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Artificial Intelligence Engineer

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A career Path to be An Artificial Intelligence Engineer

A career Path to be a data scientist

To be An Machine Learning Engineer you should

Step 1: Kick start with Intro To AI

What is Al Impact | Future Trends | Applications
Begin the Al journey by learning the basics, applications and impact of Al in business.

A career Path to be a data scientist
A career Path to be a data scientist
Step 2: Then Mathematics

Statistics | Probability | Linear Algebra | Calculus
Understand and evaluate data by performing statistical analysis and data modeling

Step 3: Learn Programming

Python I Ri Java
Become proficient in Al by mastering programming skills

A career Path to be a data scientist
A career Path to be a data scientist
Step 4: Then Big Data

Hadoop | Spark | Cassandra | MongoDB
Learn the top Big Data tools to analyse the massive amount of data that you need to work with in Al.

Step 5: Learn Data Science

Acquisition Preparation | Data Analysis | Data Manipulation
Understand and implement Data Science techniques to draw meaningful insights from the data .

A career Path to be a data scientist
A career Path to be a data scientist
Step 6: Machine Learning

Scikit learn | Supervised learning | Unsupervised learning | Reinforcement learning
Build Al models using the latest Machine Learning algorithms to predict outcomes critical to business

Step 7: Then Deep learning

TensorFlow, Keras | Neural Networks | CNN, RNN, GAN, LSTMs
Master the design of models with unstructured data using Deep Learning techniques

A career Path to be a data scientist
A career Path to be a data scientist
Step 8: Learn Business Intelligence

Tableau | Qlikview | PowerBI
Get an in-depth understanding of the latest BI tools to present the analytics and insights gained from models created

A career Path to be a data scientist

You as an Artificial Intelligence Engineer

Why Artificial Intelligence ?

Why Data Science
Data Ingestion

Artificially intelligent systems deal with huge amounts of data and also stores multiple information about multiple entities from multiple sources. All of this appears on the system in a synchronous, or a simultaneous manner

Why Data Science
Futuristic

AI-enabled systems are designed to observe and react to their surroundings. They not only perceive the environment and take actions accordingly but also keep in mind the situations that might come up in the near future

Why Data Science
Quantum Computing

AI is helping solve complex quantum physics problems with the accuracy of supercomputers with the help of quantum neural networks. This can lead to path-breaking developments in the near future.

Who is This program for

  • Data Scientists,Statisticians,Business Analysts,Project Managers
  • Data Analysts and Functional Experts
  • Python developers who want to build real-world AI applications
  • Python beginners who want a comprehensive learning plan
  • Experienced programmers looking to use AI in their existing technology stacks
Who is this program

Syllabus

Best-in-class content by leading faculty and industry leaders in the form of videos,
cases and projects, assignments and live sessions.

01 : Return of Multi-Armed Bandit

02 : Higher Level Overviw of Reinforcement Learning

03 : Markov Decision Process

04 : Dynamic Programming

05 : Monte Carlo

06 : Temporal Difference Learning

07 : Approximation Methods

08 : Stock trading Project with Reinforcement Learning

01 : Starting with Python

  • Why Python Is Hot.
  • Choosing the Right Python.
  • Tools for Success.
  • An excellent, free learning environment.
  • Installing Anaconda and VS Code.
  • Writing Python in VS Code.
  • Choosing your Python interpreter
  • Writing some Python code.
  • Getting back to VS Code Python
  • Using Jupyter Notebook for Coding

02 : Interactive Mode, Getting Help, Writing Apps.

  • Using Python Interactive Mode.
  • Opening Terminal.
  • Getting your Python version .
  • Going into the Python Interpreter .
  • Entering commands.
  • Using Python’s built-in help.
  • Exiting interactive help.
  • Searching for specific help topics online.
  • Lots of free cheat sheets .
  • Creating a Python Development Workspace.
  • Creating a Folder for your Python Code .
  • Typing, Editing, and Debugging Python Code.
  • Writing Python code.
  • Saving your code.
  • Running Python in VS Code.
  • Simple debugging.
  • The VS Code Python debugger .
  • Writing Code in a Jupyter Notebook.
  • Creating a folder for Jupyter Notebook.
  • Creating and saving a Jupyter notebook.
  • Typing and running code in a notebook .
  • Adding some Markdown text.
  • Saving and opening notebooks.

03 : Python Elements and Syntax.

  • The Zen of Python.
  • Object-Oriented Programming.
  • Indentations Count, Big Time .
  • Using Python Modules.
  • Syntax for importing modules.
  • Using an alias with modules .

04 : Building Your First Python Application.

  • Open the Python App File .
  • Typing and Using Python Comments.
  • Understanding Python Data Types.
  • Numbers.
  • Words (strings).
  • True/false Booleans .
  • Doing Work with Python Operators.
  • Arithmetic operators
  • Comparison operators.
  • Boolean operators.
  • Creating and Using Variables.
  • Creating valid variable names.
  • Creating variables in code.
  • Manipulating variables.
  • Saving your work.
  • Running your Python app in VS Code.
  • What Syntax Is and Why It Matters.
  • Putting Code Together.

UNDERSTANDING PYTHON-BUILDING BLOCKS.

05 : Working with Numbers, Text, and Dates

  • Calculating Numbers with Functions.
  • Still More Math Functions .
  • Formatting Numbers .
  • Formatting with f-strings .
  • Showing dollar amounts.
  • Formatting percent numbers .
  • Making multiline format strings .
  • Formatting width and alignment.
  • Grappling with Weirder Numbers.
  • Binary, octal, and hexadecimal numbers.
  • Complex numbers.
  • Manipulating Strings.
  • Concatenating strings.
  • Getting the length of a string.
  • Working with common string operators.
  • Manipulating strings with methods .
  • Uncovering Dates and Times.
  • Working with dates.
  • Working with times.
  • Calculating timespans.
  • Accounting for Time Zones .
  • Working with Time Zones.

06 : Controlling the Action

  • Main Operators for Controlling the Action .
  • Making Decisions with if.
  • Adding else to your if login.
  • Handling multiple else’s with elif.
  • Ternary operations.
  • Repeating a Process with for.
  • Looping through numbers in a range .
  • Looping through a string.
  • Looping through a list.
  • Bailing out of a loop .
  • Looping with continue
  • Nesting loops.
  • Looping with while .
  • Starting while loops over with continue.
  • Breaking while loops with break.

07 : Speeding Along with Lists and Tuples.

  • Defining and Using Lists.
  • Referencing list items by position.
  • Looping through a list
  • Seeing whether a list contains an item. . . .
  • Getting the length of a list.
  • Adding an item to the end of a list.
  • Inserting an item into a list .
  • Changing an item in a list.
  • Combining list
  • Removing Removing list items.
  • Clearing out a list.
  • Counting how many times an item appears in a list .
  • Finding an list item’s index.
  • Alphabetizing and sorting lists. . . . . .
  • Reversing a list.
  • Copying a list .
  • What’s a Tuple and Who Cares? .
  • Working with Sets.

08 : Cruising Massive Data with Dictionaries

  • Creating a Data Dictionary.
  • Accessing dictionary data.
  • Getting the length of a dictionary.
  • Seeing whether a key exists in a dictionary
  • Getting dictionary data with get().
  • Changing the value of a key.
  • Adding or changing dictionary data.
  • Looping through a Dictionary.
  • Data Dictionary Methods.
  • Copying a Dictionary.
  • Deleting Dictionary Items.
  • Using pop() with Data Dictionaries.
  • Fun with Multi-Key Dictionaries.
  • Using the mysterious fromkeys and setdefault methods.
  • Nesting Dictionaries

09 : Wrangling Bigger Chunks of Code

  • Creating a Function.
  • Commenting a Function.
  • Passing Information to a Function.
  • Defining optional parameters with defaults.
  • Passing multiple values to a function.
  • Using keyword arguments (kwargs).
  • Passing multiple values in a list.
  • Passing in an arbitrary number of arguments .
  • Returning Values from Functions.
  • Unmasking Anonymous Functions.

10 : Doing Python with Class

  • Mastering Classes and Objects.
  • Creating a Class.
  • How a Class Creates an Instance .
  • Giving an Object Its Attributes.
  • Creating an instance from a class.
  • Changing the value of an attribute.
  • Defining attributes with default values .
  • Giving a Class Methods.
  • Passing parameters to methods.
  • Calling a class method by class name .
  • Using class variables.
  • Using class methods.
  • Using static methods .
  • Understanding Class Inheritance
  • Creating the base (main) class.
  • Defining a subclass.
  • Overriding a default value from a subclass.
  • Adding extra parameters from a subclass.
  • Calling a base class method.
  • Using the same name twice.

11 : Sidestepping Errors

  • Understanding Exceptions
  • Handling Errors Gracefully.
  • Being Specific about Exceptions.
  • Keeping Your App from Crashing.
  • Adding an else to the Mix.
  • Using try . . . except . . . else . . . finally.
  • Raising Your Own Errors

WORKING WITH PYTHON LIBRARIES

12 : Working with External Files

  • Understanding Text and Binary Files.
  • Opening and Closing Files.
  • Reading a File’s Contents.
  • Looping through a File.
  • Looping with readlines().
  • Looping with readline().
  • Appending versus overwriting files.
  • Using tell() to determine the pointer location.
  • Moving the pointer with seek().
  • Reading and Copying a Binary File.
  • Conquering CSV Files .
  • Opening a CSV file.
  • Converting strings.
  • Converting to integers .
  • Converting to date.
  • Converting to Boolean
  • Converting to floats.
  • From CSV to Objects and Dictionaries.
  • Importing CSV to Python objects.
  • Importing CSV to Python dictionaries.

13 : Juggling JSON Data

  • Organizing JSON Data.
  • Understanding Serialization .
  • Loading Data from JSON Files.
  • Converting an Excel date to a JSON date.
  • Looping through a keyed JSON file.
  • Converting firebase timestamps to Python dates .
  • Loading unkeyed JSON from a Python string .
  • Loading keyed JSON from a Python string.
  • Changing JSON data .
  • Removing data from a dictionary.
  • Dumping Python Data to JSON.

14 : Interacting with the Internet.

  • How the Web Works.
  • Understanding the mysterious URL.
  • Exposing the HTTP headers.
  • Opening a URL from Python .
  • Posting to the Web with Python.
  • Scraping the Web with Python .
  • Parsing part of a page.
  • Storing the parsed content
  • Saving scraped data to a JSON file .
  • Saving scraped data to a CSV file .

15 : Libraries, Packages, and Modules

  • Understanding the Python Standard Library .
  • Using the dir() function.
  • Using the help() function .
  • Exploring built-in functions .
  • Exploring Python Packages .
  • Importing Python Modules .
  • Making Your Own Modules.

01 : Vectors, Matrices, and Arrays

  • Introduction
  • Creating a Vector
  • Creating a Matrix
  • Creating a Sparse Matrix
  • Selecting Elements
  • Describing a Matrix
  • Applying Operations to Elements
  • Finding the Maximum and Minimum Values
  • Calculating the Average, Variance, and Standard Deviation
  • Reshaping Arrays
  • Transposing a Vector or Matrix
  • Flattening a Matrix
  • Finding the Rank of a Matrix
  • Calculating the Determinant
  • Getting the Diagonal of a Matrix
  • Calculating the Trace of a Matrix
  • Finding Eigenvalues and Eigenvectors
  • Calculating Dot Products
  • Adding and Subtracting Matrices
  • Multiplying Matrices
  • Inverting a Matrix
  • Generating Random Values

02 : Loading Data

  • Introduction
  • Loading a Sample Dataset
  • Creating a Simulated Dataset
  • Loading a CSV File
  • Loading an Excel File
  • Loading a JSON File
  • Querying a SQL Database

03 : Data Wrangling

  • Introduction
  • Creating a Data Frame
  • Describing the Data
  • Navigating DataFrames
  • Selecting Rows Based on Conditionals
  • Replacing Values
  • Renaming Columns
  • Finding the Minimum, Maximum, Sum, Average, and Count
  • Finding Unique Values
  • Handling Missing Values
  • Deleting a Column
  • Deleting a Row
  • Dropping Duplicate Rows
  • Grouping Rows by Values
  • Grouping Rows by Time
  • Looping Over a Column
  • Applying a Function Over All Elements in a Column
  • Applying a Function to Groups
  • Concatenating DataFrames
  • Merging DataFrames

04 : Handling Numerical Data

  • Introduction
  • Rescaling a Feature
  • Standardizing a Feature
  • Normalizing Observations
  • Generating Polynomial and Interaction Features
  • Transforming Features
  • Detecting Outliers
  • Handling Outliers
  • Discretizating Features
  • Grouping Observations Using Clustering
  • Deleting Observations with Missing Values
  • Imputing Missing Values

05 : Handling Categorical Data

  • Introduction
  • Encoding Nominal Categorical Features
  • Encoding Ordinal Categorical Features
  • Encoding Dictionaries of Features
  • Imputing Missing Class Values
  • Handling Imbalanced Classes

06 : Handling Text

  • Introduction
  • Cleaning Text
  • Parsing and Cleaning HTML
  • Removing Punctuation
  • Tokenizing Text
  • Removing Stop Words
  • Stemming Words
  • Tagging Parts of Speech
  • Encoding Text as a Bag of Words
  • Weighting Word Importance

07 : Handling Dates and Times

  • Introduction
  • Converting Strings to Dates
  • Handling Time Zones
  • Selecting Dates and Times
  • Breaking Up Date Data into Multiple Features
  • Calculating the Difference Between Dates
  • Encoding Days of the Week
  • Creating a Lagged Feature
  • Using Rolling Time Windows
  • Handling Missing Data in Time Series

08 : Handling Images

  • Introduction
  • Loading Images
  • Saving Images
  • Resizing Images
  • Cropping Images
  • Blurring Images
  • Sharpening Images
  • Enhancing Contrast
  • Isolating Colors
  • Binarizing Images
  • Removing Backgrounds
  • Detecting Edges
  • Detecting Corners
  • Creating Features for Machine Learning
  • Encoding Mean Color as a Feature
  • Encoding Color Histograms as Features

09 : Dimensionality Reduction Using Feature Extraction

  • Introduction
  • Reducing Features Using Principal Components
  • Reducing Features When Data Is Linearly Inseparable
  • Reducing Features by Maximizing Class Separability
  • Reducing Features Using Matrix Factorization
  • Reducing Features on Sparse Data

10 : Dimensionality Reduction Using Feature Selection

  • Introduction
  • Thresholding Numerical Feature Variance
  • Thresholding Binary Feature Variance
  • Handling Highly Correlated Features
  • Removing Irrelevant Features for Classification
  • Recursively Eliminating Features

11 : Model Evaluation

  • Introduction
  • Cross-Validating Models
  • Creating a Baseline Regression Model
  • Creating a Baseline Classification Model
  • Evaluating Binary Classifier Predictions
  • Evaluating Binary Classifier Thresholds
  • Evaluating Multiclass Classifier Predictions
  • Visualizing a Classifier’s Performance
  • Evaluating Regression Models
  • Evaluating Clustering Models
  • Creating a Custom Evaluation Metric
  • Visualizing the Effect of Training Set Size
  • Creating a Text Report of Evaluation Metrics
  • Visualizing the Effect of Hyperparameter Values

12 : Model Selection

  • Introduction
  • Selecting Best Models Using Exhaustive Search
  • Selecting Best Models Using Randomized Search
  • Selecting Best Models from Multiple Learning Algorithms
  • Selecting Best Models When Preprocessing
  • Speeding Up Model Selection with Parallelization
  • Speeding Up Model Selection Using Algorithm-Specific Methods
  • Evaluating Performance After Model Selection

13 : Linear Regression

  • Introduction
  • Fitting a Line
  • Handling Interactive Effects
  • Fitting a Nonlinear Relationship
  • Reducing Variance with Regularization
  • Reducing Features with Lasso Regression

14 :Trees and Forests

  • Introduction
  • Training a Decision Tree Classifier
  • Training a Decision Tree Regressor
  • Visualizing a Decision Tree Model
  • Training a Random Forest Classifier
  • Training a Random Forest Regressor
  • Identifying Important Features in Random Forests
  • Selecting Important Features in Random Forests
  • Handling Imbalanced Classes
  • Controlling Tree Size
  • Improving Performance Through Boosting
  • Evaluating Random Forests with Out-of-Bag Errors

15 : K-Nearest Neighbors.

  • Introduction
  • Finding an Observation’s Nearest Neighbors
  • Creating a K-Nearest Neighbor Classifier
  • Identifying the Best Neighborhood Size
  • Creating a Radius-Based Nearest Neighbor Classifier

16 : Logistic Regression

  • Introduction
  • Training a Binary Classifier
  • Training a Multiclass Classifier
  • Reducing Variance Through Regularization
  • Training a Classifier on Very Large Data
  • Handling Imbalanced Classes

17 : Support Vector Machines

  • Introduction
  • Training a Linear Classifier
  • Handling Linearly Inseparable Classes Using Kernels
  • Creating Predicted Probabilities
  • Identifying Support Vectors
  • Handling Imbalanced Classes

18 : Naive Bayes

  • Introduction
  • Training a Classifier for Continuous Features
  • Training a Classifier for Discrete and Count Features
  • Training a Naive Bayes Classifier for Binary Features
  • Calibrating Predicted Probabilities

19 : Clustering

  • Introduction
  • Clustering Using K-Means
  • Speeding Up K-Means Clustering
  • Clustering Using Meanshift
  • Clustering Using DBSCAN
  • Clustering Using Hierarchical Merging

20 : Neural Networks

  • Introduction
  • Preprocessing Data for Neural Networks
  • Designing a Neural Network
  • Training a Binary Classifier
  • Training a Multiclass Classifier
  • Training a Regressor
  • Making Predictions
  • Visualize Training History
  • Reducing Overfitting with Weight Regularization
  • Reducing Overfitting with Early Stopping
  • Reducing Overfitting with Dropout
  • Saving Model Training Progress
  • k-Fold Cross-Validating Neural Networks
  • Tuning Neural Networks
  • Visualizing Neural Networks
  • Classifying Images
  • Improving Performance with Image Augmentation
  • 20.17 Classifying Text

21 : Saving and Loading Trained Models

  • Introduction
  • Saving and Loading a scikit-learn Model
  • Saving and Loading a Keras Model

01 : An Introduction to Data Analysis

Data Analysis

  • Knowledge Domains of the Data Analyst
  • Computer Science
  • Mathematics and Statistics
  • Machine Learning and Artificial Intelligence
  • Professional Fields of Application

02 : Understanding the Nature of the Data

  • When the Data Become Information
  • When the Information Becomes Knowledge
  • Types of Data

03 : The Data Analysis Process

  • Problem Definition
  • Data Extraction
  • Data Preparation
  • Data Exploration/Visualization
  • Predictive Modeling
  • Model Validation
  • Deployment

04 : Quantitative and Qualitative Data Analysis

  • Open Data
  • Python and Data Analysis

05 : The NumPy Library

  • NumPy: A Little History
  • The NumPy Installation
  • Ndarray: The Heart of the Library
  • Create an Array
  • Types of Data
  • The dtype Option
  • Intrinsic Creation of an Array

06 : Basic Operations

  • Arithmetic Operators
  • The Matrix Product
  • Increment and Decrement Operators
  • Universal Functions (ufunc)
  • Aggregate Functions

07 : Indexing, Slicing, and Iterating

  • Indexing
  • Slicing
  • Iterating an Array

Conditions and Boolean Arrays

Shape Manipulation

08 : Array Manipulation

  • Joining Arrays
  • Splitting Arrays

09 : General Concepts

  • Vectorization
  • Broadcasting

Structured Arrays

10 : Reading and Writing Array Data on Files

  • Loading and Saving Data in Binary Files
  • Reading File with Tabular Data

11: pandas: The Python Data Analysis Library

The pandas Library—An Introduction

  • Understanding Exceptions
  • Installation from Anaconda
  • Installation from PyPI
  • Installation on Linux
  • Installation from Source
  • A Module Repository for Windows

Test Your pandas Installation

Getting Started with pandas

WORKING WITH PYTHON LIBRARIES

12 : Introduction to pandas Data Structures

  • The Series
  • The DataFrame
  • The Index Objects

13 : Other Functionalities on Indexes

  • Reindexing
  • Dropping
  • Arithmetic and Data Alignment

14 : Operations between Data Structures

  • Flexible Arithmetic Methods
  • Operations between DataFrame and Series

15 : Function Application and Mapping

  • Functions by Element
  • Functions by Row or Column
  • Statistics Functions

Sorting and Ranking

Correlation and Covariance

16 : “Not a Number” Data

  • Assigning a NaN Value
  • Filtering Out NaN Values
  • Filling in NaN Occurrences

17 : Hierarchical Indexing and Leveling

  • Reordering and Sorting Levels
  • Summary Statistic by Level

Pandas: Reading and Writing Data

I/O API Tools

CSV and Textual Files

18 : Reading Data in CSV or Text Files

  • Using RegExp for Parsing TXT Files
  • Reading TXT Files into Parts or Partially
  • Writing Data in CSV

19 : Reading and Writing HTML Files

  • Writing Data in HTML
  • Reading Data from an HTML File

Reading Data from XML

20 : Reading and Writing Data on Microsoft Excel Files

  • JSON Data
  • The Format HDF5

21 : Pickle—Python Object Serialization

  • Serialize a Python Object with cPickle
  • Pickling with pandas

22 : Interacting with Databases

  • Loading and Writing Data with SQLite
  • Loading and Writing Data with PostgreSQL

Reading and Writing Data with a NoSQL Database: MongoDB

pandas in Depth: Data Manipulation

23 : Data Preparation

  • Merging

24 : Concatenating

  • Combining
  • Pivoting
  • Removing

25 : Data Transformation

  • Removing Duplicates
  • Mapping

26 : Discretization and Binning

  • Detecting and Filtering Outliers

Permutation

27 : String Manipulation

  • Built-in Methods for Manipulation of Strings
  • Regular Expressions

28 : Data Aggregation

  • GroupBy
  • A Practical Example
  • Hierarchical Grouping

29 : Group Iteration

  • Chain of Transformations
  • Functions on Groups

Advanced Data Aggregation

Data Visualization with matplotlib

The matplotlib Library

Installation

IPython and IPython QtConsole

30 : matplotlib Architecture

  • Backend Layer
  • Artist Layer
  • Scripting Layer (pyplot)
  • pylab and pyplot

31 : pyplot

  • A Simple Interactive Chart
  • Set the Properties of the Plot
  • matplotlib and NumPy

32 : Using the kwargs

  • Working with Multiple Figures and Axes

33 : Adding Further Elements to the Chart

  • Adding Text
  • Adding a Grid
  • Adding a Legend

34 : Saving Your Charts

  • Saving the Code
  • Converting Your Session as an HTML File
  • Saving Your Chart Directly as an Image

Handling Date Values

Chart Typology

35 : Line Chart

  • Line Charts with pandas

Histogram

36 : Bar Chart

  • Horizontal Bar Chart
  • Multiserial Bar Chart
  • Multiseries Bar Chart with pandas DataFrame
  • Multiseries Stacked Bar Charts
  • Stacked Bar Charts with pandas DataFrame
  • Other Bar Chart Representations

37 : Pie Charts

  • Pie Charts with pandas DataFrame

38 : Advanced Charts

  • Contour Plot
  • Polar Chart

39 : mplot3d

  • 3D Surfaces
  • Scatter Plot in 3D
  • Bar Chart 3D

40 : Multi-Panel Plots

  • Display Subplots within Other Subplots
  • Grids of Subplots

01 : What is Deep learning?

  • What is Deep learning?
  • Deep learning Process
  • Classification of Neural Networks
  • Types of Deep Learning Networks
  • Feed-forward neural networks
  • Recurrent neural networks (RNNs)
  • Convolutional neural networks (CNN)

02 : Machine Learning vs Deep Learning

  • What is AI?
  • What is ML?
  • What is Deep Learning?
  • Machine Learning Process
  • Deep Learning Process
  • Automate Feature Extraction using DL
  • Difference between Machine Learning and Deep Learning
  • When to use ML or DL?

03 : What is TensorFlow?

  • What is TensorFlow?
  • History of TensorFlow
  • TensorFlow Architecture
  • Where can Tensorflow run?
  • Introduction to Components of TensorFlow
  • Why is TensorFlow popular?
  • List of Prominent Algorithms supported by TensorFlow

04 : Comparison of Deep Learning Libraries

  • 8 Best Deep learning Libraries /Framework
  • MICROSOFT COGNITIVE TOOLKIT(CNTK)
  • TenserFlow Vs Theano Vs Torch Vs Keras Vs infer.net Vs CNTK Vs MXNet Vs Caffe: Key Differences

05 : How to Download and Install TensorFlow Windows and Mac

  • TensorFlow Versions
  • Install Anaconda
  • Create .yml file to install Tensorflow and dependencies
  • Launch Jupyter Notebook
  • Jupyter with the main conda environment

06 : Jupyter Notebook Tutorial

  • What is Jupyter Notebook?
  • Jupyter Notebook App
  • How to use Jupyter

07 : Tensorflow on AWS

  • PART 1: Set up a key pair
  • PART 2: Set up a security group
  • Launch your instance (Windows users)
  • Part 4: Install Docker
  • Part 5: Install Jupyter
  • Part 6: Close connection

08 : TensorFlow Basics: Tensor, Shape, Type, Graph, Sessions & Operators

  • What is a Tensor?
  • Representation of a Tensor
  • Types of Tensor
  • Shape of tensor
  • Type of data
  • Creating operator
  • Variables

09 : Tensorboard: Graph Visualization with Example

10 : Scikit-Lear

  • What is Scikit-learn?
  • Download and Install scikit-learn
  • Machine learning with scikit-learn
  • Step 1) Import the data
  • Step 2) Create the train/test set
  • Step 3) Build the pipeline
  • Step 4) Using our pipeline in a grid search

11 : Linear Regression Tensorflow

  • Linear regression
  • How to train a linear regression model
  • How to train a Linear Regression with TensorFlow
  • Numpy Solution
  • Tensorflow solution

12 : Linear Regression Case Study

  • Summary statistics
  • Facets Overview
  • Facets Deep Dive
  • Install Facet
  • Overview
  • Graph
  • Facets Deep Dive

13 : Linear Classifier in TensorFlow

  • What is Linear Classifier?
  • How Binary classifier works?
  • How to Measure the performance of Linear Classifier?
  • Linear Classifier with TensorFlow

14 : Kernel Methods

  • Why do you need Kernel Methods?
  • What is a Kernel in machine learning?
  • Type of Kernel Methods
  • Train Gaussian Kernel classifier with TensorFlow

15 : TensorFlow ANN (Artificial Neural Network)

  • What is Artificial Neural Network?
  • Neural Network Architecture
  • Limitations of Neural Network
  • Example Neural Network in TensorFlow
  • Train a neural network with TensorFlow

16 : ConvNet(Convolutional Neural Network): TensorFlow Image Classification

  • What is Convolutional Neural Network?
  • Architecture of a Convolutional Neural Network
  • Components of Convnets
  • Train CNN with TensorFlow

17 : Autoencoder with TensorFlow

  • What is an Autoencoder?
  • How does Autoencoder work?
  • Stacked Autoencoder Example
  • Build an Autoencoder with TensorFlow

18 : RNN(Recurrent Neural Network) TensorFlow

  • What do we need an RNN?
  • What is RNN?
  • Build an RNN to predict Time Series in TensorFlow

01 : Introduction to PyTorch, Tensors, andTensor operations

  • Using Tensors

02 : Probability Distributions Using PyTorch

  • Sampling Tensors
  • Variable Tensors
  • Basic Statistics
  • Gradient Computation
  • Tensor Operations
  • Tensor Operations
  • Distributions

03 : CNN and RNN Using PyTorch

  • Setting Up a Loss Function
  • Estimating the Derivative of the Loss Function
  • Fine-Tuning a Model
  • Selecting an Optimization Function
  • Further Optimizing the Function
  • Implementing a Convolutional Neural Network (CNN)
  • Reloading a Model
  • Implementing a Recurrent Neural Network (RNN)
  • Implementing a RNN for Regression Problems
  • Using PyTorch Built-in Functions
  • Working with Autoencoders
  • . Fine-Tuning Results Using Autoencoder
  • Visualizing the Encoded Data in a 3D Plot
  • Restricting Model Overfitting
  • Visualizing the Model Overfit
  • Initializing Weights in the Dropout Rate
  • Adding Math Operations
  • Embedding Layers in RNN

04 : Introduction to Neural Networks Using PyTorch

  • Working with Activation Functions
  • Visualizing the Shape of Activation Functions
  • Basic Neural Network Model
  • Tensor Differentiation

05 : Supervised Learning Using PyTorch

  • Data Preparation for the Supervised Model
  • Forward and Backward Propagation
  • Optimization and Gradient Computation
  • Viewing Predictions
  • Supervised Model Logistic Regression

06 : Fine-Tuning Deep Learning Models Using PyTorch

  • Building Sequential Neural Networks
  • Deciding the Batch Size5
  • Deciding the Learning Rate
  • Performing Parallel Training

07 : Natural Language Processing Using PyTorch

  • Word Embedding
  • CBOW Model in PyTorch
  • LSTM Model

01 : An Introduction to Deep Learning and Keras

  • Introduction to DL
  • Demystifying the Buzzwords
  • What Are Some Classic Problems Solved by DL in Today’s Market?
  • Decomposing a DL Model
  • Exploring the Popular DL Frameworks
  • Low-Level DL Frameworks
  • High-Level DL Frameworks
  • A Sneak Peek into the Keras Framework
  • Getting the Data Ready
  • Defining the Model Structure
  • Training the Model and Making Predictions
  • Summary

02 : Keras in Action

  • Setting Up the Environment
  • Selecting the Python Version
  • Installing Python for Windows, Linux, or macOS
  • Installing Keras and TensorFlow Back End
  • Getting Started with DL in Keras
  • Input Data
  • Neuron
  • Activation Function
  • Sigmoid Activation Function Model
  • Layers
  • The Loss Function Optimizers
  • Metrics
  • Model Configuration
  • Model Training
  • Model Evaluation
  • Putting All the Building Blocks Together
  • Summary

03 : Deep Neural Networks for Supervised Learning

  • Regression
  • Getting Started
  • Problem Statement
  • Why Is Representing a Problem Statement with a Design Principle
  • Important?
  • Designing an SCQ
  • Designing the Solution
  • Exploring the Data Looking at the Data Dictionary
  • Finding Data Types
  • Working with Time
  • Predicting Sales
  • Exploring Numeric Columns
  • Understanding the Categorical Features Data Engineering
  • Defining Model Baseline Performance
  • Designing the DNN
  • Testing the Model Performance
  • Improving the Model
  • Increasing the Number of Neurons
  • Plotting the Loss Metric Across Epochs
  • Testing the Model Manually
  • Summary

04 : Deep Neural Networks for Supervised Learning

  • Classification
  • Getting Started
  • Problem Statement
  • Designing the SCQ
  • Designing the Solution
  • Exploring the Data
  • Data Engineering
  • Defining Model Baseline Accuracy
  • Designing the DNN for Classification
  • Revisiting the Data
  • Standardize, Normalize, or Scale the Data
  • Transforming the Input Data
  • DNNs for Classification with Improved Data
  • Summary

05 : Tuning and Deploying Deep Neural Networks

  • The Problem of Overfitting
  • So, What Is Regularization?
  • L1 Regularization
  • L2 Regularization
  • Dropout Regularization
  • Hyperparameter Tuning
  • Hyperparameters in DL
  • Approaches for Hyperparameter Tuning
  • Model Deployment
  • Tailoring the Test Data
  • Saving Models to Memory
  • Retraining the Models with New Data
  • Online Models
  • Delivering Your Model As an API
  • Putting All the Pieces of the Puzzle Together
  • Summary

06 : The Path Ahead

  • What’s Next for DL Expertise?
  • CNN
  • RNN
  • CNN + RNN
  • Why Do We Need GPU for DL?
  • Other Hot Areas in DL (GAN)

01: Artificial Neural Network

  • The Neuron
  • The Activation Function
  • How do Neural Networks work?
  • How do Neural Networks learn?
  • Gradient Descent
  • Stochastic Gradient Descent
  • Backpropagation

02: Convolutional Neural Network

  • What are Convolutional Neural Networks?
  • The Convolution Operation
  • Bis - The ReLU Layer
  • Pooling
  • Flattening
  • Full Connection
  • Softmax & Cross-Entropy

03: AutoEncoder

  • What are AutoEncoders?
  • A Note on Biases
  • Training an AutoEncoder
  • Overcomplete Hidden Layers
  • Sparse AutoEncoders
  • Denoising AutoEncoders
  • Contractive AutoEncoders
  • Stacked AutoEncoders
  • Deep AutoEncoders

04: Variational AutoEncoder

  • Introduction to the VAE
  • Variational AutoEncoders
  • Reparameterization Trick

05: Implementing the CNN-VAE

  • Initializing all the parameters and variables of the CNN-VAE class
  • Building the Encoder part of the VAE
  • Building the "V" part of the VAE
  • Building the Decoder part of the VAE
  • Implementing the Training operations
  • Full Code Section
  • The Keras Implementation

06: Recurrent Neural Network

  • What are Recurrent Neural Networks?
  • The Vanishing Gradient Problem
  • LSTMs
  • LSTM Practical Intuition
  • LSTM Variations

07: Mixture Density Network

  • Mixture Density Networks
  • VAE + MDN-RNN Visualization

08: Implementing the MDN-RNN

  • Initializing all the parameters and variables of the MDN-RNN class
  • Building the RNN - Gathering the parameters
  • Building the RNN - Creating an LSTM cell with Dropout
  • Building the RNN - Setting up the Input, Target, and Output of the RNN
  • Building the RNN - Getting the Deterministic Output of the RNN
  • Building the MDN - Getting the Input, Hidden Layer and Output of the MDN
  • Building the MDN - Getting the MDN parameters
  • Implementing the Training operations (Part 1)
  • Implementing the Training operations (Part 2)
  • Full Code Section
  • The Keras Implementation

09: Reinforcement Learning

  • What is Reinforcement Learning?
  • A Pseudo Implementation of Reinforcement Learning for the Full World Model
  • Full Code Section

10: Deep NeuroEvolution

  • Deep NeuroEvolution
  • Evolution Strategies
  • Genetic Algorithms
  • Covariance-Matrix Adaptation Evolution Strategy (CMA-ES)
  • Parameter-Exploring Policy Gradients (PEPG)
  • OpenAI Evolution Strategy

01 : Extracting the Data

  • Collecting Data
  • Collecting Data from PDFs
  • Collecting Data from Word Files. 1-4. Collecting Data from JSON
  • Collecting Data from HTML
  • Parsing Text Using Regular Expressions
  • Handling Strings
  • Scraping Text from the Web

02 : Exploring and Processing Text Data

  • Converting Text Data to Lowercase
  • Removing Punctuation
  • Removing Stop Words
  • Standardizing Text
  • Correcting Spelling
  • Tokenizing Text
  • Stemming
  • Lemmatizing
  • Exploring Text Data
  • Building a Text Preprocessing Pipeline

03 : Converting Text to Features

  • Converting Text to Features Using One Hot Encoding
  • Converting Text to Features Using Count Vectorizing
  • Generating N-grams
  • Generating Co-occurrence Matrix
  • Hash Vectorizing
  • Converting Text to Features Using TF-IDF
  • Implementing Word Embeddings
  • Implementing fastText

04 : Advanced Natural Language Processing

  • Extracting Noun Phrases
  • Finding Similarity Between Texts
  • Tagging Part of Speech
  • Extract Entities from Text
  • Extracting Topics from Text
  • Classifying Text
  • Carrying Out Sentiment Analysis
  • Disambiguating Text
  • Converting Speech to Text
  • Converting Text to Speech
  • Translating Speech

05 : Implementing Industry Applications

  • Implementing Multiclass Classification
  • Implementing Sentiment Analysis
  • Applying Text Similarity Functions
  • Summarizing Text Data
  • Clustering Documents
  • NLP in a Search Engine

06 : Deep Learning for NLP

  • Retrieving Information
  • Classifying Text with Deep Learning
  • Next Word Prediction
450+
Hours of Content
12
Case Study & Projects
35+
Live Sessions
11
Coding Assignments
10
Capstone Projects to Choose From
20
Tools, Languages & Libraries

Languages and Tools covered

Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered Languages and Tools covered

Hands On Projects

Time Series Forecasting with LSTM Neural Network Python

Learn to apply deep learning paradigm to forecast univariate time series data

Fire Detection and Localization Using Surveillance Camera

In this Project We will improve the fire detection system through surveillance cameras by building a model that can not only detect the fire but also the location of the fire to provide effective detection and reporting system for the safety of people

Next word predictor

In this Project We will build an artificial intelligence model that can predict the next word that is most likely to come. To implement this we will be using Natural language processing and deep learning

Automatic Attendance System

In schools and colleges, a lot of time is wasted in taking the attendance of the students. The idea of the project is to automate the attendance system by using a camera that automatically recognizes the faces and marks the attendance of the people

Facial Emotion Recognition and Detection

This project seeks to expand on a pioneering modern application of Deep Learning – facial emotion recognition. Although facial emotion recognition has long been the subject of research and study, it is only now that we are witnessing tangible results of that analysis.

Online Assignment Plagiarism Checker

In this project, you will develop a plagiarism detector that can detect the similarities in copies of text and detect the percentage of plagiarism

Certification

Our training is based on latest cutting-edge infrastructure technology which makes you ready for the industry.Osacad will Present this certificate to students or employee trainees upon successful completion of the course which will encourage and add to trainee’s resume to explore a lot of opportunities beyond position

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Artificial Intelligence which is a global company with headquarters in Chicago, USA. Artificial Intelligence has partnered with GamaSec, a leading Cyber Security product company. Artificial Intelligence is focusing on building Cyber Security awareness and skills in India as it has a good demand in consulting and product support areas. The demand for which is predicted to grow exponentially in the next 3 years. The Artificial Intelligence training programs are conducted by individuals who have in depth domain experience. These training sessions will equip you with the fundamentalknowledge and skills required to be a professional cyber security consultant.

All graduates of commerce, law, science and engineering who want to build a career in cyber security can take this training.

There are a number of courses, which are either 3 months or 6 months long. To become a cyber security consultant we recommend at least 6 to 9 months of training followed by 6 months of actual project work.During project work you will be working under a mentor and experiencing real life customer scenarios.

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  • Comprehensive Hands-on with Python
  • Extensive supervised & unsupervised and Reinforcement Algorithms
  • Learn Deep Learning Techniques using TensorFlow and Keras, PyTorch
  • Learn how to Natural language Processing applications

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