Full Stack Python Development Building RealWorld Application

سرفصل های دوره

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

1 - Introduction to Python and Lists

1 -Python Lists Your Creative Toolkit

2 -Mastering List Magic Advanced Techniques

3 -From Data to Art Lists and Tuples in Action

4 -Unleash Your Creativity with Sets

5 -Organizing Your Art with Dictionaries

6 -Text Alchemy String Manipulation in Python

7 -Time as Art Working with Dates and Times in Python

8 -Data-Driven Storytelling Customer Churn Prediction

9 -The Power of Lambda Functional Programming for Artists

10 -Map, Reduce, and Conquer Functional Programming Essentials

11 -Building Blocks of Creativity Functions in Python

12 -Function Mastery Arguments, Scope, and Beyond

2 - Recursion and Global Variables

1 -Recursive Art Unlocking Patterns with Python

2 -Time as a Feature Engineering with Datetime

3 -Unveiling the Iris Dataset A Machine Learning Prelude

4 -Pythons Math and Random Toolbox

5 -Exploring Your Data File Handling and EDA

6 -Finding Patterns Correlation and Visualization

7 -Data Distributions Telling Your Story

8 -Spotting the Unusual Outlier Detection Techniques

9 -Mastering Outliers Advanced Detection Strategies

10 -Data Preparation The Foundation for Artful Insights

3 - Logistic Regression Fundamentals

1 -Logistic Regression From Zero to Hero

2 -Demystifying Logistic Regression Math

3 -Logistic Regression Real-World Examples You Cant Ignore

4 -Data Cleaning The Unsung Hero of ML

5 -Feature Engineering Magic Transform Your Data

6 -Know Your Model Essential Evaluation Metrics

7 -NLP for Beginners Start with Logistic Regression

8 -Supercharge Your NLP with Advanced Techniques

9 -Transfer Learning The NLP Shortcut You Need

10 -Taming COVID-19 Data A Data Scientists Guide

11 -Unmasking COVID-19 Trends Data-Driven Insights

12 -The Machine Learning Lifecycle From Data to Deployment

13 -Text Preprocessing Clean Up Your Act

14 -Advanced Text Preprocessing Unlock Hidden Patterns

15 -Telling Stories with Text Data EDA Mastery

16 -Feature Engineering The Secret to NLP Success

17 -Optimize Your Model Hyperparameter Tuning Tips

18 -Finding the Perfect Hyperparameters A Practical Guide

19 -Regularization Prevent Overfitting Like a Pro

20 -Which Model Wins A Showdown

21 -Linear Regression The Building Block of ML

22 -Linear Regression Simple Models, Big Impact

23 -Boost Your Linear Regression Game

24 -Decision Trees Easy to Understand, Powerful to Use

25 -Decision Trees The Building Blocks

26 -Mastering Entropy and Information Gain

27 -Avoid Overfitting Deep Dive into Decision Trees

28 -Handling Categorical Data Decision Tree Style

29 -Train and Conquer Decision Tree Mastery

31 -Data Visualization Tell Your Story Visually

32 -Spotting Trends Outliers and Correlations

33 -Advanced Visualization Uncover Hidden Insights

34 -Bivariate Analysis Uncover Relationships

35 -Multivariate Analysis Mastering Complexity

36 -Time Series Analysis Forecasting the Future

37 -K-means Clustering Find Your People

38 -Mastering K-means Tips and Tricks

39 -K-means in Action Real-World Examples

40 -Beyond K-means Advanced Clustering Techniques

41 -Evaluating Your Clusters Does It Make Sense

4 - Introduction to Deep Learning Concepts

1 -The History of Deep Learning and Inspired by Neuroscience

2 -Understanding Neural Networks Weights, Multi-Neuron Networks

3 -Dive Deep into Backpropagation

4 -Introduction to RNNs The Intuition Behind RNNs and Different Cells

5 -Building RNNs with TensorFlow Hands-on Multiple Neural Networks

6 -Training RNNs in TensorFlow Model Fit, Compile, and Execute

7 -Optimizing Model Training Model Training with Number of Epochs

8 -Sequence-to-Sequence Models Encoder and Decoder Models

9 -LSTM Networks and Applications Random Initialization and LSTM Intuition

10 -Implementing LSTMs with TensorFlow Custom Implementation

11 -Introduction to Computer Vision Pixel Idea and Conversion into Arrays

12 -Basics of Convolutional Neural Networks Padding and Kernel

13 -Understanding Kernels in CNNs Different Kernels

14 -Padding, Strides, and Pooling in CNNs

15 -Data Augmentation and Optimization in CNNs Hands-on TensorFlow

16 -Building and Training CNN Models

17 -Implementing LSTMs with TensorFlow Preprocessing of Data

18 -New! Building Generative Models with LSTMs Train Models with Hyperparameter Tun

19 -Introduction to Computer Vision with Deep Learning Preprocessing and Training w

20 -Training Deep Learning Models for Image Data 1500 Images on Training and Test D

21 -Efficiently Handling Large Image Data Training Samples

22 -Advanced Image Processing Techniques Cleaning and Preprocessing Data

23 -Classification with Deep Learning 10 Classification Tasks

24 -Model Evaluation and Transfer Learning Evaluating Models and Transformers

25 -Interpreting Deep Learning Models Geometric Intuition of VGG16 Models

26 -Optimizing Deep Learning Models Gradient Descent and Stochastic Gradient Descen

27 -Advanced Optimization Techniques

28 -Practical Deployment of Deep Learning Models Mathematical Equations

29 -Deploying Models with Flask Understanding the Internals

30 -Handling Requests with Keras and Flask Keras Models and GetPost Methods

31 -Scaling Deep Learning Models Image CNN Animal in Action

32 -Ensuring Low Latency in Model Deployment Getting Logs Flask Application

33 -Flask Deployment Made Easy Step-by-Step Guide for Real-World Applications

34 -Practical Flask Deployment for Beginners Go Live Today!

5 - Introduction to Business Statistics

1 -Introduction to Data Types and Business Statistics

2 -Quantitative vs Qualitative Data A Comparative Analysis

3 -Measures of Central Tendency Mean, Median, and Mode

4 -Understanding Measures of Dispersion

5 -Introduction to Distributions and the Central Limit Theorem

6 -Sampling and Z-Scores

7 -Hypothesis Testing and P-Value Interpretation

8 -T-tests, Confidence Intervals, and ANOVA

9 -Pearson Correlation Coefficient Explained

10 -Advanced Hypothesis Testing and Correlation Analysis

11 -Data Cleaning and Preprocessing Techniques

12 -Visualising Data with Histograms and Box Plots

13 -Summary Statistics and Variable Relationships

14 -Correlation and Pair Plots

15 -Handling High Correlation and Using Heat Maps

6 - Foundations of Time Series Analysis

1 -Introduction to Time Series Data

2 -Understanding Time Series Components

3 -Stationarity and Its Importance

4 -ARIMA Model Fundamentals

5 -Building and Evaluating ARIMA Models

6 -Seasonal Time Series and Decomposition

7 -Probability Distributions in Time Series

8 -Descriptive Statistics and Exploratory Data Analysis

9 -Hypothesis Testing and Confidence Intervals

10 -Forecasting with ARIMA Models

11 -Model Selection and Evaluation

12 -Practical Forecasting and Model Improvement

13 -Data Visualization for Time Series

14 -Time Series in Python Practical Implementation

15 -Real-world Case Studies and Applications

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تولید کننده: Udemy