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About This Course
- 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
-
Welcome to the Python for Data Science & ML bootcamp!
00:01:00 -
Introduction to Python
00:01:00
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Setting Up Python
00:02:00
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What is Jupyter?
00:01:00
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Anaconda Installation Windows Mac and Ubuntu
00:04:00
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How to implement Python in Jupyter
00:01:00
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Managing Directories in Jupyter Notebook
00:03:00
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Input & Output
00:02:00
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Working with different datatypes
00:01:00
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Variables
00:02:00
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Arithmetic Operators
00:02:00
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Comparison Operators
00:01:00
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Logical Operators
00:03:00
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Conditional statements
00:02:00
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Loops
00:04:00
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Sequences Part 1: Lists
00:03:00
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Sequences Part 2: Dictionaries
00:03:00
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Sequences Part 3: Tuples
00:01:00
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Functions Part 1: Built-in Functions
00:01:00
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Functions Part 2: User-defined Functions
00:03:00
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Course Materials
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Installing Libraries
00:01:00
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Importing Libraries
00:01:00
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Pandas Library for Data Science
00:01:00
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NumPy Library for Data Science
00:01:00
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Pandas vs NumPy
00:01:00
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Matplotlib Library for Data Science
00:01:00
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Seaborn Library for Data Science
00:01:00
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Introduction to NumPy arrays
00:01:00
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Creating NumPy arrays
00:06:00
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Indexing NumPy arrays
00:07:00
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Array shape
00:01:00
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Iterating Over NumPy Arrays
00:05:00
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Basic NumPy arrays: zeros()
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Basic NumPy arrays: ones()
00:01:00
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Basic NumPy arrays: full()
00:01:00
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Adding a scalar
00:02:00
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Subtracting a scalar
00:01:00
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Multiplying by a scalar
00:01:00
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Dividing by a scalar
00:01:00
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Raise to a power
00:01:00
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Transpose
00:01:00
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Element-wise addition
00:02:00
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Element-wise subtraction
00:01:00
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Element-wise multiplication
00:01:00
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Element-wise division
00:01:00
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Matrix multiplication
00:02:00
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Statistics
00:03:00
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What is a Python Pandas DataFrame?
00:01:00
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What is a Python Pandas Series?
00:01:00
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DataFrame vs Series
00:01:00
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Creating a DataFrame using lists
00:03:00
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Creating a DataFrame using a dictionary
00:01:00
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Loading CSV data into python
00:02:00
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Changing the Index Column
00:01:00
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Inplace
00:01:00
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Examining the DataFrame: Head & Tail
00:01:00
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Statistical summary of the DataFrame
00:01:00
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Slicing rows using bracket operators
00:01:00
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Indexing columns using bracket operators
00:01:00
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Boolean list
00:01:00
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Filtering Rows
00:01:00
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Filtering rows using AND OR operators
00:02:00
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Filtering data using loc()
00:04:00
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Filtering data using iloc()
00:02:00
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Adding and deleting rows and columns
00:03:00
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Sorting Values
00:02:00
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Exporting and saving pandas DataFrames
00:02:00
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Concatenating DataFrames
00:01:00
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groupby()
00:03:00
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Introduction to Data Cleaning
00:01:00
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Quality of Data
00:01:00
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Examples of Anomalies
00:01:00
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Quality of Data
00:01:00
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Examples of Anomalies
00:01:00
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Median-based Anomaly Detection
00:03:00
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Mean-based anomaly detection
00:03:00
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Z-score-based Anomaly Detection
00:03:00
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Interquartile Range for Anomaly Detection
00:05:00
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Dealing with missing values
00:06:00
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Regular Expressions
00:07:00
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Feature Scaling
00:01:00
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Introduction (Exploratory Data Analysis in Python)
00:01:00
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What is Exploratory Data Analysis?
00:01:00
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Univariate Analysis
00:02:00
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Univariate Analysis: Continuous Data
00:06:00
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Univariate Analysis: Categorical Data
00:02:00
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Bivariate analysis: Continuous & Continuous
00:05:00
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Bivariate analysis: Categorical & Categorical
00:03:00
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Bivariate analysis: Continuous & Categorical
00:02:00
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Detecting Outliers
00:06:00
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Categorical Variable Transformation
00:04:00
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Introduction to Time Series
00:02:00
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Getting stock data using yfinance
00:03:00
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Converting a Dataset into Time Series
00:04:00
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Working with Time Series
00:04:00
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Visualising a Time Series
00:03:00
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Data Visualisation using python
00:01:00
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Setting Up Matplotlib
00:01:00
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Plotting Line Plots using Matplotlib
00:02:00
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Title, Labels & Legend
00:07:00
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Plotting Histograms
00:01:00
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Plotting Bar Charts
00:02:00
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Plotting Pie Charts
00:03:00
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Plotting Scatter Plots
00:06:00
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Plotting Log Plots
00:01:00
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Plotting Polar Plots
00:02:00
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Handling Dates
00:01:00
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Creating multiple subplots in one figure
00:03:00
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What is Machine Learning?
00:02:00
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Applications of machine learning
00:02:00
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Machine Learning Methods
00:01:00
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What is Supervised learning?
00:01:00
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What is Unsupervised learning?
00:01:00
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Supervised learning vs Unsupervised learning
00:04:00
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Introduction to regression
00:02:00
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How Does Linear Regression Work?
00:02:00
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Line representation
00:01:00
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Implementation in python: Importing libraries & datasets
00:02:00
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Implementation in python: Distribution of the data
00:02:00
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Implementation in python: Creating a linear regression object
00:03:00
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Understanding Multiple linear regression
00:02:00
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Exploring the dataset
00:04:00
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Encoding Categorical Data
00:05:00
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Splitting data into Train and Test Sets
00:01:00
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Training the model on the Training set
00:01:00
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Predicting the Test Set results
00:03:00
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Evaluating the performance of the regression model
00:01:00
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Root Mean Squared Error in Python
00:03:00
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Introduction to classification
00:01:00
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K-Nearest Neighbours algorithm
00:01:00
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Example of KNN
00:01:00
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K-Nearest Neighbours (KNN) using python
00:01:00
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Importing required libraries
00:01:00
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Importing the dataset
00:03:00
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Splitting data into Train and Test Sets
00:01:00
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Feature Scaling
00:01:00
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Importing the KNN classifier
00:02:00
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Results prediction & Confusion matrix
00:02:00
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Introduction to decision trees
00:01:00
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What is Entropy?
00:01:00
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Exploring the dataset
00:01:00
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Decision tree structure
00:01:00
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Importing libraries & datasets
00:03:00
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Encoding Categorical Data
00:03:00
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Splitting data into Train and Test Sets
00:01:00
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Results Prediction & Accuracy
00:03:00
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Introduction (Classification Algorithms: Logistic regression)
00:01:00
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Implementation steps
00:01:00
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Importing libraries & datasets
00:03:00
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Splitting data into Train and Test Sets
00:03:00
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Pre-processing
00:02:00
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Training the model
00:01:00
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Results prediction & Confusion matrix
00:02:00
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Logistic Regression vs Linear Regression
00:02:00
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Introduction to clustering
00:01:00
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Use cases
00:01:00
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K-Means Clustering Algorithm
00:01:00
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Elbow method
00:02:00
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Steps of the Elbow method
00:01:00
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Implementation in python
00:04:00
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Hierarchical clustering
00:01:00
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Density-based clustering
00:02:00
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Implementation of k-means clustering in python
00:01:00
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Implementation of k-means clustering in python
00:01:00
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Visualising the dataset
00:02:00
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Defining the classifier
00:02:00
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3D Visualisation of the clusters
00:03:00
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3D Visualisation of the predicted values
00:03:00
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Number of predicted clusters
00:02:00
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Introduction (Recommender System)
00:01:00
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Collaborative Filtering in Recommender Systems
00:09:00
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Content-based Recommender System
00:01:00
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Importing libraries & datasets
00:03:00
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Merging datasets into one dataframe
00:01:00
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Sorting by title and rating
00:04:00
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Histogram showing number of ratings
00:01:00
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Frequency distribution
00:01:00
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Jointplot of the ratings and number of ratings
00:01:00
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Data pre-processing
00:02:00
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Sorting the most-rated movies
00:01:00
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Grabbing the ratings for two movies
00:01:00
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Correlation between the most-rated movies
00:02:00
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Sorting the data by correlation
00:01:00
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Filtering out movies
00:01:00
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Sorting values
00:01:00
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Repeating the process for another movie
00:02:00
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Conclusion
00:01:00
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