Autoplay
Autocomplete
Previous Lecture
Complete and Continue
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
Supervised vs unsupervised learning
Workflow & Types of Supervised Learning
Types of Regression Algorithms
Types of Classification Algorithms
Workflow of Unsupervised Learning
Categories of unsupervised learning
How Reinforcement Learning Works
Types of Reinforcement Learning
Markov Decision Process
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-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
Notepad application Using Python Tkinter
Module-13: Packaging and Distributing Executables
Overview of Pyinstaller
Convert PY to exe using console
Convert PY to exe using without console
Pack the exe to distributable file
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
Installation of Django using Conda
Creating a Project & Application in Django
Setting up Database in Django
Various useful Django Commands
Module-17: Dynamic Web Pages in Django
View in Django
URL Configuration & Loose Coupling in Django
404 Error in Django
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
Module-21: Forms in Django
Creating a feedback form in Django
Processing the submission of forms
Customizing forms in Django
Validation rules for forms in Django
Creating forms from models in Django
Module-22: Generic, Advanced Views and URL configurations in Django
Introduction to generic views and its objects
Extending generic views
Template contexts
Viewing subsets of objects
Complex filtering
URL configuration tips in Django
Working with Named Groups
Using Default View Arguments
Capturing Text in URLs in Django
Including Other URL configurations
How Captured Parameters Work with include()
Module-25: Introduction to Pandas
Pandas Introduction (2:14)
Installation of Pandas using package manager (12:12)
Installation of pandas
Dataset for data Analysis
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
Slicing dataframe using loc and iloc
Create & delete Operations in Pandas
Merge & concat operations in pandas
Handling Missing Values in Pandas
Data manipulation using group by and crosstab
Working with date and time
Operations and visualization of dataframe
Module-28: Project on Data Analysis using Python
Google Play Store Apps
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
Working of Universal Functions (13:04)
Numpy Matrix Library
NumPy Histogram Using Matplotlib
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-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
Model Setup
Model Training
Model Testing
Module-43: Convolution & Deep Neural Network in PyTorch
Convolutions and MNIST
Convolutional Layer
Pooling
Fully Connected Layer
Non-Linear Boundary
Feedforward Process in PyTorch
Backpropagation in PyTorch
Testing Mode in PyTorch
Module-44: Image Recognition in PyTorch
MNIST Dataset in PyTorch
Image Transforms
Neural Network Implementation
Neural Network Validation
Module-45: CIFAR10 Classification in PyTorch
The CIFAR 10 Dataset
Hyperparameter Tuning
Data Augmentation
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
Multi layer Perception
Optimizer in TensorFlow
Recommendations for Neural Network Training
TensorFlow XOR implementation
Module-49: TensorBoard Visualization
TensorBoard Visualization
TensorFlow Graph Visualization Using TensorBoard
How to visualize models, data, and training with TensorBoard
Hyperparameter Tuning with the HParams Dashboard
Graph and loss visualization using TensorBoard
Working with NumPy arrays
Lecture content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock