Full Stack Python Development Building Realworld Application

Master Python for Full Stack Development. Build scalable web apps, APIs, and databases using Django, Flask, and React.

What you'll learn

Master Python Fundamentals: Gain a solid understanding of Python syntax, data structures, control flow, and functions.

Build Dynamic User Interfaces: Learn HTML, CSS, and JavaScript to create interactive and visually appealing web pages.

Develop Server-Side Logic: Utilize Python frameworks like Django or Flask to handle user requests, manage data, and power your web applications.

Connect to Databases: Work with relational databases like PostgreSQL or MySQL to store and retrieve data for your applications.

Deploy Applications: Learn how to deploy your web application to a live server, making it accessible to users worldwide.

Requirements

This course is designed for beginners and requires no prior programming experience.

You'll be starting with the fundamentals and building your skills step-by-step.

A basic understanding of computers and the internet will be helpful, but not mandatory.

A computer with a reliable internet connection.

Enthusiasm for learning and problem-solving!

No prior programming experience is required! This course is designed for beginners with an interest in web development and a willingness to learn.

amiliarity with using a computer and navigating operating systems.

Ability to follow written instructions and troubleshoot basic computer issues.

Description

Are you ready to become a proficient full-stack developer using Python? This course is your ultimate guide to mastering full-stack development, focusing on building real-world, scalable applications. Whether you are a beginner or have prior programming experience, this course provides a hands-on approach to understanding and implementing Python in full-stack development.In this course, you will:Learn Python fundamentals for backend development.Master frontend frameworks like React and HTML/CSS.Build robust APIs using Flask and Django.Understand database integration with MySQL, PostgreSQL, and MongoDB.Deploy web applications on cloud platforms like AWS and Heroku.Collaborate on real-world projects, following Agile and Git-based workflows.By the end of the course, you will have built a fully functional, real-world application and gained the confidence to tackle modern web development challenges.This course is perfect for students, software professionals, and anyone passionate about creating impactful, scalable web solutions.Enroll now and begin your journey to becoming a Full Stack Python Developer!By the end of the course, you will have built a fully functional, real-world application and gained the confidence to tackle modern web development challenges.This course is perfect for students, software professionals, and anyone passionate about creating impactful, scalable web solutions.Enroll now and begin your journey to becoming a Full Stack Python Developer!

Overview

Section 1: Introduction to Python and Lists

Lecture 1 Python Lists: Your Creative Toolkit

Lecture 2 Mastering List Magic: Advanced Techniques

Lecture 3 From Data to Art: Lists and Tuples in Action

Lecture 4 Unleash Your Creativity with Sets

Lecture 5 Organizing Your Art with Dictionaries

Lecture 6 Text Alchemy: String Manipulation in Python

Lecture 7 Time as Art: Working with Dates and Times in Python

Lecture 8 Data-Driven Storytelling: Customer Churn Prediction

Lecture 9 The Power of Lambda: Functional Programming for Artists

Lecture 10 Map, Reduce, and Conquer: Functional Programming Essentials

Lecture 11 Building Blocks of Creativity: Functions in Python

Lecture 12 Function Mastery: Arguments, Scope, and Beyond

Section 2: Recursion and Global Variables

Lecture 13 Recursive Art: Unlocking Patterns with Python

Lecture 14 Time as a Feature: Engineering with Datetime

Lecture 15 Unveiling the Iris Dataset: A Machine Learning Prelude

Lecture 16 Python's Math and Random Toolbox

Lecture 17 Exploring Your Data: File Handling and EDA

Lecture 18 Finding Patterns: Correlation and Visualization

Lecture 19 Data Distributions: Telling Your Story

Lecture 20 Spotting the Unusual: Outlier Detection Techniques

Lecture 21 Mastering Outliers: Advanced Detection Strategies

Lecture 22 Data Preparation: The Foundation for Artful Insights

Section 3: Logistic Regression Fundamentals

Lecture 23 Logistic Regression: From Zero to Hero

Lecture 24 Demystifying Logistic Regression Math

Lecture 25 Logistic Regression: Real-World Examples You Can't Ignore

Lecture 26 Data Cleaning: The Unsung Hero of ML

Lecture 27 Feature Engineering Magic: Transform Your Data

Lecture 28 Know Your Model: Essential Evaluation Metrics

Lecture 29 NLP for Beginners: Start with Logistic Regression

Lecture 30 Supercharge Your NLP with Advanced Techniques

Lecture 31 Transfer Learning: The NLP Shortcut You Need

Lecture 32 Taming COVID-19 Data: A Data Scientist's Guide

Lecture 33 Unmasking COVID-19 Trends: Data-Driven Insights

Lecture 34 The Machine Learning Lifecycle: From Data to Deployment

Lecture 35 Text Preprocessing: Clean Up Your Act

Lecture 36 Advanced Text Preprocessing: Unlock Hidden Patterns

Lecture 37 Telling Stories with Text Data: EDA Mastery

Lecture 38 Feature Engineering: The Secret to NLP Success

Lecture 39 Optimize Your Model: Hyperparameter Tuning Tips

Lecture 40 Finding the Perfect Hyperparameters: A Practical Guide

Lecture 41 Regularization: Prevent Overfitting Like a Pro

Lecture 42 Which Model Wins? A Showdown

Lecture 43 Linear Regression: The Building Block of ML

Lecture 44 Linear Regression: Simple Models, Big Impact

Lecture 45 Boost Your Linear Regression Game

Lecture 46 Decision Trees: Easy to Understand, Powerful to Use

Lecture 47 Decision Trees: The Building Blocks

Lecture 48 Mastering Entropy and Information Gain

Lecture 49 Avoid Overfitting: Deep Dive into Decision Trees

Lecture 50 Handling Categorical Data: Decision Tree Style

Lecture 51 Train and Conquer: Decision Tree Mastery

Lecture 52 Data-Driven Insights: Univariate Analysis

Lecture 53 Data Visualization: Tell Your Story Visually

Lecture 54 Spotting Trends: Outliers and Correlations

Lecture 55 Advanced Visualization: Uncover Hidden Insights

Lecture 56 Bivariate Analysis: Uncover Relationships

Lecture 57 Multivariate Analysis: Mastering Complexity

Lecture 58 Time Series Analysis: Forecasting the Future

Lecture 59 K-means Clustering: Find Your People

Lecture 60 Mastering K-means: Tips and Tricks

Lecture 61 K-means in Action: Real-World Examples

Lecture 62 Beyond K-means: Advanced Clustering Techniques

Lecture 63 Evaluating Your Clusters: Does It Make Sense?

Section 4: Introduction to Deep Learning Concepts

Lecture 64 The History of Deep Learning and Inspired by Neuroscience

Lecture 65 Understanding Neural Networks: Weights, Multi-Neuron Networks

Lecture 66 Dive Deep into Backpropagation

Lecture 67 Introduction to RNNs: The Intuition Behind RNNs and Different Cells

Lecture 68 Building RNNs with TensorFlow: Hands-on Multiple Neural Networks

Lecture 69 Training RNNs in TensorFlow: Model Fit, Compile, and Execute

Lecture 70 Optimizing Model Training: Model Training with Number of Epochs

Lecture 71 Sequence-to-Sequence Models: Encoder and Decoder Models

Lecture 72 LSTM Networks and Applications: Random Initialization and LSTM Intuition

Lecture 73 Implementing LSTMs with TensorFlow: Custom Implementation

Lecture 74 Introduction to Computer Vision: Pixel Idea and Conversion into Arrays

Lecture 75 Basics of Convolutional Neural Networks: Padding and Kernel

Lecture 76 Understanding Kernels in CNNs: Different Kernels

Lecture 77 Padding, Strides, and Pooling in CNNs

Lecture 78 Data Augmentation and Optimization in CNNs: Hands-on TensorFlow

Lecture 79 Building and Training CNN Models

Lecture 80 Implementing LSTMs with TensorFlow: Preprocessing of Data

Lecture 81 New! Building Generative Models with LSTMs: Train Models with Hyperparameter Tun

Lecture 82 Introduction to Computer Vision with Deep Learning: Preprocessing and Training w

Lecture 83 Training Deep Learning Models for Image Data: 1500 Images on Training and Test D

Lecture 84 Efficiently Handling Large Image Data: Training Samples

Lecture 85 Advanced Image Processing Techniques: Cleaning and Preprocessing Data

Lecture 86 Classification with Deep Learning: 10 Classification Tasks

Lecture 87 Model Evaluation and Transfer Learning: Evaluating Models and Transformers

Lecture 88 Interpreting Deep Learning Models: Geometric Intuition of VGG16 Models

Lecture 89 Optimizing Deep Learning Models: Gradient Descent and Stochastic Gradient Descen

Lecture 90 Advanced Optimization Techniques

Lecture 91 Practical Deployment of Deep Learning Models: Mathematical Equations

Lecture 92 Deploying Models with Flask: Understanding the Internals

Lecture 93 Handling Requests with Keras and Flask: Keras Models and Get/Post Methods

Lecture 94 Scaling Deep Learning Models: Image CNN Animal in Action

Lecture 95 Ensuring Low Latency in Model Deployment: Getting Logs Flask Application

Lecture 96 Flask Deployment Made Easy: Step-by-Step Guide for Real-World Applications

Lecture 97 Practical Flask Deployment for Beginners: Go Live Today!

Section 5: Introduction to Business Statistics

Lecture 98 Introduction to Data Types and Business Statistics

Lecture 99 Quantitative vs Qualitative Data: A Comparative Analysis

Lecture 100 Measures of Central Tendency: Mean, Median, and Mode

Lecture 101 Understanding Measures of Dispersion

Lecture 102 Introduction to Distributions and the Central Limit Theorem

Lecture 103 Sampling and Z-Scores

Lecture 104 Hypothesis Testing and P-Value Interpretation

Lecture 105 T-tests, Confidence Intervals, and ANOVA

Lecture 106 Pearson Correlation Coefficient Explained

Lecture 107 Advanced Hypothesis Testing and Correlation Analysis

Lecture 108 Data Cleaning and Preprocessing Techniques

Lecture 109 Visualising Data with Histograms and Box Plots

Lecture 110 Summary Statistics and Variable Relationships

Lecture 111 Correlation and Pair Plots

Lecture 112 Handling High Correlation and Using Heat Maps

Section 6: Foundations of Time Series Analysis

Lecture 113 Introduction to Time Series Data

Lecture 114 Understanding Time Series Components

Lecture 115 Stationarity and Its Importance

Lecture 116 ARIMA Model Fundamentals

Lecture 117 Building and Evaluating ARIMA Models

Lecture 118 Seasonal Time Series and Decomposition

Lecture 119 Probability Distributions in Time Series

Lecture 120 Descriptive Statistics and Exploratory Data Analysis

Lecture 121 Hypothesis Testing and Confidence Intervals

Lecture 122 Forecasting with ARIMA Models

Lecture 123 Model Selection and Evaluation

Lecture 124 Practical Forecasting and Model Improvement

Lecture 125 Data Visualization for Time Series

Lecture 126 Time Series in Python: Practical Implementation

Lecture 127 Real-world Case Studies and Applications

Absolute Beginners with No Coding Experience,Career Changers or Enthusiasts Looking to Enter Web Development,Individuals with Basic Computer Skills and a Curiosity for Coding,Absolute Beginners with No Programming Experience,Students with Basic Coding Knowledge,Career Changers or Enthusiasts