Python

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.

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
  7m
• 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

Projects

• 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