Module-1: Introduction to Machine Learning
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Introduction to Machine Learning (9:53)
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Steps of Machine learning (15:15)
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Supervised, Unsupervised Learning & Reinforcement Learning in Machine Learning (10:39)
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Supervised vs unsupervised learning (4:03)
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Workflow & Types of Supervised Learning (12:27)
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Types of Regression Algorithms (10:19)
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Types of Classification Algorithms (10:51)
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Workflow of Unsupervised Learning (7:00)
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Categories of unsupervised learning (10:28)
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How Reinforcement Learning Works (7:38)
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Types of Reinforcement Learning (6:31)
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Markov Decision Process (4:33)
Module-2: Introduction to Python Programming
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Introduction to Python Programming (6:56)
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Installation of Python on Windows, Linux, Mac, Docker, etc (7:10)
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How to access & work with Python online (repilit, https://www.online-python.com, google collab) (4:06)
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Understanding Python environment (Pip, Conda, Venv) (20:14)
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Setting up Python development environment (15:21)
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Git & Github (11:53)
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Your first Python program (10:29)
Module-3: Basics of Python
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Python Identifiers, Keywords (6:33)
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Python Variables (9:30)
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Python Data Types (18:30)
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Python naming conventions and coding standards (18:04)
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Document Interlude in Python (9:55)
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Understanding of operators in Python (22:52)
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Python functions (User-defined and built-in functions in Python) (17:52)
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Working with Conditional Statements in Python (16:02)
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Working with Looping Statements in Python (18:38)
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Working with Jump Statements (9:08)
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Working with Python String (26:31)
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Working with Python List (27:01)
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Working with Tuple in Python (20:43)
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String vs List vs Tuple (10:22)
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Python Dictionaries (26:45)
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Python Sets (26:21)
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Dictionary vs Sets (11:36)
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Python Regular Expression (19:59)
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Modules in Python (12:07)
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Packages in Python (10:27)
Module-4: Exception handling in Python
Module-5: Working with Files in Python
Module-6: Object Oriented Programming in Python
Module-7: Python Multithreaded Programming
Module-8: Using Databases in Python
Module-9: Introduction to Tkinter Library
Module-10: Geometry Manager in Tkinter
Module-11: Widgets in Tkinter
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Display Widget in Python Tkinter (14:34)
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Entry Widget in Python Tkinter (18:25)
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Text Input Widget in Tkinter (22:10)
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Scrollbar Input Widget in Python Tkinter (7:27)
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Scale Input Widget in Python Tkinter (7:16)
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Spinbox Input widget (4:52)
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Canvas Input Widget (15:59)
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Container Widgets in Python Tkinter (19:04)
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Action buttons - button, radiobutton in Python Tkinter (18:11)
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Action button - checkbutton, menubutton in Python Tkinter (13:05)
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Filedialog Widgets in Python Tkinter (19:17)
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Messagebox Widgets in Python Tkinter (5:05)
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Combobox Widget in Python Tkinter (7:34)
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ColorChooser in Python Tkinter (6:06)
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Treeview Widget in Python Tkinter (8:37)
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Progressbar Widget in Python Tkinter (9:26)
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Notebook widget in Tkinter (13:15)
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Seperator in Tkinter (4:50)
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Sizegrip Widget in Tkinter (3:33)
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Images in Tkinter (9:26)
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Working with Python-Docx with Python Tkinter (20:53)
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Working with PyPDF2 with Python TKinter (39:33)
Module-12: Tkinter with Object Oriented Programming
Module-13: Packaging and Distributing Executables
Module-14: Introduction to Python Turtle
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Introduction to Turtle (3:27)
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Getting started with turtle (5:11)
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Programming with turtle (3:31)
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Change Turtle Size & Shape (5:21)
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Drawing Shapes and Preset Figures (6:09)
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Working with Pen in Turtle (7:33)
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Turtle Fill color and clear screen (7:25)
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Resetting the Environment, leaving a Stamp, and Cloning Our Turtle (5:39)
Module-15: Using loops and conditional statement in Turtle
Module-16: Django introduction and installation
Module-17: Dynamic Web Pages in Django
Module-18: Template System in Django
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Introduction to Django Template System (6:54)
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Multiple contexts, Template tags & Filters (21:31)
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render(), render_to_response(), & locals (8:00)
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The include Template Tag (6:00)
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Template Inheritance (9:15)
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How to write your own Context Processors (6:55)
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Writing Custom Template Filters and Tags (10:44)
Module-19: Models in Django
Module-20: Django Administration
Module-21: Forms in Django
Module-22: Generic, Advanced Views and URL configurations in Django
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Introduction to generic views and its objects (11:26)
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Extending generic views (26:38)
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Template contexts (14:29)
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Viewing subsets of objects (11:32)
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Complex filtering (11:45)
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URL configuration tips in Django (5:39)
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Working with Named Groups (16:58)
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Using Default View Arguments (9:31)
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Capturing Text in URLs in Django (6:04)
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Including Other URL configurations (17:56)
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How Captured Parameters Work with include() (5:19)
Module-23: Users and Registration
Module-25: Introduction to Pandas
Module-26: Basics of Pandas
Module-27: Operation in Pandas
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Selecting, Viewing and Describing data in Pandas (15:11)
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Slicing dataframe using loc and iloc (5:36)
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Create & delete Operations in Pandas (7:29)
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Merge & concat operations in pandas (8:03)
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Handling Missing Values in Pandas (11:20)
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Data manipulation using group by and crosstab (11:05)
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Working with date and time (4:18)
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Operations and visualization of dataframe (9:49)
Module-28: Project on Data Analysis using Python
Module-29: Introduction to Python NumPy
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Python NumPy introduction (3:49)
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Install and setup NumPy (6:36)
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Different ways to create arrays in NumPy (10:26)
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NumPy data types and attributes (10:00)
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Working with NumPy arrays (5:42)
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String arrays in NumPy (16:43)
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Basic slicing Nd arrays (5:12)
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Copy, Arange and random in NumPy (14:15)
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Numpy arithmetic operations (10:52)
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Logical and comparison operators in NumPy (13:10)
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Mathematical Functions in NumPy (10:54)
Module-30: Advanced Operations in NumPy
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Working with Linear algebra module (11:27)
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NumPy Array Manipulation (15:49)
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NumPy Statistcal Functions (14:39)
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NumPy Counting Functions (8:11)
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Working of Universal Functions (13:04)
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Numpy Matrix Library (9:30)
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NumPy Histogram Using Matplotlib (8:52)
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Scaler Objects in NumPy (5:20)
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Basic and advanced indexing on NumPy Arrays (7:02)
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Character Arrays in Python NumPy (6:42)
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Sorting Arrays Using NumPy (6:34)
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Record Arrays in NumPy (8:30)
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Masked Arrays in NumPy Python (6:34)
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Working with File IO with Python NumPy (12:18)
Module-31: Overview of Matplotlib
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Introduction to Matplotlib (4:17)
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Installation of Matplotlib (Pip Package) (8:39)
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Installation of Matplotlib (Conda Package) (7:13)
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How to start with Matplotlib (19:22)
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Add title, labels, legend, grids, handling axes (15:00)
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Save Plot in default formats (12:08)
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Working with backend (15:46)
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Working with color properties (7:41)
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Working with line properties (10:14)
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Handling ticks in Matplotlib (8:15)
Module-32: Working with different type of plots in Matplotlib
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Multiple Lines Chart using Matplotlib (4:48)
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Basic Bar Chart (16:59)
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Stacked Bar Chart (12:05)
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Grouped Bar Chart (11:18)
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Histogram (26:38)
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Scatter Plot (11:15)
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Pie Chart in Matplotlib (18:46)
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Donut Chart in Matplotlib (8:00)
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Error Bars (13:38)
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Polar Chart (6:37)
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Radial and Angular Grid (5:38)
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Quiver Plot (8:57)
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Contour plot (7:50)
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Date plot (9:17)
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Text in figure in matplotlib (7:29)
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Text function and fonts (9:12)
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LaTeX Formatting (16:45)
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Annotations and Arrows (12:31)
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Subplots (26:02)
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Multiple Figures (5:59)
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Twin Axes in Matplotlib (10:05)
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Logarithmic Axes in Matplotlib (15:55)
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Share Axis in Matplotlib (6:52)
Module-33: Statistical and Three-Dimensional Charts
Module-34: Plotting Data From DataSource
Module-35: Embedding Matplotlib in GUI (PyQt5, Tkinter, Django, wxFrame)
Module-36: Working with Seaborn Library
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Introduction to Seaborn (4:52)
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Installation of Seaborn (Using Pip) (10:20)
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Installation of Seaborn (Using Conda) (8:03)
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Relational Plot (relplot()) (17:37)
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Relational Plot (scatterplot()) (7:53)
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Relational Plot (lineplot()) (8:06)
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Categorical Plot ( Catplot, Barplot, Countplot, Boxplot) (3:44)
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Categorical Plot ( Violinplot, Stripplot, Swarmplot, Factorplot) (20:51)
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Distribution Plot (Displot) (15:40)
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Distribution Plot (Pairplot, Jointplot) (14:16)
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Distribution Plot (Rugplot) (7:30)
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Regression Plot (23:54)
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Matrix Plot (18:28)
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Multi-plot grids (18:28)
Module-37: Introduction to scikit learn
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Introduction to Scikit Learn (3:20)
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Scikit learn installation (Windows, MacOS, Linux) (4:48)
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How to implement Scikit learn workflow (4:09)
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Modeling data in Scikit learn (6:27)
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Convert data into numbers (6:16)
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Handle missing value using Simple Imputer (6:19)
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Fitting and Predicting the model in scikit learn (6:01)
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Evaluation of Model in Scikit Learn (14:54)
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Improvement of Model in Scikit Learn (5:41)
Module-38: Scikit learn supervised methods
Module-39: Scikit learn unsupervised methods
Module-40: Overview of PyTorch
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Introduction to PyTorch (4:15)
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Installation of PyTorch (5:23)
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How to start with PyTorch (5:38)
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TensorFlow vs PyTorch (4:24)
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One Dimensional Tensor (7:26)
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Vector Operations in PyTorch (6:06)
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Two Dimensional Tensors (5:31)
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Slicing Three Dimensional Tensor (5:12)
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Matrix Multiplication in PyTorch (10:11)
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Gradient with PyTorch (5:09)
Module-41: Linear Regression in PyTorch
Module-42: Preceptron in PyTorch
Module-43: Convolution & Deep Neural Network in PyTorch
Module-44: Image Recognition in PyTorch
Module-45: CIFAR10 Classification in PyTorch
Module-46: Overview of TensorFlow
Module-47: Convolutional and Recurrent Neural Networks
Module-48: Perception in TensorFlow
Module-49: TensorBoard Visualization
