Python for Data Science & Machine Learning: Zero to Hero

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Python for Data Science & Machine Learning: Zero to Hero

Python for Data Science & Machine Learning: Zero to Hero

Level: Intermediate
Modules: 185 Modules
Duration: 6 hours, 11 minutes
Students: 1

Unlock the power of data and machine learning with our comprehensive course, Python for Data Science & Machine Learning: Zero to Hero! This course is meticulously designed to guide you from the basics to advanced techniques, ensuring you gain a solid understanding of Python’s capabilities in data science. You’ll dive deep into essential libraries, learn to clean and analyse data, and master various machine learning algorithms. With Python for Data Science & Machine Learning: Zero to Hero, you’ll transform your approach to data, discovering how to uncover hidden patterns, visualize data effectively, and make informed decisions. Embrace the world of data science with confidence and precision, equipped with the knowledge and skills from our in-depth curriculum. Whether you’re new to programming or looking to enhance your data science skills, this course offers a structured path to becoming proficient in Python for Data Science & Machine Learning: Zero to Hero.

Learning Outcomes

Understand and use essential Python data science libraries.
Perform data cleaning and preprocessing.
Conduct exploratory data analysis.
Implement various machine learning algorithms.
Visualize data using Python libraries.
Analyse time-series data.

Why buy this Python for Data Science & Machine Learning: Zero to Hero course?

Unlimited access to the course for forever
Digital Certificate, Transcript, student ID all included in the price
Absolutely no hidden fees
Directly receive CPD accredited qualifications after course completion
Receive one to one assistance on every weekday from professionals
Immediately receive the PDF certificate after passing
Receive the original copies of your certificate and transcript on the next working day
Easily learn the skills and knowledge from the comfort of your home

Certification

After studying the course materials of the Python for Data Science & Machine Learning: Zero to Hero there will be a written assignment test which you can take either during or at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99. Original Hard Copy certificates need to be ordered at an additional cost of £8.

Who is this Python for Data Science & Machine Learning: Zero to Hero course for?

Beginners in programming with an interest in data science.
Data analysts looking to enhance their Python skills.
Statisticians wanting to apply their knowledge using Python.
Students in computer science or related fields.
Professionals transitioning to a data-focused career.
Anyone curious about machine learning and data analysis.

Prerequisites

This Python for Data Science & Machine Learning: Zero to Hero does not require you to have any prior qualifications or experience. You can just enrol and start learning. This Python for Data Science & Machine Learning: Zero to Hero was made by professionals and it is compatible with all PC’s, Mac’s, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.

Career path

Data Scientist: £40,000 to £70,000 per year
Machine Learning Engineer: £45,000 to £80,000 per year
Data Analyst: £30,000 to £50,000 per year
AI Research Scientist: £50,000 to £90,000 per year
Business Intelligence Analyst: £35,000 to £60,000 per year
Big Data Engineer: £50,000 to £85,000 per year

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
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	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	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