithmetic Mean for samples and populations
• Central Tendency
• Variance and Standard deviation for Population and sample
• Percentiles and Quartiles
• Inter Quartile Ranges and Box Plots
• Outliers in data 
• Skewness for the data
• The normal curves 
• Z-scores and z-test for the data

• Basics of probability
• Addition Rule
• Multiplication Rule
• Permutations
• Combination 
• Discrete and Continuous Random Variables
• Discrete probability distribution
• Probability Histogram
• Mean and Expected values of discrete random variables
• Variance and standard deviation of discrete random variables

• Binomial distribution
• Normal distribution
• Quadrants
• Pearsons correlation
• Hypothesis testing with Pearson’s r
• Spearman correlation 
• Central Limit theorem 
• Sample proportions
• Confidence intervals about the mean, population, standard deviation
• NULL and alternative Hypotheses
• Type I and Type II Errors
• One-Tailed and Two-Tailed Tests

• What is Data?
• Difference between CPU and GPU
• Parallel and sequence processors
• How data will be arranged in the axis
• Types of machine learnings
• What is classification?
• What is regression?
• What is clustering?
• Performance metrics
• What are errors?
• What are all the libraries in Machine Learning?
• Knowing about Tensorflow, Keras, Scikit-Learn, etc.
• Explorative Data Analysis
• Bias and variance

• Linear Regression Maths 
• Linear Regression building from scratch without libraries
• Linear Regression Building with Libraries (Scikit Learn)
• Maths for the Mean Squared Error, Squared Error, Absolute Squared Error.
• Writing Code from scratch for Mean Squared error 
• Writing Code from scratch for Squared error 
• Writing Code from scratch for absolute Squared error 
• Logistic Regression Maths 
• Logistic Regression building from scratch without libraries
• Logistic Regression Building with Libraries (Scikit Learn)
• Maths for the Accuracy, Precision, Recall, F1-Score
• Writing Code from scratch for Accuracy
• Writing Code from scratch for Precision 
• Writing Code from scratch for Recall
• Writing Code from scratch for F1-Score
• Writing code for all the metrics using sklearn (MSE, SE, Accuracy, Precision, etc..)

• Decision Tree Maths
  • Gini 
  • Entropy
• Building Decision Tree classifier using python
• Random Forest Maths
• Building Random Forest Maths
• KNN classifier
• KNN using python
• SVM with maths
• SVM building by using python 
• Voting classifier Maths
  • Harding Voting 
  • Soft Voting
• Building voting classifier with python
• Bagging classifier with maths
• Bagging classifier building with python
• Ridge Regression with maths
• Ridge Regression building with python
• Lasso Regression with maths
• Lasso Regression building with python
• SVR with maths
• SVR building with python
• Decision Tree Regressor with Maths
• Decision Tree with python 

• What is Perceptron?
• Neurons in humans and AI?
• What is a single layers perceptron?
• Neural Networks
• Hidden Layers
• Weights and bias
• Neural networks maths behind it
• Tensorflow and Keras introduction
• Building neural networks with TensorFlow
• Activation functions 
• Gradient descent algorithms
• Feedforward network
• Backpropagation
• Error and accuracy

• Stock Price Prediction using Machine Learning
• Housing Prices Prediction Project
• Wine Quality Test Project
• Mall Customers Clustering Analysis

• CNN introduction
• Convolutions introduction in humans and AI
• Padding in CNN
• Strides in CNN
• Max pooling in CNN
• Average pooling in CNN
• Kernels
• Features 
• Math behind CNN 
• Building CNN with TensorFlow and Keras
• Training and Testing it

• What is Corpus?
• What are Tokens?
•  What are Engrams?
• What is Tokenization?
  • What is White-space Tokenization?
  • What is Regular Expression Tokenization?
• What is Normalization?
  • What is Stemming?
  • What is Lemmatization?
• Part of Speech tags in NLP
• Building NLP model with SVM 
• Maths behind RNN and LSTM
• Working with RNN
• Working with LSTM

• Road Lane line detection – Computer Vision Project in Python
• text to speech and creating small chatbots
• Face Detection
• Flower Detection with pretrained models
• YOLO Object Detection

• Creating DB
• Creating tables
• Select 
• Where Clause
• Having Clause  
• Operators
• Aggregate functions
• Joints
  • Inner
  • Outer
  • Left 
  • Right

• Introduction
• Opencv
• Installations
• Reading and writing of image
• Reading and writing or video
• Drawing shapes in videos and images
• Writing time date and frames in video
• Contours
• Threshold
• Edges
• Harcascade files

• Face Detection
• Eye Detection
• Car Detection
• Bike Detection
• Face Recognition
• Face mask  detection