MACHINE LEARNING MASTER CLASS, AI MADE EASY

MACHINE LEARNING MASTER CLASS, AI MADE EASY (ZERO TO HERO!!)

Master Machine Learning effortlessly with Python! This beginner-friendly course offers step-by-step AI tutorials, hands-on ML projects, and clear explanations, guiding you from zero to hero in Machine Learning. Start your AI journey today!

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Course curriculum

1. en - About the author Take 2
2. en - Connect on linkedin
3. en - Discussion forum
4. en - Facebookgroup
5. en - Project file
6. en - S01L03 - Short History of ML
7. en - S01L04 - Pre-requisits
8. en - S01L05 - Review and rating
1. en - S02L01 - Short History of Python
2. en - S02L02 - Python 2 Vs 3
3. en - S02L03 - Python IDE_s options
4. en - S02L04 - Anaconda navigator and IDE_s
5. en - S02L05 - Jupyter notebook, Google Colab
6. en - S02L06 - PyCharm and VS Code
7. en - S02L07 - Virtual environment
1. en - S03L01 - Data types
2. en - S03L02 - Python numbers
3. en - S03L03 - Variables and assignment
4. en - S03L04 - String basics
5. en - S03L05 - String Start Stop and Step
6. en - S03L06 - String slicing
7. en - S03L07 - String formatting
8. en - S03L08 - Lists in Python
9. en - S03L09 - List shorting, reversing, removing, clear, list of list
10. en - S03L10 - Sets
11. en - S03L11 - Tuples
12. en - S03L12 - Dictionary in python
13. en - S03L13 - None and Bool
14. en - S03L14 - Comparison operators
15. en - S03L15 - Logical operators
1. en - S04L01 - If ElIf _ else
2. en - S04L02 - while loop
3. en - S04L03 - For loop
4. en - S04L04 - Tuple unpacking
5. en - S04L05 - Break, continue and pass
6. en - S04L06 - Range, enumerate and zip
7. en - S04L07 - In python
8. en - S04L08 - input and import
1. en - S05L01 - User-defined functions
2. en - S05L02- Help function
3. en - S05L03 - Scopes
4. en - S05L04 - args and kwargs
5. en - S05L05 - Maps, Filters and Lambdas
6. en - S05L06 - Lambda once again
1. en - S06L01-Python Packages
2. en - S06L02 - User defined packages
3. en - S06L03 - User defined packages continues
1. en - S07L01 - Naming conventions and introduction
2. en - S07L02 - Class attributes and Methods
3. en - S07L03 - Inheritance
4. en - S07L04 - Multiple, multi level inheritance and MRO
5. en - S07L05 - Polymorphism
6. en - S07L06 - Special class methods
1. en - S08L01 - Try except finally
2. en - S08L02 - Error types, else and finally
1. en - S09L01 - Python decorators
2. en - S09L02 - Class method decorator
3. en - S09L03 - Python Generators
1. en - S12L01 - Regular expression introduction
2. en - S12L02 - Regular expression, grouping and pipe
3. en - S12L03 - Repetition and range
4. en - S12L04 - Greedy, non-greedy matches and findall
5. en - S12L05 - BeginsWith endsWith and dot character
6. en - S12L06 - BeginsWith endsWith and dot character continues
7. en - S12L07 - Sets
8. en - S12L08 - Literal matching, Sub and verbose
1. en - S13L01 - Files introduction
2. en - S13L02 - Paths
3. en - S13L03 - Read mode, write mode and methods
4. en - S13L03 - Read mode, write note and methods
1. en - S14L01 - Setting up
2. en - S14L03 - NumPy array fuctions - Array generate
3. en - S14L04 - Random array based methods
4. en - S14L05 - Slicing and broadcast
5. en - S14L06 - Matrices selection and conditional selection
6. en - S14L07 - Numpy operations
1. en - S15L01 - Panda series
2. en - S15l02 - DataFrame introduction
3. en - S15L03 - DataFrame Selections
4. en - S15L04 - GroupBy and Concatenation
5. en - S15L05 - Concatenation
6. en - S15L06 - Operations
1. en - S17L01 - Python random class
2. en - S17L02 - Random under numpy and Arange
3. en - S17L03 - Python collections
4. en - S17L04 - Python counter from collections
5. en - S17L05 - Math Matrix maltiplication
1. en - S18L01 - Matplotlib crash course (Part 01)
2. en - S18L02 - Matplotlib crash course (Part 02)
3. en - S18L03 - Matplotlib - Subplot and histogram
4. en - S18L04 - Matplotlib Scatter plots and Pie charts
5. en - S18L05 - Matplotlib 3D statter and simple plot
6. en - S18L06 - Matpotlib Wireframe surface ploting
7. Project files/Notebooks for the section 18
1. en - S02L01-Introduction to ML _ Supervised Learning
2. en - S02L02-Unsupervised Learning
3. en - S02L03 - Type of Data
4. en - S02L04 - Mean, Median, Mode
5. en - S02L05 - Standard Deviation
6. en - S02L06 - Most common data distributions
7. en - S02L07 - Percentiles, moment and Quantiles
1. en - S03L00.YT-Autocomplete on jupyter notebook
2. en - S03L01 - Scatter plot on Iris dataset
3. en - S03L02 - Pair plot and limitations
4. en - S03L03-Tips Dataset
5. en - S03L04 - Seaborn plots
6. en - S03L05 - Facetgrid plots
7. en - S03L06 - Univariate Analysis using PDF
8. en - S03L07 - Boxplot and Violin Plot
9. en - S03L08 - HeatMap
1. en - S04L01-Matrices
2. en - S04L02 - Matrix operations and scalar operations
3. en - S04L03-Matrix Multiplication
4. en - S04L04 - Vector multiplication
5. en - S04L05-Identity matrix, matrix inverse properties, transpose of matrix
1. en - S05L01-Data Import
2. en - S05L02 - handling missing data
3. en - S05L03 - Feature selection and Encoding categorical data
4. en - S05L04 - Test and train data split and Feature scaling
5. en - S05L05 - Under and over sampling
6. en - S05L06 - Assignment and Tips
7. en - S05L07 - Assignment solution and OneHotEncoding - Part 01
8. en - S05L08 - Assignment solution and OneHotEncoding - Part 02
1. en - S06L01 - Linear regression working and Cost function
2. en - S06L02 - Linear regression implementation in python - Part 1
3. en - S06L03 - Linear regression implementation in python - Part 2
1. en - S07L01 - Multiple linear regression in Python
2. en - S07L02 - Multiple linear regression behind the scene - Part 1
3. en - S07L03 - Multiple linear regression behind the scene - Part 2
1. en - S08L01 - Polynomial regression
2. en - S08L02 - Polynomial regression on multiple feature dataset
1. en - S09L01 - Bias, Variance and overfitting
2. en - S09L02 - Gradient decent - Background
3. en - S09L03 - Gradient descent in 2D and 3D space
1. en - S10L01 - Measuring Entropy and Gini
2. en - S10L02 - Decision Tree implementation - 1 feature
3. en - S10L03 - Visualization of decision tree model
4. en - S10L04 - Decision Tree implementation - multiple features
1. en - S11L01 - Ensemble Learning
2. en - S11L02 - Random Forest
1. en - S12L01 - Bagging
2. en - S12L02 - Boosting
1. en - S13L01 - AdaBoost and XGBoost regressor
1. en - S14L01 - SVM (regression) Background
2. en - S14L02 - SVR under Python
1. en - S15L01 -R-square
2. en - S15L02 - Adjusted R-Square
1. en - S16L01 - Master template regression model - Data creation
2. en - S16L02 - Master template regression model - Models and evaluation
1. en - S17L01 - K-Fold validation, GridSearch
2. en - S17L02 - Updated template with GridSearchCV
3. en - S17L03 - K Fold cross validation without GridSearchCV
4. en - S17L04 - K Fold cross validation without GridSearchCV continues
1. en - S18L01 - Why Co-relation is important
2. en - S18L02 - Co-variance
3. en - S18L03 -Co-relation
4. en - S18L04 - Curse of dimensionality
5. en - S18L05 - Pre-processing re-visited
6. en - S18L06 - Pre-processing re-visited continues
7. en - S18L07 -Feature selection
8. en - S18L08 - Short discussion
1. en - S19L01-KNN Background
2. en - S19L02-KNN in python
3. en - S19L03 -Visualization and few more things
4. en - S19L04 -LabelEncoding classes
5. en - S19L05 - KNN on multi class classification
1. en - S20L01 -Why Logistic regression
2. en - S20L02 -Logistic regression background
3. en - S20L03 -Logistic regression under python
4. en - S20L04 -Logistic regression on multi-class classification
5. en - S20L05 -Logistic regression on multi-class classification under python
1. en - S21L01 -Bayes theorem
2. en - S21L02 - Likelihood vs probability, normal distribution
3. en - S21L03 -Multinomial naive bayes
4. en - S21L04 - The log scale
5. en - S21L05 - Gaussian naive bayes
6. en - S21L06 - Gaussian naive bayes under Python
1. en - S22L01 - Euler's number
2. en - S22L02 - Balanced vs imbalanced data
1. en - S23L01 -SVM getting started with 1D data
2. en - S23L02 -SVM, mapping higher dimension
3. en - S23L03 -SVM, in 2D space
4. en - S23L04 -SVM implementation using python
1. en - S24L01 -Decision Tree and Random forest
1. en - S25L01 -AdaBoost and XGBoost classifier
1. en - S26L01 -The accuracy, not so accurate
2. en - S26L02 -Confusion matrix
3. en - S26L03 -Accuracy, precision, recall, Specificity, F1 Score
4. en - S26L04 -Confusion Matrix 3D
1. en - S27L01 - Classification model master template
2. en - S27L02 -Classification model master template
1. en - S28L01 -Updated template with GridSearchCV
2. en - S28L02 -RandomizedSearchCV
1. en - S29L01 -ROC, AUC and PR curve background
2. en - S29L02 -ROC, AUC - Evaluating best model
3. en - S29L03 - ROC, AUC - Calculating the optimal threshold (Youdens method)
4. en - S29L04 - ROC, AUC - Calculating the optimal threshold (best Accuracy method)
5. en - S29L05 -CAP curve background
6. en - S29L06 -CAP curve implementation
7. en - S29L07 -CAP curve with multiple models and multi-class
1. en - S30L01 -Voting classifier
1. en - S31L01 -Model deployment basics
2. en - S31L02 -Prediction using value
1. en - S32L01 - About Flask
2. en - S32L02 - Requirements
3. en - S32L03 - Getting started
4. en - S32L04 - Flask debug mode
5. en - S32L05 - Routes
6. en - S32L06 - Use template for pages
7. en - S32L07 - Visual Code for Flask
8. en - S32L08 - Variables with templates
9. en - S32L09 - Header and footer
10. en - S32L10 -Web template
11. en - S32L11 - Navigation bar
12. en - S32L12 - Getting started with WTForms
13. en - S32L13 - Registration form
14. en - S32L14 - Validation errors messages under Flask
15. en - S32L15 - Bootstrap with Flask
16. en - S32L16 -Validation error messages with bootstrap
17. en - S32L17 - Flask Blueprint design pattern overview
18. en - S32L18 - Blueprint finalization and few fixes
1. en - S33L01 -ML model on Flask App - Part 01
2. en - S33L02 -ML model on Flask App - Part 02
3. en - S33L03 -ML model on Flask App - Part 03
4. en - S33L04 - ML model on Flask App - Part 04
1. en - S34L01 -Basics of recommendation systems
2. en - S34L02 - Preparing the data
3. en - S34L03 - The pivot table
4. en - S34L04 -Extracting correlations
5. en - S34L05 -Providing recommendations
6. en - S34L06 -Testing recommendations
1. en - S35L01 - Getting started with Apriori algorithm
2. en - S35L02 - Market basket optimization
1. en - S36L01 - Unsupervised learning
2. en - S36L02 - K-means clustering
3. en - S36L03 - Optimal clusters
4. en - S36L04 - The Elbow method
5. en - S36L05 - K-means clustering in Python
6. en - S36L06 -Visualization
7. en - S36L07 - Dendrogram
1. en - S37L01-Reinforcement learning - basics
2. en - S37L02 -Bonus video - Reinforcement learning
1. en - S38L01 - Why Reinforcement learning
2. en - S38L02 -Explore only, Exploit only process
3. en - S38L03 -Upper Confidence Bound Algorithm
4. en - S38L04 -Upper Confidence Bound Algorithm continues
1. en - S39L01 -What exactly is NLP
2. en - S39L02 -Text classification using ML, issues
3. en - S39L03 -Text to document term matrix
4. en - S39L04 -Term frequency inverse document frequency
5. en - S39L05 -Building Text classifier
6. en - S39L06 -Building Text classifier continues with pipeline
7. en - S39L07 -Building Text classifier continues with multiple models
1. en - S40L01 - NLP, DL, DNLP
2. en - S40L02 - Neurons
3. en - S40L03 - Why Neural Network
4. en - S40L04 - Data representations using numbers
5. en - S40L05 - Neural Network - Data conversion process for input layer
6. en - S40L06 - Neural Network - Activation value and Overfitting concept
7. en - S40L07 -Neural Network - Generalization, Simplification, Filter sizes
8. en - S40L08 - Neural network, behind the scene calculations
9. en - S40L09 - Activation Functions
10. en - S40L10 - Types of activation functions
11. en - S40L11 - Back propogation
12. en - S40L12 - A simple digit classifier
1. en - S41L01 - Overview of DeepSpeech

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266 lessons

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Python: Section 01: Introduction

Python: Section 02: Setting up

Python: Section 03: Basics

Python: Section 04: Statements

Python: Section 05: Method and Functions

Python: Section 06: Module and packages

Python: Section 07: OOPS in python

Python: Section 08: Errors handling

Python: Section 09: Python decorators and Generators

Python: Section 12: Regular expression

Python: Section 13: Files

Python: Section 14: Numpy

Python: Section 15: Pandas

Python: Section 17: More useful modules

Python: Section 18: Matplotlib

ML: Before we start

ML: Visualisation (Exploratory Data Analysis) with Seaborn

ML: Linear Algebra basics for ML

ML: Pre-processing

ML: Linear Regression

ML: Multiple linear regression

ML: Polynomial regression

ML: Before we move forward

ML: Decision Tree regression

ML: Random forest regression

ML: Bagging and boosting

ML: Regression - AdaBoost and XGBoost regressor

ML: Regression - SVM (regression)

ML: Regression - Evaluation technique background

ML: Regression - Regression models master template

ML: Concept - K-Fold validation, GridSearch

ML: Pre-processing revisited

ML: Classification - K-nearest neighbors algorithm (KNN)

ML: Logistic regression classier

ML: Naive bayes classification

ML: Few good things to know about ML

ML: Classification - Support Vector machines

ML: Classification - Decision Tree and Random forest

ML: Classification - AdaBoost and XGBoost classifier

ML: Concept - Evaluation techniques (Manual)

ML: Classification - Classification model master template

ML: GridSearchCV, RandomizedSearchCV and KFold validation

ML: Evaluation techniques using curves (ROC, AUC, PR, CAP)

ML: Ensemble techniques

ML: Model deployment basics

ML: Web Application using Flask (Basics)

ML: Web App - Model Deployment on Flask Web Application

ML: Recommender Systems

ML: Association Rule - Apriori algorithm

ML: Clustering - K means clustering

ML: Reinforcement learning

ML: Upper Confidence Bound (UCB)

ML: Natural Language processing

ML: Deep learning

ML: DeepSpeech overview - A DeepNLP project

About this course

$15.00 / year with 7 day free trial

$199.99