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- Overview
Educational accomplishments and income are closely correlated. Education and skill with endorsed certificates from credible and renowned authorities typically lead to better jobs with attractive salaries. Educated and skilled workers also have lower rates of unemployment. Therefore, skills and endorsed certificates to showcase are essential for people early in their careers.
- Why Choose Imperial Academy?
Imperial Academy offers this premium Python for Data Science & Machine Learning: Zero to Hero course to ensure security in your career. In addition, this comprehensive Python for Data Science & Machine Learning: Zero to Hero course will assist you in building relevant skills that will help you find a job in the related sectors. Also, the Certificate you’ll get after completing the Python for Data Science & Machine Learning: Zero to Hero will put your head and shoulder above others in front of potential employers.
Become the person who would attract the results you seek. What you plant now, you will harvest later. So, grab this opportunity and start learning Python for Data Science & Machine Learning: Zero to Hero!
- What Imperial Academy Offers You
- QLS/ CPD/ CIQ Accredited
- 24/7 Assistance from our Support Team
- 100% Online
- Self-paced course
- Bite-sized Audio-visual Modules
- Rich Learning Materials
- Developed by Industry Specialists
- Career Guidance
- Course Design
Learn at your own pace from the comfort of your home, as the rich learning materials of this premium course is accessible from any place at any time. The advanced course curriculums are divided into tiny bite-sized audio-visual modules by industry specialists with years of experience behind them.
- Audio-visual Lessons
- Online Study Materials
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 | 00:07:00 | ||
| Array shape | 00:01:00 | ||
| Iterating Over NumPy Arrays | 00:05:00 | ||
| Basic NumPy arrays: zeros() | 00:00:00 | ||
| 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 | ||
| 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 | 00:01:00 | ||
| 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 | 00:07:00 | ||
| 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 | 00:01:00 | ||
| 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 | 00:03:00 | ||
| 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 | 00:03:00 | ||
| 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 | ||
| Exploring the dataset | 00:01:00 | ||
| Decision tree structure | 00:01:00 | ||
| Importing libraries & datasets | 00:03:00 | ||
| 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 | 00:03:00 | ||
| 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 | ||
| 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 | 00:09:00 | ||
| 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 | ||
Certificate of Achievement
Learners will get an certificate of achievement directly at their doorstep after successfully completing the course!
It should also be noted that international students must pay £10 for shipping cost.
CPD Accredited Certification
Upon successfully completing the course, you will be qualified for CPD Accredited Certificate. Certification is available –
- PDF Certificate £7.99
- Hard Copy Certificate £14.99
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