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Python for Data Science & Machine Learning: Zero to Hero
1 Students (5 Reviews)
Register on the today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study …
GET THIS COURSE AND 3000+ OTHERS FOR ONLY £49 PER YEAR. FIND OUT MORE
Register on the Python for Data Science & Machine Learning: Zero to Hero today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career.
The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials.
Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a certificate as proof of your course completion.
The Python for Data Science & Machine Learning: Zero to Hero course is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablets, and smartphones.
The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly!
What You Get With The Python for Data Science & Machine Learning: Zero to Hero course
- Receive a digital certificate upon successful completion of the course
- Get taught by experienced, professional instructors
- Study at a time and pace that suits your learning style
- Get instant feedback on assessments
- 24/7 help and advice via email or live chat
- Get full tutor support on weekdays (Monday to Friday)
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
- Video lessons
- Online study materials
Certification
After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post.
Who Is This Course For:
The course is ideal for those who already work in this sector or are aspiring professionals. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge.
Requirements:
The online training is open to all students and has no formal entry requirements. To study the Python for Data Science & Machine Learning: Zero to Hero course, all you need is a passion for learning, A good understanding of English, numeracy, and IT skills. You must also be over the age of 16.
Course Curriculum
| Section 01: 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 | ||
| Section 02: 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 | ||
| Section 03: 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 | ||
| Section 04: 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 | ||
| Section 05: 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 | ||
| Section 06: Exploratory Data Analysis in Python | |||
| Introduction (Exploratory Data Analysis in Python) | 00:01:00 | ||
| What is Exploratory Data Analysis? | 00:01:00 | ||
| 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 | ||
| Section 07: 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 | ||
| Section 08: 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 | ||
| Section 09: 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 | ||
| Section 10: 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 | ||
| Section 11: 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 | ||
| Section 12: 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 | 00:03:00 | ||
| 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 | ||
| Section 13: 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 | ||
| Section 14: Classification Algorithms: Logistic regression | |||
| Introduction (Classification Algorithms: Logistic regression) | 00:01:00 | ||
| Implementation Steps | 00:29:00 | ||
| Importing libraries & datasets | 00:02:00 | ||
| Splitting data into Train and Test Sets | 00:01:00 | ||
| Pre-processing | 00:02:00 | ||
| Training the model on the Training set | 00:01:00 | ||
| Results prediction & Confusion matrix | 00:02:00 | ||
| Logistic Regression vs Linear Regression | 00:02:00 | ||
| Section 15: 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 | 00:02:00 | ||
| 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 | ||
| Section 16: 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 | 00:01:00 | ||
| 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 | 00:21:00 | ||
| 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 | ||
| Section 17: Conclusion | |||
| Conclusion | 00:01:00 | ||
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Frequently asked questions
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There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course.
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For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime.
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