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Python and Machine Learning Training Course

Module-1: Introduction to Machine Learning

Introduction to Machine Learning (9:53)
Steps of Machine learning (15:15)
Supervised, Unsupervised Learning & Reinforcement Learning in Machine Learning (10:39)
Supervised vs unsupervised learning (4:03)
Workflow & Types of Supervised Learning (12:27)
Types of Regression Algorithms (10:19)
Types of Classification Algorithms (10:51)
Workflow of Unsupervised Learning (7:00)
Categories of unsupervised learning (10:28)
How Reinforcement Learning Works (7:38)
Types of Reinforcement Learning (6:31)
Markov Decision Process (4:33)

Module-2: Introduction to Python Programming

Introduction to Python Programming (6:56)
Installation of Python on Windows, Linux, Mac, Docker, etc (7:10)
How to access & work with Python online (repilit, https://www.online-python.com, google collab) (4:06)
Understanding Python environment (Pip, Conda, Venv) (20:14)
Setting up Python development environment (15:21)
Git & Github (11:53)
Your first Python program (10:29)

Module-3: Basics of Python

Python Identifiers, Keywords (6:33)
Python Variables (9:30)
Python Data Types (18:30)
Python naming conventions and coding standards (18:04)
Document Interlude in Python (9:55)
Understanding of operators in Python (22:52)
Python functions (User-defined and built-in functions in Python) (17:52)
Working with Conditional Statements in Python (16:02)
Working with Looping Statements in Python (18:38)
Working with Jump Statements (9:08)
Working with Python String (26:31)
Working with Python List (27:01)
Working with Tuple in Python (20:43)
String vs List vs Tuple (10:22)
Python Dictionaries (26:45)
Python Sets (26:21)
Dictionary vs Sets (11:36)
Python Regular Expression (19:59)
Modules in Python (12:07)
Packages in Python (10:27)

Module-4: Exception handling in Python

Python Errors (6:19)
Understanding Various Python Exceptions (8:37)
Exception Handling in Python (16:55)

Module-5: Working with Files in Python

Working with files in Python (28:50)
Working with Binary Files (12:44)
Working with Docx In Python (15:32)
Working with PDF in Python (29:07)
Working with CSV files in Python (17:15)
Convert one file to another in Python (10:35)
Convert PDF to Docx in Python (11:24)

Module-6: Object Oriented Programming in Python

Overview of OOPs (12:56)
Private, Public, and self-variables (9:56)
Functions or Methods (6:32)
Constructor & Destructor (6:26)
Abstraction & Encapsulation (10:54)
Inheritance and multiple inheritance (5:44)
Polymorphism (8:09)

Module-7: Python Multithreaded Programming

What is multithreading? (11:19)
Multiprocessing vs Multi-Threading (2:48)
Thread Synchronization (9:57)
Daemon Threads (4:58)
Deadlock of Threads & Avoiding Deadlocks (7:18)

Module-8: Using Databases in Python

Working with MySQL in Python (8:35)
Working with SQLite3 in Python (5:39)
Working with PostgreSQL in Python (8:09)
CREATE, INSERT, READ, Delete Operation in Python (11:04)
DDL Operation with Database in Python (8:52)

Module-9: Introduction to Tkinter Library

Introduction to Tkinter in Python (3:55)
Installation of Tkinter (2:14)
Termonologies of Tkinter (2:08)
First GUI Program using Python Tkinter (8:02)

Module-10: Geometry Manager in Tkinter

Introduction to Geometry Manager in Python Tkinter (7:24)
Working with pack geometry manager (18:50)
Grid geometry manager (10:06)
Place geometry manager (14:48)
Pack vs grid vs place (7:12)

Module-11: Widgets in Tkinter

Display Widget in Python Tkinter (14:34)
Entry Widget in Python Tkinter (18:25)
Text Input Widget in Tkinter (22:10)
Scrollbar Input Widget in Python Tkinter (7:27)
Scale Input Widget in Python Tkinter (7:16)
Spinbox Input widget (4:52)
Canvas Input Widget (15:59)
Container Widgets in Python Tkinter (19:04)
Action buttons - button, radiobutton in Python Tkinter (18:11)
Action button - checkbutton, menubutton in Python Tkinter (13:05)
Filedialog Widgets in Python Tkinter (19:17)
Messagebox Widgets in Python Tkinter (5:05)
Combobox Widget in Python Tkinter (7:34)
ColorChooser in Python Tkinter (6:06)
Treeview Widget in Python Tkinter (8:37)
Progressbar Widget in Python Tkinter (9:26)
Notebook widget in Tkinter (13:15)
Seperator in Tkinter (4:50)
Sizegrip Widget in Tkinter (3:33)
Images in Tkinter (9:26)
Working with Python-Docx with Python Tkinter (20:53)
Working with PyPDF2 with Python TKinter (39:33)

Module-12: Tkinter with Object Oriented Programming

Tkinter Application Using Object Oriented Programing (8:02)
Notepad application Using Python Tkinter (22:27)

Module-13: Packaging and Distributing Executables

Overview of Pyinstaller (2:22)
Convert PY to exe using console (4:13)
Convert PY to exe using without console (2:57)
Pack the exe to distributable file (4:01)

Module-14: Introduction to Python Turtle

Introduction to Turtle (3:27)
Getting started with turtle (5:11)
Programming with turtle (3:31)
Change Turtle Size & Shape (5:21)
Drawing Shapes and Preset Figures (6:09)
Working with Pen in Turtle (7:33)
Turtle Fill color and clear screen (7:25)
Resetting the Environment, leaving a Stamp, and Cloning Our Turtle (5:39)

Module-15: Using loops and conditional statement in Turtle

Working with Turtle using Loops (5:40)
Conditional Statements in Python Turtle (5:05)
Game Project from Scratch (7:40)

Module-16: Django introduction and installation

Introduction to web framework, Django and Features (10:10)
Installation of Django using PIP (9:43)
Installation of Django using Conda (5:13)
Creating a Project & Application in Django (12:05)
Setting up Database in Django (12:28)
Various useful Django Commands (10:33)

Module-17: Dynamic Web Pages in Django

View in Django (12:26)
URL Configuration & Loose Coupling in Django (5:36)
404 Error in Django (9:10)
Dynamic URLs in Django (9:32)
Django Pretty Error Pages (6:10)

Module-18: Template System in Django

Introduction to Django Template System (6:54)
Multiple contexts, Template tags & Filters (21:31)
render(), render_to_response(), & locals (8:00)
The include Template Tag (6:00)
Template Inheritance (9:15)
How to write your own Context Processors (6:55)
Writing Custom Template Filters and Tags (10:44)

Module-19: Models in Django

Understanding models in Django (11:46)
Adding model string representations (9:06)
Insert, update, select, delete and filter data objects (13:07)
Ordering, slicing, chaining lookups data objects (11:19)
Add, update & remove fields (7:36)

Module-20: Django Administration

When and Why use Admin Interface in Django (15:58)
Working with users, groups, and permissions in Django (25:30)
How to customize the admin interface in Django (19:31)
Customizing the admin index page (22:47)

Module-21: Forms in Django

Creating a feedback form in Django (14:11)
Processing the submission of forms (13:17)
Customizing forms in Django (14:54)
Validation rules for forms in Django (26:49)
Creating forms from models in Django (18:28)

Module-22: Generic, Advanced Views and URL configurations in Django

Introduction to generic views and its objects (11:26)
Extending generic views (26:38)
Template contexts (14:29)
Viewing subsets of objects (11:32)
Complex filtering (11:45)
URL configuration tips in Django (5:39)
Working with Named Groups (16:58)
Using Default View Arguments (9:31)
Capturing Text in URLs in Django (6:04)
Including Other URL configurations (17:56)
How Captured Parameters Work with include() (5:19)

Module-23: Users and Registration

Understanding how cookies works in Django (2:53)
Setting up cookies (9:38)
Users and authentication (3:44)
Enabling authentication support (6:28)
Logged-in users access control (10:20)
Managing users and messages (23:28)
Creating profile and password (26:32)
Managing profile (31:21)

Module-25: Introduction to Pandas

Pandas Introduction (2:14)
Installation of Pandas using package manager (12:12)
Installation of pandas (9:47)
Dataset for data Analysis (2:14)

Module-26: Basics of Pandas

Series & DataFrame in Pandas (9:39)
Import and Export CSVs, Excel and URLs in Pandas (8:32)
Head & Tail in Pandas (2:16)

Module-27: Operation in Pandas

Selecting, Viewing and Describing data in Pandas (15:11)
Slicing dataframe using loc and iloc (5:36)
Create & delete Operations in Pandas (7:29)
Merge & concat operations in pandas (8:03)
Handling Missing Values in Pandas (11:20)
Data manipulation using group by and crosstab (11:05)
Working with date and time (4:18)
Operations and visualization of dataframe (9:49)

Module-28: Project on Data Analysis using Python

Google Play Store Apps (23:55)

Module-29: Introduction to Python NumPy

Python NumPy introduction (3:49)
Install and setup NumPy (6:36)
Different ways to create arrays in NumPy (10:26)
NumPy data types and attributes (10:00)
Working with NumPy arrays (5:42)
String arrays in NumPy (16:43)
Basic slicing Nd arrays (5:12)
Copy, Arange and random in NumPy (14:15)
Numpy arithmetic operations (10:52)
Logical and comparison operators in NumPy (13:10)
Mathematical Functions in NumPy (10:54)

Module-30: Advanced Operations in NumPy

Working with Linear algebra module (11:27)
NumPy Array Manipulation (15:49)
NumPy Statistcal Functions (14:39)
NumPy Counting Functions (8:11)
Working of Universal Functions (13:04)
Numpy Matrix Library (9:30)
NumPy Histogram Using Matplotlib (8:52)
Scaler Objects in NumPy (5:20)
Basic and advanced indexing on NumPy Arrays (7:02)
Character Arrays in Python NumPy (6:42)
Sorting Arrays Using NumPy (6:34)
Record Arrays in NumPy (8:30)
Masked Arrays in NumPy Python (6:34)
Working with File IO with Python NumPy (12:18)

Module-31: Overview of Matplotlib

Introduction to Matplotlib (4:17)
Installation of Matplotlib (Pip Package) (8:39)
Installation of Matplotlib (Conda Package) (7:13)
How to start with Matplotlib (19:22)
Add title, labels, legend, grids, handling axes (15:00)
Save Plot in default formats (12:08)
Working with backend (15:46)
Working with color properties (7:41)
Working with line properties (10:14)
Handling ticks in Matplotlib (8:15)

Module-32: Working with different type of plots in Matplotlib

Multiple Lines Chart using Matplotlib (4:48)
Basic Bar Chart (16:59)
Stacked Bar Chart (12:05)
Grouped Bar Chart (11:18)
Histogram (26:38)
Scatter Plot (11:15)
Pie Chart in Matplotlib (18:46)
Donut Chart in Matplotlib (8:00)
Error Bars (13:38)
Polar Chart (6:37)
Radial and Angular Grid (5:38)
Quiver Plot (8:57)
Contour plot (7:50)
Date plot (9:17)
Text in figure in matplotlib (7:29)
Text function and fonts (9:12)
LaTeX Formatting (16:45)
Annotations and Arrows (12:31)
Subplots (26:02)
Multiple Figures (5:59)
Twin Axes in Matplotlib (10:05)
Logarithmic Axes in Matplotlib (15:55)
Share Axis in Matplotlib (6:52)

Module-33: Statistical and Three-Dimensional Charts

Autocorrelation in Matplotlib (5:42)
Box Plot in Matplotlib (8:44)
Violin Plot in Python Matplotlib (5:33)
Heatmap in Python Matplotlib (6:43)
Image Plotting in Matplotlib (11:01)
Colorbar in Matplotlib (7:30)
Basic 3D Plots in Matplotlib (12:37)
Advance 3D Plots in Matplotlib (11:47)

Module-34: Plotting Data From DataSource

Plotting Data from Pandas dataframe (12:28)
Plotting Data from CSV (14:40)
Plotting Data from Database MySQL (16:03)
Plotting Data from Database MariaDB (12:22)
Plotting Data from Database SQLite (6:52)

Module-35: Embedding Matplotlib in GUI (PyQt5, Tkinter, Django, wxFrame)

Embedding matplotlib figure in PyQt5 (10:41)
Embedding matplotlib figure in Tkinter (8:20)
Embedding matplotlib figure in Django (15:08)
Embedding matplotlib figure in wxFrame (6:04)

Module-36: Working with Seaborn Library

Introduction to Seaborn (4:52)
Installation of Seaborn (Using Pip) (10:20)
Installation of Seaborn (Using Conda) (8:03)
Relational Plot (relplot()) (17:37)
Relational Plot (scatterplot()) (7:53)
Relational Plot (lineplot()) (8:06)
Categorical Plot ( Catplot, Barplot, Countplot, Boxplot) (3:44)
Categorical Plot ( Violinplot, Stripplot, Swarmplot, Factorplot) (20:51)
Distribution Plot (Displot) (15:40)
Distribution Plot (Pairplot, Jointplot) (14:16)
Distribution Plot (Rugplot) (7:30)
Regression Plot (23:54)
Matrix Plot (18:28)
Multi-plot grids (18:28)

Module-37: Introduction to scikit learn

Introduction to Scikit Learn (3:20)
Scikit learn installation (Windows, MacOS, Linux) (4:48)
How to implement Scikit learn workflow (4:09)
Modeling data in Scikit learn (6:27)
Convert data into numbers (6:16)
Handle missing value using Simple Imputer (6:19)
Fitting and Predicting the model in scikit learn (6:01)
Evaluation of Model in Scikit Learn (14:54)
Improvement of Model in Scikit Learn (5:41)

Module-38: Scikit learn supervised methods

Naive Bayes: Bernoulli - Multinomial (10:08)
Logistic regression (3:52)
Linear regression (6:48)
Support Vector Machines (SVM) (3:24)
Decision trees (5:31)
Ensemble Method in Scikit learn (9:30)

Module-39: Scikit learn unsupervised methods

Density Estimation (5:14)
Principal Component Analysis (3:28)
K-Means (4:24)
DBSCAN (4:13)
Clustering (5:49)
Outlier Detection (6:46)
Novelty detection (4:16)

Module-40: Overview of PyTorch

Introduction to PyTorch (4:15)
Installation of PyTorch (5:23)
How to start with PyTorch (5:38)
TensorFlow vs PyTorch (4:24)
One Dimensional Tensor (7:26)
Vector Operations in PyTorch (6:06)
Two Dimensional Tensors (5:31)
Slicing Three Dimensional Tensor (5:12)
Matrix Multiplication in PyTorch (10:11)
Gradient with PyTorch (5:09)

Module-41: Linear Regression in PyTorch

Understanding of Linear Regression in PyTorch (4:18)
Making Predictions and Linear class in PyTorch (5:52)
Custom Module in PyTorch (4:07)
Loss Function in PyTorch (6:37)
Gradient Decent in PyTorch (5:36)
Mean Squared Error in PyTorch (5:27)

Module-42: Preceptron in PyTorch

What is Deep Learning in PyTorch (8:44)
Creating dataset (4:09)
Model Setup (5:54)
Model Training (5:18)
Model Testing (6:07)

Module-43: Convolution & Deep Neural Network in PyTorch

Convolutions and MNIST (5:43)
Convolutional Layer (7:29)
Pooling (5:52)
Fully Connected Layer (3:35)
Non-Linear Boundary (3:57)
Feedforward Process in PyTorch (5:36)
Backpropagation in PyTorch (4:42)
Testing Mode in PyTorch (5:27)

Module-44: Image Recognition in PyTorch

MNIST Dataset in PyTorch (3:29)
Image Transforms (4:25)
Neural Network Implementation (5:41)
Neural Network Validation (5:37)

Module-45: CIFAR10 Classification in PyTorch

The CIFAR 10 Dataset (3:34)
Hyperparameter Tuning (8:00)
Data Augmentation (8:06)

Module-46: Overview of TensorFlow

Introduction to TensorFlow (5:16)
Installation of TensorFlow (7:54)
How to start with TensorFlow (6:12)
Various Operations of TensorFlow (17:05)
Slicing and Indexing of Tensor (8:55)
TensorFlow vs NumPy (6:37)
Linear Regression in TensorFlow (8:23)

Module-47: Convolutional and Recurrent Neural Networks

Implementation of Convolutional Neural Network (7:36)
Recurrent Neural Network implementation with TensorFlow (8:37)
CNN AND RNN Difference in TensorFlow (7:18)
A predicted model for time series data by using the RNN in TensorFlow (5:56)

Module-48: Perception in TensorFlow

Artificial Neural Network in TensorFlow (7:17)
Single Layer Perception (8:17)
Multi layer Perception (6:31)
Optimizer in TensorFlow (11:42)
Recommendations for Neural Network Training (8:41)
TensorFlow XOR implementation (5:16)

Module-49: TensorBoard Visualization

TensorBoard Visualization (7:35)
TensorFlow Graph Visualization Using TensorBoard (5:49)
How to visualize models, data, and training with TensorBoard (5:55)
Hyperparameter Tuning with the HParams Dashboard (6:35)
Graph and loss visualization using TensorBoard (4:20)

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