Overview:
Welcome to the Data Science and Machine Learning using Python Bootcamp! This comprehensive course is designed to equip participants with the essential knowledge and skills to excel in the fields of data science and machine learning using the Python programming language. Through hands-on exercises, real-world projects, and expert guidance, participants will learn the fundamental concepts, tools, and techniques required to analyze data, build predictive models, and extract valuable insights.- Interactive video lectures by industry experts
- Instant e-certificate and hard copy dispatch by next working day
- Fully online, interactive course with Professional voice-over
- Developed by qualified first aid professionals
- Self paced learning and laptop, tablet, smartphone friendly
- 24/7 Learning Assistance
- Discounts on bulk purchases
Main Course Features:
- Comprehensive coverage of Python programming fundamentals and libraries for data science and machine learning, including NumPy, Pandas, Matplotlib, and Scikit-learn.
- Hands-on coding exercises and projects to reinforce learning and practical application of concepts.
- In-depth exploration of data preprocessing, feature engineering, model selection, and evaluation techniques.
- Guidance on building and deploying machine learning models for various applications, including classification, regression, clustering, and recommendation systems.
- Introduction to deep learning and neural networks using TensorFlow and Keras.
- Interactive lectures, demonstrations, and code-along sessions led by industry experts and experienced instructors.
- Access to a curated selection of datasets and resources for further study and practice.
- Ongoing support and mentorship to help participants succeed in their data science and machine learning journey.
Who Should Take This Course:
- Aspiring data scientists, machine learning engineers, and AI enthusiasts seeking to develop practical skills in Python-based data science and machine learning.
- Professionals looking to transition into data science roles or enhance their existing skill set with hands-on experience in Python.
- Students and researchers interested in exploring data science and machine learning concepts for academic or professional purposes.
Learning Outcomes:
- Master Python programming fundamentals and libraries essential for data science and machine learning.
- Acquire practical skills in data preprocessing, visualization, and analysis using Python.
- Build and evaluate predictive models for classification, regression, and clustering tasks.
- Gain proficiency in deep learning techniques for image classification and natural language processing.
- Develop the ability to interpret and communicate insights derived from data effectively.
- Apply best practices and methodologies for developing end-to-end machine learning solutions.
- Complete real-world projects demonstrating proficiency in data science and machine learning concepts.
- Prepare for career opportunities in data science, machine learning, and AI with a strong foundation and portfolio of projects.
Certification
Once you’ve successfully completed your course, you will immediately be sent a digital certificate. Also, you can have your printed certificate delivered by post (shipping cost £3.99). All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.Assessment
At the end of the Course, there will be an online assessment, which you will need to pass to complete the course. Answers are marked instantly and automatically, allowing you to know straight away whether you have passed. If you haven’t, there’s no limit on the number of times you can take the final exam. All this is included in the one-time fee you paid for the course itself.Curriculum
- Welcome & Course Overview
- Set-up the Environment for the Course (lecture 1)
- Protected: Set-up the Environment for the Course (lecture 2)
- Two other options to setup environment
- Protected: Python data types Part 1
- Protected: Python Data Types Part 2
- Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1)
- Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2)
- Python Essentials Exercises Overview
- Python Essentials Exercises Solutions
- Protected: What is Numpy? A brief introduction and installation instructions.
- NumPy Essentials – NumPy arrays, built-in methods, array methods and attributes.
- NumPy Essentials – Indexing, slicing, broadcasting & boolean masking
- NumPy Essentials – Arithmetic Operations & Universal Functions
- NumPy Essentials Exercises Overview
- Protected: NumPy Essentials Exercises Solutions
- What is pandas? A brief introduction and installation instructions.
- Pandas Introduction
- Protected: Pandas Essentials – Pandas Data Structures – Series
- Pandas Essentials – Pandas Data Structures – DataFrame
- Pandas Essentials – Handling Missing Data
- Protected: Pandas Essentials – Data Wrangling – Combining, merging, joining
- Pandas Essentials – Groupby
- Protected: Pandas Essentials – Useful Methods and Operations
- Protected: Pandas Essentials – Project 1 (Overview) Customer Purchases Data
- Pandas Essentials – Project 1 (Solutions) Customer Purchases Data
- Pandas Essentials – Project 2 (Overview) Chicago Payroll Data
- Protected: Pandas Essentials – Project 2 (Solutions Part 1) Chicago Payroll Data
- Protected: Matplotlib Essentials (Part 1) – Basic Plotting & Object Oriented Approach
- Matplotlib Essentials (Part 2) – Basic Plotting & Object Oriented Approach
- Matplotlib Essentials (Part 3) – Basic Plotting & Object Oriented Approach
- Matplotlib Essentials – Exercises Overview
- Matplotlib Essentials – Exercises Solutions
- Seaborn – Introduction & Installation
- Seaborn – Distribution Plots
- Seaborn – Categorical Plots (Part 1)
- Seaborn – Categorical Plots (Part 2)
- Seborn-Axis Grids
- Seaborn – Matrix Plots
- Seaborn – Regression Plots
- Protected: Seaborn – Controlling Figure Aesthetics
- Seaborn – Exercises Overview
- Seaborn – Exercise Solutions
- Pandas Built-in Data Visualization
- Pandas Data Visualization Exercises Overview
- Panda Data Visualization Exercises Solutions
- Plotly & Cufflinks – Interactive & Geographical Plotting (Part 1)
- Plotly & Cufflinks – Interactive & Geographical Plotting (Part 2)
- Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Overview)
- Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Solutions)
- Protected: Project 1 – Oil vs Banks Stock Price during recession (Overview)
- Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 1)
- Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 2)
- Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 3)
- Project 2 (Optional) – Emergency Calls from Montgomery County, PA (Overview)
- Protected: Introduction to ML – What, Why and Types…..
- Protected: Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff
- scikit-learn – Linear Regression Model – Hands-on (Part 1)
- scikit-learn – Linear Regression Model Hands-on (Part 2)
- Good to know! How to save and load your trained Machine Learning Model!
- Protected: scikit-learn – Linear Regression Model (Insurance Data Project Overview)
- scikit-learn – Linear Regression Model (Insurance Data Project Solutions)
- Protected: Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc.
- Protected: scikit-learn – Logistic Regression Model – Hands-on (Part 1)
- scikit-learn – Logistic Regression Model – Hands-on (Part 2)
- Protected: scikit-learn – Logistic Regression Model – Hands-on (Part 3)
- scikit-learn – Logistic Regression Model – Hands-on (Project Overview)
- Protected: scikit-learn – Logistic Regression Model – Hands-on (Project Solutions)
- Theory: K Nearest Neighbors, Curse of dimensionality ….
- Protected: scikit-learn – K Nearest Neighbors – Hands-on
- scikt-learn – K Nearest Neighbors (Project Overview)
- scikit-learn – K Nearest Neighbors (Project Solutions)
- Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging….
- scikit-learn – Decision Tree and Random Forests – Hands-on (Part 1)
- scikit-learn – Decision Tree and Random Forests (Project Overview)
- Protected: scikit-learn – Decision Tree and Random Forests (Project Solutions)
- Support Vector Machines (SVMs) – (Theory Lecture)
- scikit-learn – Support Vector Machines – Hands-on (SVMs)
- scikit-learn – Support Vector Machines (Project 1 Overview)
- scikit-learn – Support Vector Machines (Project 1 Solutions)
- Protected: scikit-learn – Support Vector Machines (Optional Project 2 – Overview)
- Theory: K Means Clustering, Elbow method …..
- scikit-learn – K Means Clustering – Hands-on
- scikit-learn – K Means Clustering (Project Overview)
- scikit-learn – K Means Clustering (Project Solutions)
- Protected: Theory: Principal Component Analysis (PCA)
- Protected: scikit-learn – Principal Component Analysis (PCA) – Hands-on
- Protected: scikit-learn – Principal Component Analysis (PCA) – (Project Overview)
- Protected: scikit-learn – Principal Component Analysis (PCA) – (Project Solutions)
- Theory: Recommender Systems their Types and Importance
- Protected: Python for Recommender Systems – Hands-on (Part 1)
- Python for Recommender Systems – – Hands-on (Part 2)
- Protected: Natural Language Processing (NLP) – (Theory Lecture)
- NLTK – NLP-Challenges, Data Sources, Data Processing …..
- NLTK – Feature Engineering and Text Preprocessing in Natural Language Processing
- NLTK – NLP – Tokenization, Text Normalization, Vectorization, BoW….
- NLTK – BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes …
- NLTK – NLP – Pipeline feature to assemble several steps for cross-validation…
Frequently Asked Questions
Contents Not Found