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** 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+

12

35+

11

10

20

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

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

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

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

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.

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

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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OR

- 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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4th floor, Khajaguda Main Road, next to Andhra Bank, near DPS, Khajaguda, Gachibowli, Hyderabad, Telangana 500008

Plot No. 430, Sri Ayyappa Society, Khanamet, Madhapur, Hyderabad-500081

Uptown Cyberabad Building, Block-C, 1st Floor Plot – 532 & 533, 100 Feet Road Sri Swamy Ayyappa Housing Society, Madhapur, Hyderabad, Telangana 500081

5999 S New Wilke Rd, Bldg 3, #308 Rolling Meadows, IL 60008