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Python for Data Science & Machine Learning: Zero to Hero

Python for Data Science & Machine Learning: Zero to Hero

Ready to master Python for Data Science & ML? Learn from zero with hands-on projects, Pandas, NumPy, and real ML models.

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Overview of Python for Data Science & Machine Learning: Zero to Hero

Join our Python for Data Science & Machine Learning: Zero to Hero course and discover your hidden skills, setting you on a path to success in this area. Get ready to improve your skills and achieve your biggest goals. The Python for Data Science & Machine Learning: Zero to Hero course has everything you need to get a great start in this sector.

Improving and moving forward is key to getting ahead personally. The Python for Data Science & Machine Learning: Zero to Hero course is designed to teach you the important stuff quickly and well, helping you to get off to a great start in the field. So, what are you looking for? Enrol now!

Get a Quick Look at The Course Content:

This Python for Data Science & Machine Learning: Zero to Hero Course will help you to learn:

This course covers the topic you must know to stand against the tough competition. The future is truly yours to seize with this Python for Data Science & Machine Learning: Zero to Hero. Enrol today and complete the course to achieve a certificate that can change your career forever.

Course Description

Perks of Learning with IOMH

One-to-one support from a dedicated tutor throughout your course.
Study online — whenever and wherever you want.
Instant digital/ PDF certificate.
100% money back guarantee.
12 months access.

Process of Evaluation

After studying the course, an MCQ exam or assignment will test your skills and knowledge. You have to get a score of 60% to pass the test and get your certificate.

Certificate of Achievement

Upon successfully completing the final assessment, you’ll unlock a CPD-accredited Certificate of Achievement—a valuable credential that demonstrates your commitment to professional development. The PDF Certificate is priced at £5.99 and will be emailed to you immediately. Also, A printed Hard copy Certificate can be purchased for £12.99 and will be sent to your address by post.

Who Is This Course for?

This Python for Data Science & Machine Learning: Zero to Hero is suitable for anyone aspiring to start a career in relevant field; even if you are new to this and have no prior knowledge, this course is going to be very easy for you to understand.

On the other hand, if you are already working in this sector, this course will be a great source of knowledge for you to improve your existing skills and take them to the next level.

This course has been developed with maximum flexibility and accessibility, making it ideal for people who don’t have the time to devote to traditional education.

Requirements

You don’t need any educational qualifications or experience to enrol in the Python for Data Science & Machine Learning: Zero to Hero course.

Do note: You must be at least 16 years old to enrol.

Any internet-connected device, such as a computer, tablet, or smartphone, can access this online course.

Career Path

The certification and skills you get from this Python for Data Science & Machine Learning: Zero to Hero Course can help you advance your career and gain expertise in several fields, allowing you to apply for high-paying jobs in related sectors.

Frequently Asked Questions

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Course Curriculum

Introduction
	Introduction to Python	00:01:00
	Setting Up Python	00:02:00
	What is Jupyter?	00:01:00
	Anaconda Installation Windows Mac and Ubuntu	00:04:00
	How to implement Python in Jupyter	00:01:00
	Managing Directories in Jupyter Notebook	00:03:00
	Input & Output	00:02:00
	Working with different datatypes	00:01:00
	Variables	00:02:00
	Arithmetic Operators	00:02:00
	Comparison Operators	00:01:00
	Logical Operators	00:04:00
	Conditional statements	00:02:00
	Loops	00:04:00
	Sequences Part 1: Lists	00:03:00
	Sequences Part 2: Dictionaries	00:03:00
	Sequences Part 3: Tuples	00:01:00
	Functions Part 1: Built-in Functions	00:01:00
	Functions Part 2: User-defined Functions	00:03:00
	Course Materials	00:00:00
The Must-Have Python Data Science Libraries
	Installing Libraries	00:01:00
	Importing Libraries	00:01:00
	Pandas Library for Data Science	00:01:00
	NumPy Library for Data Science	00:01:00
	Pandas vs NumPy	00:03:00
	Matplotlib Library for Data Science	00:01:00
	Seaborn Library for Data Science	00:01:00
NumPy Mastery: Everything you need to know about NumPy
	Introduction to NumPy arrays	00:01:00
	Creating NumPy arrays	00:06:00
	Indexing NumPy arrays	00:06:00
	Array shape	00:01:00
	Iterating Over NumPy Arrays	00:05:00
	Basic NumPy arrays: zeros()	00:02:00
	Basic NumPy arrays: full()	00:01:00
	Basic NumPy arrays: ones()	00:01:00
	Adding a scalar	00:02:00
	Subtracting a scalar	00:02:00
	Multiplying by a scalar	00:01:00
	Dividing by a scalar	00:01:00
	Raise to a power	00:01:00
	Transpose	00:01:00
	Element-wise division	00:01:00
	Element-wise subtraction	00:03:00
	Element-wise multiplication	00:01:00
	Element-wise addition	00:02:00
	Matrix multiplication	00:02:00
	Statistics	00:03:00
DataFrames and Series in Python's Pandas
	What is a Python Pandas DataFrame?	00:01:00
	What is a Python Pandas Series?	00:01:00
	DataFrame vs Series	00:01:00
	Creating a DataFrame using lists	00:03:00
	Creating a DataFrame using a dictionary	00:01:00
	Loading CSV data into python	00:05:00
	Changing the Index Column	00:01:00
	Inplace	00:01:00
	Examining the DataFrame: Head & Tail	00:01:00
	Statistical summary of the DataFrame	00:01:00
	Slicing rows using bracket operators	00:01:00
	Indexing columns using bracket operators	00:01:00
	Boolean list	00:01:00
	Filtering Rows	00:01:00
	Filtering rows using AND OR operators	00:02:00
	Filtering data using iloc()	00:02:00
	Filtering data using loc()	00:04:00
	Adding and deleting rows and columns	00:03:00
	Sorting values	00:01:00
	Exporting and saving pandas DataFrames	00:01:00
	Concatenating DataFrames	00:01:00
	groupby()	00:03:00
Data Cleaning Techniques for Better Data
	Introduction to Data Cleaning	00:01:00
	Quality of Data	00:02:00
	Examples of Anomalies	00:01:00
	Median-based Anomaly Detection	00:03:00
	Z-score-based Anomaly Detection	00:03:00
	Interquartile Range for Anomaly Detection	00:05:00
	Dealing with missing values	00:06:00
	Regular Expressions	00:07:00
	Feature Scaling	00:03:00
Exploratory Data Analysis in Python
	What is Exploratory Data Analysis?	00:01:00
	Univariate Analysis	00:02:00
	Univariate Analysis: Continuous Data	00:06:00
	Univariate Analysis: Categorical Data	00:02:00
	Bivariate analysis: Continuous & Categorical	00:02:00
	Bivariate analysis: Continuous & Categorical	00:02:00
	Bivariate analysis: Continuous & Continuous	00:05:00
	Detecting Outliers	00:06:00
	Categorical Variable Transformation	00:04:00
Python for Time-Series Analysis: A Primer
	Getting stock data using yfinance	00:03:00
	Converting a Dataset into Time Series	00:04:00
	Working with Time Series	00:04:00
	Visualising a Time Series	00:03:00
Python for Data Visualisation: Library Resources, and Sample Graphs
	Data Visualisation using python	00:01:00
	Setting Up Matplotlib	00:01:00
	Plotting Line Plots using Matplotlib	00:02:00
	Title, Labels & Legend	00:07:00
	Plotting Histograms	00:01:00
	Plotting Bar Charts	00:03:00
	Plotting Pie Charts	00:03:00
	Plotting Scatter Plots	00:06:00
	Plotting Log Plots	00:02:00
	Plotting Polar Plots	00:02:00
	Handling Dates	00:01:00
	Creating multiple subplots in one figure	00:03:00
The Basics of Machine Learning
	What is Machine Learning?	00:02:00
	Applications of machine learning	00:02:00
	Machine Learning Methods	00:01:00
	What is Supervised learning?	00:01:00
	What is Unsupervised learning?	00:01:00
	Supervised learning vs Unsupervised learning	00:04:00
Simple Linear Regression with Python
	Introduction to regression	00:02:00
	How Does Linear Regression Work?	00:02:00
	Line representation	00:01:00
	Implementation in python: Importing libraries & datasets	00:02:00
	Implementation in python: Distribution of the data	00:02:00
	Implementation in python: Creating a linear regression object	00:00:00
Multiple Linear Regression with Python
	Understanding Multiple linear regression	00:02:00
	Exploring the dataset	00:01:00
	Encoding Categorical Data	00:03:00
	Splitting data into Train and Test Sets	00:01:00
	Training the model on the Training set	00:01:00
	Predicting the Test Set results	00:03:00
	Evaluating the performance of the regression model	00:01:00
	Root Mean Squared Error in Python	00:02:00
Classification Algorithms: K-Nearest Neighbors
	Introduction to classification	00:01:00
	K-Nearest Neighbours algorithm	00:01:00
	Example of KNN	00:01:00
	K-Nearest Neighbours (KNN) using python	00:01:00
	Importing required libraries	00:01:00
	Importing the dataset	00:04:00
	Splitting data into Train and Test Sets	00:01:00
	Feature Scaling	00:01:00
	Importing the KNN classifier	00:02:00
	Results prediction & Confusion matrix	00:02:00
Classification Algorithms: Decision Tree
	Introduction to decision trees	00:01:00
	What is Entropy?	00:01:00
	Decision tree structure	00:01:00
	Importing libraries & datasets	00:01:00
	Encoding Categorical Data	00:05:00
	Splitting data into Train and Test Sets	00:02:00
	Results Prediction & Accuracy	00:03:00
Classification Algorithms: Logistic regression
	Implementation steps	00:01:00
	Importing libraries & datasets	00:02:00
	Splitting data into Train and Test Sets	00:03:00
	Pre-processing	00:02:00
	Training the model	00:01:00
	Results prediction & Confusion matrix	00:02:00
	Logistic Regression vs Linear Regression	00:02:00
Clustering
	Introduction to clustering	00:01:00
	Use cases	00:01:00
	K-Means Clustering Algorithm	00:01:00
	Elbow method	00:02:00
	Steps of the Elbow method	00:01:00
	Implementation in python	00:04:00
	Hierarchical clustering	00:01:00
	Density-based clustering	00:02:00
	Implementation of k-means clustering in python	00:01:00
	Visualising the dataset	00:02:00
	Defining the classifier	00:02:00
	3D Visualisation of the clusters	00:03:00
	3D Visualisation of the predicted values	00:03:00
	Number of predicted clusters	00:02:00
Recommender System
	Introduction (Recommender System)	00:01:00
	Collaborative Filtering in Recommender Systems	00:01:00
	Content-based Recommender System	00:01:00
	Importing libraries & datasets	00:05:00
	Merging datasets into one dataframe	00:01:00
	Sorting by title and rating	00:04:00
	Histogram showing number of ratings	00:01:00
	Frequency distribution	00:01:00
	Jointplot of the ratings and number of ratings	00:01:00
	Data pre-processing	00:02:00
	Sorting the most-rated movies	00:01:00
	Grabbing the ratings for two movies	00:01:00
	Correlation between the most-rated movies	00:02:00
	Sorting the data by correlation	00:01:00
	Filtering out movies	00:01:00
	Sorting Values	00:02:00
	Repeating the process for another movie	00:02:00
Conclusion
	Conclusion	00:01:00