Python for Data Science and Machine Learning Bootcamp Course
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1 Year
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Intermediate
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Course Certificate
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186
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6 hours, 19 minutes
Duration
Overview
Begin your career journey in Data Science realm with our Python for Data Science and Machine Learning Bootcamp Course. The course is designed to make you a hero from zero.
In the introductory chapter, you will learn about Python and Jupyter, followed by Python setup and Anaconda installation. After that, you will be taught about The Must-Have Python Data Science Libraries. Finally, after 13 more detailed modules, this comprehensive course concludes with lesson on Recommender System.
At the end of the course, you will receive a certificate of achievement. This certificate will enhance your employability in the IT industry. Join now!
Course Preview
Learning Outcomes
- Develop a thorough understanding of Python programming for data science
- Master the use of fundamental data science libraries such as NumPy, Pandas, Matplotlib and Seaborn
- Gain expertise in data cleaning, exploratory data analysis, and data visualisation.
- Understand and implement different machine learning algorithms.
Why Take This Course From John Academy?
- Affordable, well-structured and high-quality e-learning study materials
- Meticulously crafted, engaging and informative tutorial videos and materials
- Efficient exam systems for the assessment and instant result
- Earn UK & internationally recognised accredited qualification
- Easily access the course content on mobile, tablet, or desktop from anywhere, anytime
- Excellent career advancement opportunities
- Get 24/7 student support via email
What Skills Will You Learn from This Course?
- Python Programming Basics
- Data Manipulation with Pandas
- Numerical Computation with NumPy
- Data Visualisation with Matplotlib and Seaborn
- Data Cleaning and Preprocessing
- Machine Learning Algorithms and Techniques
- Building and Evaluating Machine Learning Models
Who Should Take This Python for Data Science and Machine Learning Bootcamp Course?
Whether you’re an existing practitioner or an aspiring professional, this course is an ideal training opportunity. It will elevate your expertise and boost your CV with key skills and a recognised qualification attesting to your knowledge.
Are There Any Entry Requirements?
This Python for Data Science and Machine Learning Bootcamp Course is available to all learners of all academic backgrounds. But learners should be aged 16 or over to undertake the qualification. And a good understanding of the English language, numeracy, and ICT will be helpful.
Python for Data Science and Machine Learning Bootcamp Course Certification
After completing and passing the Python for Data Science and Machine Learning Bootcamp Course successfully, you will be able to obtain a Recognised Certificate of Achievement. Learners can obtain the certificate in hard copy at £14.99 or PDF format at £11.99.
Career Path
This exclusive Python for Data Science and Machine Learning Bootcamp Course will equip you with effective skills and abilities and help you explore career paths such as
- Data Scientist
- Machine Learning Engineer
- Data Analyst
- Research Scientist
- AI Specialist
Course Curriculum
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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 & datasets 00:03:00
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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 00:06:00
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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() 00:02:00
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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 data frame 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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ltering 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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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 00:03:00
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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 00:03:00
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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 00:02:00
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Splitting data into Train and Test Sets 00:03:00
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Feature Scaling 00:03:00
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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 00:02:00
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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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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 00:01:00
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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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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 00:01:00
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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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