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Machine Learning in Python: From Zero to Hero in 10 Hours

Name: Machine Learning in Python: From Zero to Hero in 10 Hours
Rating: 4.6 (77 reviews)

Machine Learning & Data Science in Python with real life projects. Source codes included.

Created bySanjay Singh

Last updated 4/2026

English

What you'll learn

Hands-on explanation of every major Machine Learning techniques.
Model Development, Deployment and Monitoring.
Regression: Simple, Polynomial, and Multinomial
Classification: Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Naive Bayes
Ensemble Modeling: Voting Classifier, Bagging, Boosting, Stacking, Random Forest
Implementation of every concepts explained in the course. Source codes are made available to you for your use.
Data Visualization with MatPlotLib and Seaborn
Use train, test and Cross Validation to choose and tune data
Feature Engineering (Reduce Noise, Outliers) and Data Preprocessing
Practical examples of How to trade-off between Bias, Variance, Irreducible errors using Ensemble Learning model and Bagging, Boosting
Understand how to implement Machine Learning at massive scale
Understand math and statistics behind Machine Learning models

Course content

21 sections • 75 lectures • 8h 7m total length

What is Machine Learning3:17
Welcome to this course. Let's understand what is Machine learning.
You will learn about relations between Algorithm, Features, Data and Classical Programs, Data Science, Data Mining, and Machine Learning.
Machine Learning Terms and Keywords3:59
In this lecture, you will learn about some of the important terms and keywords used in machine learning i.e. Observations, Labels, Features, Predictors, Independent Variable, Target Variable, Predictions, Categorical Variable, Numerical Variables, Feature Matrix, Target Vector.
Types of ML Algorithm3:19
In this lecture, you will learn about the different types of the machine learning algorithms.
Supervised
Regression
Linear Regression
Simple Linear Regression
Multiple Linear Regression
Polynomial Linear Regression
Classification
Logistic Regression
Decision Tree
Support Vector Machine (SVM)
Naive Bayes (NB)
K-Nearest Neighbors (KNN)
Random Forest
Unsupervised
Clustering
K-Means
Association
Aprio
Reinforcement Learning
Trial & Error
Markov Decision Process
ML Introduction Quiz

Python Basics18:26
In this lecture, you will learn the basics of Python programming. You will learn about:
White space Indentation while writing a Python Program
Different data types and data structures like int, string, list, dictionary, tuples
Flow Control and Loop
Functions
Modules
Numpy- Single Dimensional Array (Vector)17:37
In this lecture, you will learn about python library NumPy and it's functions like
array, max,argmax,min,argmin,linspace,random.randnt,random.rand,size and other functions.
The focus of this lecture is the use of NumPy in the single dimensional array.
Numpy-Multidimensional Array (Matrix)10:25
In this lecture, you will learn about python library NumPy and it's functions like
array, max,argmax,min,argmin,linspace,random.randnt,random.rand,size and other functions.
The focus of this lecture is the use of NumPy in a multi-dimensional array.
Numpy-Statistical Functions2:21
In this lecture, you will learn about python library NumPy and it's functions like
std,log,sqrt
The focus of this lecture is the use of NumPy statistical functions.
Pandas21:05
In this lecture, you will explore different features of Jupyter and also explore Python library Pandas.
Create Pandas data frame
Indexing using the index method
Slicing and Dicing Data Set using iloc and loc methods
Import data from files using Pandas read_csv and other methods.
Pandas- Time Series11:45
In this lecture, you will go through the essential features of Panda's time series tool.
Matplot13:56
In this lecture, you will learn about Matplot and it's features like 2D visualization, 3D visualization, Basemap.
Seaborn14:15
In this lecture, you will learn about data exploration using the Python library Seaborn. You will understand
Data Distribution
Data correlation and heat map
Scatter plots and linear regression plots
Cat Plots
and other visualizations
Quiz: Python basics and Libraries

Simple Linear Regression3:56
In this lecture you will learn what is simple linear regression and how it's coefficients are calculated using least square method and root mean square error (RMSE).
Hans-on: Simple Linear Regression Algorithm11:00
In this lecture, you will write a Simple Linear Algorithm from scratch using Python 3.
Hands-on: Simple Linear Regression -18:45
In this lecture, you are going to do predictive analysis on building height and number of stories data set using machine learning simple linear algorithm. You will use python provided libraries Numpy, Pandas, Matplot, and Sklearn.
Hands-on: Simple Linear Regression -28:11
In this lecture, you are going to do predictive analysis on building height and number of stories data set using machine learning simple linear algorithm. You will use python provided libraries Numpy, Pandas, Matplot, and Sklearn.

Multiple Linear Regression Introduction3:48
In this lecture, you will understand what is Multiple Linear Regression.
Multiple Linear Regression Data Set0:13
This lecture will show you where to get the data from for practicing multiple linear regression.
Hands-on: Multiple Linear Regression - 18:37
In this lecture, you will import the mpg data set into the pandas dataframe, provide column names, use the to_numeric function to change one of the feature values to the numeric value, and check multicollinearity.
Hands-on: Multiple Linear Regression -210:30
This lecture is a continuation of the multiple linear regression you are building from the previous lecture. In this lecture, you are going to use the Variance Inflation Factor to get rid of multicollinearity. Then you will draw a scatter plot of remaining independent variables using a sea-born pair plot.
Hands-on: Multiple Linear Regression -311:19
This lecture is a continuation of the multiple linear regression you are building from the previous lecture. In this lecture, you are going to compare p-value with t, split the data set in training and test data, and run multiple linear regression models. Then you will draw a distribution plot of y pred and y test.

KNN Introduction6:49
In this lecture, you will understand what is the difference between the Parametric and Non-parametric models and how K-Nearest Neighbor (KNN) model works.
Hand-on: KNN Regression- Step19:15
In this lecture, you will learn about K-Nearest Neighbors (KNN) Regression algorithm. You will calculate the r2 score and root mean square error of each regression model to compare the improvement in prediction.
Hands-on: KNN Regression -Step24:37
In this lecture, you will learn about K-Nearest Neighbors (KNN) Regression algorithm. You will calculate the r2 score and root mean square error of each regression model to compare the improvement in prediction.

Requirements

To be able to operate computer
A lot of curiosity!
Some knowledge of Python programming and high school level math will be an asset

Description

Join the most comprehensive Machine Learning Hands-on Course, because now is the time to get started!

From basic concepts about Python Programming, Supervised Machine Learning, Unsupervised Machine Learning to Reinforcement Machine Learning, Natural Language Processing (NLP), this course covers all you need to know to become a successful Machine Learning Professional!

But that's not all! Along with covering all the steps of Machine Learning functions, this course also has quizzes and projects, which allow you to practice the things learned throughout the course!

You'll not only learn about the concepts but also practice each of those concepts through hands-on and real-life Projects.

And if you do get stuck, you benefit from extremely fast and friendly support - both via direct messaging or discussion. You have my word!

With more than two decades of IT experience, I have designed this course for students and professionals who wish to master how to develop and support industry-standard Machine learning projects.

This course will be kept up-to-date to ensure you don't miss out on any changes once Machine Learning is required in your project!

Why Machine Learning?

In modern times, Machine Learning is one of the most popular (if not the most!) career choices. According to available data, Machine Learning Engineer Is The Best Job of 2019 with a 344% growth and an average base salary of $146,085 per year.

If you are looking for a thriving career in Data Analytics, Artificial Intelligence, Robotics, this is the right time to learn Machine Learning.

Don't be left out and prepare well for these opportunities.

So, what are you waiting for?

Pay once, benefit a lifetime! This is an evolving course! Machine Learning and future enhancements will be covered in this course. You won’t lose out on anything! Don’t lose any time, gain an edge, and start now!

Who this course is for:

Students and professionals who want to become Machine Learning Expert or Data Scientist.
IT Professionals, Mathematicians, Statisticians.
Machine learning enthusiasts.
Project Managers, Data Analytics, and Business Intelligence Professionals.
Python developers.

Machine Learning in Python: From Zero to Hero in 10 Hours

What you'll learn

Explore related topics

Course content

Introduction3 lectures • 11min

Basic Math and Statistics1 lecture • 7min

Prerequisite Tools4 lectures • 12min

Python Programming8 lectures • 1hr 50min

Data Pre-processing2 lectures • 15min

Part 1: Supervised Learning -> Regression1 lecture • 1min

Simple Linear Regression4 lectures • 32min

Multiple Linear Regression (MLR)5 lectures • 34min

Polynomial Linear Regression (PLR)2 lectures • 18min

K-Nearest Neighbors (KNN)3 lectures • 21min

Requirements

Description

Who this course is for: