Learn how to automate AWS with Python scripts, manage AWS resources with Boto3 library, and more with Automating AWS with Python and Boto3 training course.
  • 3 hours 36 minutes of training Videos
  • 20 lectures
  • Very exhaustive coverage to all the topics
  • Unlimited Access
Over 1.5 million AWS experts have started their preparation with our Online Courses. Enroll now for Automating AWS with Python & Boto3 online course.
Topic-wise Content Distribution
  • Introduction & Prerequisites
  • 5m 27s
Getting Started
  • Install AWS SDK
    7m 51s
  • Setting up authentication
    16m 19s
  • API Key Best practices
    11m 45s
  • Testing setup
    10m 51s
  • IAM Permissions for Development
    10m 23s
  • AWS Boto3 Documentation
    14m 16s
EC2 Instance Use-Case
  • Starting an EC2 Instance with Python
    15m 53s
  • Additional features for EC2
    13m 52s
  • Unit Test for Boto3 SDK
    24m 1s
  • Waiters in Boto3 SDK
    5m 50s
S3 Bucket Enforcement Use-Case
  • Enforcing S3 with Python
    9m 32s
  • Exceptions in Boto3 SDK
    10m 47s
  • Automating AWS Lambda to run Python
    11m 54s
EC2 Export use-case
  • Amazon EC2 Report with Python
    10m 26s
  • Pagination in Boto3 SDK
    7m 23s
  • Filtering Amazon EC2 Query
    5m 7s
CloudTrail Enforcement use-case
  • Enforcing AWS CloudTrail with Python
    10m 42s
  • Deploying the AWS Lambda and trigger
    10m 7s
  • What Next?
  • 3m 49s
From web development to data science, Python is everywhere. Learn the basic and advanced concepts of Python with Python for Beginners training course.
  • 6+ hours of Training Videos
  • 52 lectures
  • Very exhaustive coverage to all the topics
  • Unlimited Access
Topic-wise Content Distribution Section One-Introducing Python Programming
  • Python Programming Overview
    3m 22s
  • Python Installation
  • Basic Syntax
    4m 28s
  • Variable Types
    6m 59s
  • Basic Operators
    6m 31s
Section Two - String in Python 
  • About String in Python
    5m 37s
  • Print a string Program
    6m 16s
  • Program to Extract Characters
    9m 13s
  • String Concatenation Program
    6m 58s
  • Palindrome String Program
    9m 9s
  • Sort a string
    7m 10s
Section Three-Lists in Python
  • About Lists in Python
    2m 38s
  • Define Lists
    6m 1s
  • Lists Basic Operations - Part 1
    7m 43s
  • Lists Basic Operations - Part 2
    6m 49s
Section Four - Tuples in Python
  • About Tuples in Python
    2m 41s
  • Basic Python Program for Tuples
    5m 35s
  • Tuples Basic Operations – Part 1
    8m 35s
  • Tuples Basic Operations – Part 2
    7m 33s
Section Five - Set in Python
  • About Set in Python
    2m 32s
  • Print Set Program
    5m 19s
  • Methods in Set
    5m 33s
  • Use of Operators in set
    7m 29s
Section Six - Dictionary in Python
  • About Dictionary in Python
    3m 12s
  • Program to Extract Elements from dictionary
    4m 49s
  • Program to Change Values
    5m 45s
  • Program to Delete Items from dictionary
    6m 58s
  • Dictionary Basic Operations
    8m 28s
Section Seven - if- else loop in Python
  • If loop Program
    7m 50s
  • If - else loop Program
    6m 13s
  • If- elif loop Program
    7m 54s
  • If-else nested Program
    8m 45s
Section Eight - While, for Range loop in Python
  • About While, for and Range loop
    3m 27s
  • For loop Program
    7m 1s
  • Range Function Program
    7m 33s
  • Range Function in a for loop
    5m 54s
  • While loop Program
    8m 8s
Section Nine - Break and Continue Statements in Python
  • About Break and Continue Statement
    3m 29s
  • Break Statement Program
    11m 28s
  • Continue Statement Program
    9m 58s
Section Ten - Function and Lambda in Python
  • Program to define Functions
    7m 19s
  • Default Parameters in function
    8m 24s
  • Factorial Program using function
    6m 39s
  • Lambda Program
    8m 50s
Section Eleven - Map, Math and Excaption in Python
  • Map Program
    7m 13s
  • Math Program
    6m 10s
  • Exception Program
    9m 22s
Section Twelve - Class, Object and Constructor
  • Class in Python Program
    7m 4s
  • Defining Object in python
    6m 6s
  • Constructors in python Program
    10m 33s
  • Project 1 – Guess the Number Game in python
    14m 58s
  • Project 2 – Rock, Paper, Scissors Game in python
    18m 9s
This Introduction to Data Science with Python course aims to help you master Python programming concepts that are essential for Data Science.
  • 8+ hours of training videos
  • Very extensive coverage to all the topics
  • Unlimited access for 2 years
Topic-wise Content Distribution Introduction
  • Course Intro
  • 5m 35s
Data Pre-Processing 
  • Data Structures in Python
    6m 35s
  • Basic Python libraries
    7m 58s
  • Demo - NumPy
    15m 16s
  • Demo - Pandas
    10m 34s
  • Demo - Matplotlib
    11m 50s
  • Demo - Pandas Case Study
    5m 7s
  • Covariance and Correlation
    7m 25s
  • Data Preprossesing for Machine Learning
    13m 8s
Types of MAchine Learning Problems
  • The two types of ML Problems
    2m 22s
  • Classification
    14m 48s
  • Regression
    12m 10s
  • K-means Clustering
    8m 53s
  • Demo - Hierarchical-clustering
    9m 3s
  • Outlier Detection
    7m 30s
Linear Regression
  • Linear Regression
    14m 28s
  • Multiple Linear Regression
    13m 5s
Optimization and Gradient Descent
  • Optimization in Machine Learning
    16m 9s
  • Batch Gradient Descent
    15m 21s
  • Stochastic Gradient Descent
    11m 56s
Logistic Regression
  • Logistic Regression - Sigmoid Function
    12m 55s
  • Cost Function and Decision Boundary
    5m 1s
  • Demo - Cost Function and Finding the Decision Boundaries
    16m 12s
Deep Learning Introduction & Perceptrons
  • Deep Learning and Perceptron
    7m 34s
  • Forward and Backward Propagation
    12m 27s
  • Demo - Forward and Backward Propagation
    9m 41s
Modern Deep Learning Libraries
  • TensorFlow
    9m 35s
  • Demo - Image Classification using TensorFlow
    15m 56s
  • PyTorch
    9m 33s
  • Demo - Defining a Neural Network in PyTorch
    7m 53s
  • Keras
    13m 47s
Feedforward Neural Network
  • Feedforward Neural Network
    11m 23s
  • Demo - Activation Functions in Neural Networks
    8m 45s
  • Perceptron Model
    15m 49s
Recurrent Neural Networks
  • Recurrent Neural Networks (RNN)
    11m 38s
  • LSTM and GRUs
    6m 11s
  • Demo - Recurrent Neural Networks (RNN) with Keras
    11m 6s
Convolutional Neural Networks
  • Convolutional Neural Networks
    16m 16s
  • Demo - Computer Vision with OpenCV
    7m 17s
  • Computer Vision
    6m 33s
  • Demo - Convolutional Neural Networks with Max Pooling
    14m 11s
Machine Learning on the Cloud
  • Machine Learning on AWS Cloud
    14m 5s
  • Demo - ML on AWS - Part 1
    8m 41s
  • Demo - ML on AWS - Part 2
    10m 19s
  • Demo - ML on AWS - Part 3
    8m 26s
  • Machine Learning on Azure
    6m 11s
  • Demo - ML on Azure - Part 1
    7m 5s
  • Demo - ML on Azure - Part 2
    13m 11s
  • Machine Learning on GCP
    6m 57s
  • Demo - ML on GCP - Part 1
    8m 33s
  • Demo - ML on GCP - Part 2
    12m 11s
  • Key differences between AWS, Azure and GCP
    2m 25s
Enroll in our Natural Language Processing course today to master the fundamentals of NLP, Machine Learning concepts, Python toolkits, and gain confidence. This course will take you through beginner to advanced-level concepts of NLP techniques, NLP Models to design and develop NLP-based applications using Python. After going through all the 24 videos you will have hands-on experience and complete understanding of how NLP works in Python.
NLP is a technique for deciphering and manipulating human language using algorithms. It is one of the most widely used techniques of machine learning. Professionals proficient in designing models that evaluate voice and language, find contextual patterns, and provide insights from text and audio will be in high demand as AI grows.
This course provides you necessary skills and knowledge of some of the NLP concepts and following are the important concepts covered:
  1. Use Text Analytics with Python
  2. Use Machine Learning concepts for NLP
  3. Use Python toolkits for NLP
  4. Use NLTK for Sentiment Analysis
  5. Learn about NLP Modules
  6. Learn Clustering and topic modeling
  7. Learn spaCy, Glove, SpamHam
  • Parts-of-Speech Tagging
  • Natural Language Analysis
  • Bagging
  • Text processing of data
If you are an application developer, you will be able to build applications that help to process and analyse NLP data. Whereas, if you are a Python developer, you will be able to build Python programs for NLP using NLTK. If you are a data scientist, then you will be able to use NLP techniques such as Sentiment analysis, topic modeling, POS Tagging, and so on. Finally, if you are an NLP developer, then you will expand your knowledge and skills on NLP using Python.
By the end of this course, you will be able to demonstrate the skills to use Python for designing NLP applications. You will also get unlimited access to 3+ hours of all 24 video lectures developed by our experts. Earn a course completion certificate once you complete this course to showcase your technical skills and for credibility.
  • Growing demand: According to MarketsandMarkets, the NLP market is growing at a Compound Annual Growth Rate (CAGR) of 20.3%, between 2020 and 2025 period. You can become one among the NLP professionals in this rapidly growing market that is worth around USD 11-26 billion.
  • NLP skills: Learning NLP using Python is easier, there are a large number of libraries to develop NLP based applications and community support is high. This helps you to enrich your knowledge and skills to become an expert.
  • Career growth: With NLP skill sets, you can find job roles in top companies like Accenture, Qualcomm Technologies, Huawei Technologies, Thomson Reuters, and so on.
  • 24 full length video lectures
  • Auto-updates to the course content
  • 24x7 Support from our Subject Matter Experts
  • Lifetime validity and unlimited access
  • Course completion certificate
Topic-wise Content Distribution Course Lectures
  • Introduction
    19m 51s
  • Corpus
    10m 28s
  • Feature Extraction
    17m 38s
  • Demo Installation
    6m 31s
  • Demo Sentiment Analysis
    19m 2s
  • Demo Text Processing
    20m 2s
  • Text Analytics
    15m 30s
  • Machine learning
    25m 10s
  • Natural Language
    13m 47s
  • Python Toolkits
    19m 4s
  • POSTagging
    11m 58s
  • SpamHam
    14m 17s
  • ClusteringAndTopics
    7m 59s
  • Bagging
    5m 19s
  • Deep learning 1: Introduction
    16m 33s
  • Deep learning 2: NLP models
    16m 52s
  • Demo setup
    11m 51s
  • Demo clustering and topic modeling 1
    20m 27s
  • Demo clustering and topic modeling 2
    21m 47s
  • Demo Sentiment Analysis
    19m 2s
  • Demo NLTK
    12m 48s
  • Demo sPacy
    23m 39s
  • Demo Glove
    12m 43s
  • Demo SpamHam
    18m 13s
The “TensorFlow for Deep Learning with Python” course is designed to help you gain knowledge of Deep Learning models using TensorFlow. You will learn the architectures of common deep learning models, implement them in TensorFlow 2.0 and learn to optimize them.
Anyone interested in this program needs to have:
    • Basic knowledge of Python, math
    • Suggested: Basic understanding of AI, ML
  • Basics of Deep Learning
  • Learn how to build in TensorFlow 2.0:
    • Multilayer Perceptron
    • Convolutional Neural Network
    • Recurrent Neural Network
    • Generative adversarial network
    • Auto Encoder
    • Variational Autoencoder
  • Re-enforcement Learning
  • Metrics, model tuning, loss functions
  • TensorFlow Lite
  • Mathematicians, computer scientists
  • Anyone interested in AI, ML and TenorFlow 2.0
Topic-wise Content Distribution Course Lectures
  • Introduction to deep learning
    17m 18s
  • Recurrent neural networks: Introduction
    13m 3s
  • Recurrent neural networks: Variants
    18m 5s
  • Recurrent neural network: Advanced
    9m 22s
  • Reinforcement Learning: Bsics
    13m 15s
  • Reinforcement Learning: Example
    6m 32s
  • Generative Adversarial Networks: Basics
    16m 30s
  • Generative Adversarial Networks: Types
    9m 39s
  • Convolutional neural networks: Basics
    15m 56s
  • Convolutional neural networks: Layers
    18m 41s
  • Convolutional neural networks: Architectures
    13m 41s
  • Autoencoders
    10m 12s
  • Large Deep Networks
    15m 52s
  • TensorFlow Lite
    9m 14s
  • Model Evaluation: Metrics
    13m 11s
  • Model Evaluation: Optimization methods
    18m 27s
  • Loss functions
    13m 27s
  • Variational autoencoder
    9m 47s
  • demo CNN layer analysis
    8m 31s
  • demo RNN 1: Build model
    18m 7s
  • demo RNN 2: Analyze results
    12m 1s
  • demo Variational autoencoder
    6m 25s
  • demo Autoencoder
    5m 16s
  • demo Regression
    9m 51s
  • demo Generative Adversarial Network
    8m 29s
  • demo Regularization
    7m 20s