Python for Data Science & Machine Learning: Zero to Hero

Python for Data Science & Machine Learning Zero to Hero


Empower your career journey with our in-demand course: Python for Data Science & Machine Learning: Zero to Hero

Boost your proficiency and propel your career forward with our meticulously crafted course, designed to be your ultimate guide to professional development. Our super-accessible modules break down complex topics into bite-sized, easy-to-understand lessons, filling your knowledge gaps and equipping you with real-world, practical skills.

Seeking career advancement and application of your skills? You’ve found the right place. This Python for Data Science & Machine Learning: Zero to Hero is your exclusive passport to unlocking your full potential.

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This sought-after course is your key to a successful and lucrative career. Don’t miss out on this transformative opportunity. Enroll now and take your professional life to the next level!

Course design

The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.

You are taught through a combination of

Exam & Retakes

It is to inform our learners that the initial exam for this online course is provided at no additional cost. In the event of needing a retake, a nominal fee of £9.99 will be applicable.


Upon successful completion of the assessment procedure, learners can obtain their certification by placing an order and remitting a fee of £9 for PDF Certificate and £15 for the Hardcopy Certificate within the UK ( An additional £10 postal charge will be applicable for international delivery).

Course Curriculum

Welcome to the Python for Data Science & ML bootcamp! 00:01:00
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 00:03:00
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 00:01:00
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: ones() 00:01:00
Basic NumPy arrays: full() 00:01:00
Adding a scalar 00:02:00
Subtracting a scalar 00:01:00
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 addition 00:02:00
Element-wise subtraction 00:01:00
Element-wise multiplication 00:01:00
Element-wise division 00:01:00
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 00:02:00
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 loc() 00:04:00
Filtering data using iloc() 00:02:00
Adding and deleting rows and columns 00:03:00
Sorting Values 00:02:00
Exporting and saving pandas DataFrames 00:02:00
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 00:01:00
Examples of Anomalies 00:01:00
Median-based Anomaly Detection 00:03:00
Mean-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
Introduction (Exploratory Data Analysis in Python) 00:01:00
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 & Continuous 00:05:00
Bivariate analysis: Categorical & Categorical 00:03:00
Bivariate analysis: Continuous & Categorical 00:02:00
Detecting Outliers 00:06:00
Categorical Variable Transformation 00:04:00
Python for Time-Series Analysis: A Primer
Introduction to Time Series 00:02:00
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 00:05:00
Plotting Histograms 00:01:00
Plotting Bar Charts 00:02:00
Plotting Pie Charts 00:03:00
Plotting Scatter Plots 00:06:00
Plotting Log Plots 00:01:00
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
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 00:03:00
Multiple Linear Regression with Python
Understanding Multiple linear regression 00:02:00
Exploring the dataset 00:04:00
Encoding Categorical Data 00:05:00
Splitting data into Train and Test Sets 00:02:00
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 00:03:00
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 00:02:00
Splitting data into Train and Test Sets 00:03:00
Feature Scaling 00:03:00
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
Exploring the dataset 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 00:01:00
Results Prediction & Accuracy 00:03:00
Classification Algorithms: Logistic regression
Introduction (Classification Algorithms: Logistic regression) 00:01:00
Implementation steps 00:01:00
Importing libraries & datasets 00:02:00
Splitting data into Train and Test Sets 00:01:00
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
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
Importing the dataset 00:03:00
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 00:03:00
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 00:01:00
Repeating the process for another movie 00:02:00
Conclusion 00:01:00

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