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Python for Data Science & Machine Learning: Zero to Hero

Overview Welcome to Python for Data Science & Machine Learning: Zero to Hero. This comprehensive course takes you from the …

Python for Data Science & Machine Learning: Zero to Hero

Python for Data Science & Machine Learning: Zero to Hero

Original price was: $417.25.Current price is: $35.30.

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

Welcome to Python for Data Science & Machine Learning: Zero to Hero. This comprehensive course takes you from the basics of Python programming to advanced concepts in data science and machine learning. You’ll explore essential Python libraries, master data manipulation with NumPy and Pandas, learn data cleaning and exploratory data analysis techniques, and delve into time-series analysis and data visualisation. Additionally, you’ll gain a solid understanding of machine learning fundamentals, including regression, classification, clustering, and recommender systems. Through hands-on projects and real-world examples, you’ll build the skills needed to become proficient in data science and machine learning.

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

 
  • Understand the key libraries for data science.
  • Master data manipulation using NumPy.
  • Work with DataFrames and Series in Pandas.
  • Apply data cleaning techniques for improved data quality.
  • Perform exploratory data analysis in Python.
  • Conduct time-series analysis with Python..
  • Create data visualisations using Python libraries
  • Understand the basics of machine learning.
  • Implement simple and multiple linear regression models.
  • Utilise classification algorithms like K-Nearest Neighbours, Decision Tree, and Logistic Regression.

Who Is This Course For 

This course is designed for anyone interested in data science and machine learning, whether you’re a beginner with no prior programming experience or a professional looking to expand your skills. It’s ideal for students, data analysts, aspiring data scientists, and anyone keen to leverage Python for data-driven decision-making. If you want to transition into a data science career or enhance your analytical capabilities, this course provides the foundational knowledge and practical skills you need.

Eligibility Requirements

There are no prerequisites for this course. Basic computer skills and a willingness to learn are all you need. Access to a computer with Python installed is necessary to complete the exercises and projects.

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

  • Age Requirement: Applicants must be aged 16 or above, allowing both young learners and adults to engage in this educational pursuit.
  • Academic Background: There are no specific educational prerequisites, opening the door to individuals from diverse academic histories.
  • Language Proficiency: A good command of the English language is essential for comprehension and engagement with the course materials.
  • Numeracy Skills: Basic numeracy skills are required for understanding nutritional data and dietary planning.

Why Choose Us

  • Affordable, engaging & high-quality e-learning study materials;
  • Tutorial videos/materials from the industry-leading experts;
  • Study in a user-friendly, advanced online learning platform;
  • Efficient exam systems for the assessment and instant result;
  • The UK & internationally recognised accredited
  • Access to course content on mobile, tablet or desktop from anywhere, anytime;
  • The benefit of career advancement opportunities;
  • 24/7 student support via email.

Career Path with Python for Data Science & Machine Learning: Zero to Hero

By completing this course, you can pursue various career paths in data science and machine learning. You might become a data analyst, machine learning engineer, data scientist, or business intelligence analyst. Your proficiency in Python and data science techniques will make you an asset to organisations in tech, finance, healthcare, and many other industries. This course also prepares you for advanced studies and specialised roles in the rapidly growing field of data science.

 

Course Curriculum

Introduction
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
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 00:03:00
Multiple Linear Regression with Python
Understanding Multiple linear regression 00:02:00
Exploring the dataset multi 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 00:01:00
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
Exploring the dataset :Decison tree 00:01:00
Decision tree structure 00:01:00
Importing libraries & datasets 00:01:00
Encoding Categorical Data 00:03:00
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
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
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
Conclusion 00:01:00

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