Overview
In this age of technology, data science and machine learning skills have become highly demanding skill sets. In the UK a skilled data scientist can earn around £62,000 per year. If you are aspiring for a career in the IT industry, secure these skills before you start your journey. The Complete Machine Learning & Data Science Bootcamp 2023 course can help you out.
This course will introduce you to the essentials of Python. From the highly informative modules, you will learn about NumPy, Pandas and matplotlib. The course will help you grasp the skills required for using python for data analysis and visualisation. After that, you will receive step-by-step guidance on Python for machine learning. The course will then focus on the concepts of Natural Language Processing.
Upon successful completion of the course, you will receive a certificate of achievement. This certificate will help you elevate your resume. So enrol today!
How will I get my certificate?
You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.
Who is This course for?
Anyone with an interest in learning about data science can enrol in this course. It will help aspiring professionals develop the basic skills to build a promising career. Professionals already working in this can take the course to improve their skill sets.
Requirements
The students will not require any formal qualifications or previous experience to enrol in this course. Anyone can learn from the course anytime from anywhere through smart devices like laptops, tabs, PC, and smartphones with stable internet connections. They can complete the course according to their preferable pace so, there is no need to rush.
Career Path
This course will equip you with valuable knowledge and effective skills in this area. After completing the course, you will be able to explore career opportunities in the fields such as
- Data Analyst
- Data Scientist
- Data Manager
- Business Analyst
Course Curriculum
- 18 sections
- 98 lectures
- 23 hours, 48 minutes total length
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Welcome & Course Overview6 00:07:00 -
Set-up the Environment for the Course (lecture 1) 00:09:00
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Set-up the Environment for the Course (lecture 2) 00:25:00
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Two other options to setup environment 00:04:00
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Python data types Part 1 00:21:00
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Python Data Types Part 2 00:15:00
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Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00
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Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00
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Python Essentials Exercises Overview 00:02:00
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Python Essentials Exercises Solutions 00:22:00
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What is Numpy? A brief introduction and installation instructions. 00:03:00
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NumPy Essentials – NumPy arrays, built-in methods, array methods and attributes. 00:28:00
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NumPy Essentials – Indexing, slicing, broadcasting & boolean masking 00:26:00
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NumPy Essentials – Arithmetic Operations & Universal Functions 00:07:00
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NumPy Essentials Exercises Overview 00:02:00
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NumPy Essentials Exercises Solutions 00:25:00
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What is pandas? A brief introduction and installation instructions. 00:02:00
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Pandas Introduction 00:02:00
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Pandas Essentials – Pandas Data Structures – Series 00:20:00
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Pandas Essentials – Pandas Data Structures – DataFrame 00:30:00
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Pandas Essentials – Handling Missing Data 00:12:00
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Pandas Essentials – Data Wrangling – Combining, merging, joining 00:20:00
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Pandas Essentials – Groupby 00:10:00
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Pandas Essentials – Useful Methods and Operations 00:26:00
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Pandas Essentials – Project 1 (Overview) Customer Purchases Data 00:08:00
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Pandas Essentials – Project 1 (Solutions) Customer Purchases Data 00:31:00
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Pandas Essentials – Project 2 (Overview) Chicago Payroll Data 00:04:00
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Pandas Essentials – Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00
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Matplotlib Essentials (Part 1) – Basic Plotting & Object Oriented Approach 00:13:00
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Matplotlib Essentials (Part 2) – Basic Plotting & Object Oriented Approach 00:22:00
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Matplotlib Essentials (Part 3) – Basic Plotting & Object Oriented Approach 00:22:00
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Matplotlib Essentials – Exercises Overview 00:06:00
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Matplotlib Essentials – Exercises Solutions 00:21:00
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Seaborn – Introduction & Installation 00:04:00
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Seaborn – Distribution Plots 00:25:00
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Seaborn – Categorical Plots (Part 1) 00:21:00
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Seaborn – Categorical Plots (Part 2) 00:16:00
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Seborn-Axis Grids 00:25:00
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Seaborn – Matrix Plots 00:13:00
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Seaborn – Regression Plots 00:11:00
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Seaborn – Controlling Figure Aesthetics 00:10:00
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Seaborn – Exercises Overview 00:04:00
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Seaborn – Exercise Solutions 00:19:00
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Pandas Built-in Data Visualization 00:34:00
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Pandas Data Visualization Exercises Overview 00:03:00
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Panda Data Visualization Exercises Solutions 00:13:00
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Plotly & Cufflinks – Interactive & Geographical Plotting (Part 1) 00:19:00
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Plotly & Cufflinks – Interactive & Geographical Plotting (Part 2) 00:14:00
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Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Overview) 00:11:00
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Plotly & Cufflinks – Interactive & Geographical Plotting Exercises (Solutions) 00:17:00
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Project 1 – Oil vs Banks Stock Price during recession (Overview) 00:15:00
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Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00
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Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00
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Project 1 – Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00
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Project 2 (Optional) – Emergency Calls from Montgomery County, PA (Overview) 00:03:00
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Introduction to ML – What, Why and Types….. 00:15:00
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Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00
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scikit-learn – Linear Regression Model – Hands-on (Part 1) 00:17:00
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scikit-learn – Linear Regression Model Hands-on (Part 2) 00:19:00
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Good to know! How to save and load your trained Machine Learning Model! 00:01:00
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scikit-learn – Linear Regression Model (Insurance Data Project Overview) 00:08:00
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scikit-learn – Linear Regression Model (Insurance Data Project Solutions) 00:30:00
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Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificity…etc. 00:10:00
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scikit-learn – Logistic Regression Model – Hands-on (Part 1) 00:17:00
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scikit-learn – Logistic Regression Model – Hands-on (Part 2) 00:20:00
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scikit-learn – Logistic Regression Model – Hands-on (Part 3) 00:11:00
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scikit-learn – Logistic Regression Model – Hands-on (Project Overview) 00:05:00
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scikit-learn – Logistic Regression Model – Hands-on (Project Solutions) 00:15:00
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Theory: K Nearest Neighbors, Curse of dimensionality …. 00:08:00
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scikit-learn – K Nearest Neighbors – Hands-on 00:25:00
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scikt-learn – K Nearest Neighbors (Project Overview) 00:04:00
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scikit-learn – K Nearest Neighbors (Project Solutions) 00:14:00
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Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging…. 00:18:00
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scikit-learn – Decision Tree and Random Forests – Hands-on (Part 1) 00:19:00
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scikit-learn – Decision Tree and Random Forests (Project Overview) 00:05:00
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scikit-learn – Decision Tree and Random Forests (Project Solutions) 00:15:00
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Support Vector Machines (SVMs) – (Theory Lecture) 00:07:00
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scikit-learn – Support Vector Machines – Hands-on (SVMs) 00:30:00
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scikit-learn – Support Vector Machines (Project 1 Overview) 00:07:00
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scikit-learn – Support Vector Machines (Project 1 Solutions) 00:20:00
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scikit-learn – Support Vector Machines (Optional Project 2 – Overview) 00:02:00
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Theory: K Means Clustering, Elbow method. 00:11:00
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scikit-learn – K Means Clustering – Hands-on 00:23:00
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scikit-learn – K Means Clustering (Project Overview) 00:07:00
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scikit-learn – K Means Clustering (Project Solutions) 00:22:00
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Theory: Principal Component Analysis (PCA) 00:09:00
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scikit-learn – Principal Component Analysis (PCA) – Hands-on 00:22:00
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scikit-learn – Principal Component Analysis (PCA) – (Project Overview) 00:02:00
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scikit-learn – Principal Component Analysis (PCA) – (Project Solutions) 00:17:00
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Theory: Recommender Systems their Types and Importance 00:06:00
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Python for Recommender Systems – Hands-on (Part 1) 00:18:00
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Python for Recommender Systems – – Hands-on (Part 2) 00:19:00
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Natural Language Processing (NLP) – (Theory Lecture) 00:13:00
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NLTK – NLP-Challenges, Data Sources, Data Processing ….. 00:13:00
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NLTK – Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00
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NLTK – NLP – Tokenization, Text Normalization, Vectorization, BoW…. 00:19:00
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NLTK – BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes … 00:13:00
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NLTK – NLP – Pipeline feature to assemble several steps for cross-validation… 00:09:00
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This course includes:
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Duration:23 hours, 48 minutes
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Access:1 year access
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Units:98
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Level:
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CPD Points:10
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Certificate:Yes
