Complete Machine Learning & Data Science Bootcamp 2023

Description

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!

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.

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.

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.

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

18 sections • 98 lectures • 23:48:00 total length

•Welcome & Course Overview6: 00:07:00

•Set-up the Environment for the Course (lecture 1): 00:09:00

•Set-up the Environment for the Course (lecture 2): 00:25:00

•Two other options to setup environment: 00:04:00

•Python data types Part 1: 00:21:00

•Python Data Types Part 2: 00:15:00

•Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1): 00:16:00

•Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2): 00:20:00

•Python Essentials Exercises Overview: 00:02:00

•Python Essentials Exercises Solutions: 00:22:00

•What is Numpy? A brief introduction and installation instructions.: 00:03:00

•NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes.: 00:28:00

•NumPy Essentials - Indexing, slicing, broadcasting & boolean masking: 00:26:00

•NumPy Essentials - Arithmetic Operations & Universal Functions: 00:07:00

•NumPy Essentials Exercises Overview: 00:02:00

•NumPy Essentials Exercises Solutions: 00:25:00

•What is pandas? A brief introduction and installation instructions.: 00:02:00

•Pandas Introduction: 00:02:00

•Pandas Essentials - Pandas Data Structures - Series: 00:20:00

•Pandas Essentials - Pandas Data Structures - DataFrame: 00:30:00

•Pandas Essentials - Handling Missing Data: 00:12:00

•Pandas Essentials - Data Wrangling - Combining, merging, joining: 00:20:00

•Pandas Essentials - Groupby: 00:10:00

•Pandas Essentials - Useful Methods and Operations: 00:26:00

•Pandas Essentials - Project 1 (Overview) Customer Purchases Data: 00:08:00

•Pandas Essentials - Project 1 (Solutions) Customer Purchases Data: 00:31:00

•Pandas Essentials - Project 2 (Overview) Chicago Payroll Data: 00:04:00

•Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data: 00:18:00

•Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach: 00:13:00

•Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach: 00:22:00

•Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach: 00:22:00

•Matplotlib Essentials - Exercises Overview: 00:06:00

•Matplotlib Essentials - Exercises Solutions: 00:21:00

•Seaborn - Introduction & Installation: 00:04:00

•Seaborn - Distribution Plots: 00:25:00

•Seaborn - Categorical Plots (Part 1): 00:21:00

•Seaborn - Categorical Plots (Part 2): 00:16:00

•Seborn-Axis Grids: 00:25:00

•Seaborn - Matrix Plots: 00:13:00

•Seaborn - Regression Plots: 00:11:00

•Seaborn - Controlling Figure Aesthetics: 00:10:00

•Seaborn - Exercises Overview: 00:04:00

•Seaborn - Exercise Solutions: 00:19:00

•Pandas Built-in Data Visualization: 00:34:00

•Pandas Data Visualization Exercises Overview: 00:03:00

•Panda Data Visualization Exercises Solutions: 00:13:00

•Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1): 00:19:00

•Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2): 00:14:00

•Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview): 00:11:00

•Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions): 00:17:00

•Project 1 - Oil vs Banks Stock Price during recession (Overview): 00:15:00

•Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1): 00:18:00

•Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2): 00:18:00

•Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3): 00:17:00

•Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview): 00:03:00

•Introduction to ML - What, Why and Types..: 00:15:00

•Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff: 00:15:00

•scikit-learn - Linear Regression Model - Hands-on (Part 1): 00:17:00

•scikit-learn - Linear Regression Model Hands-on (Part 2): 00:19:00

•Good to know! How to save and load your trained Machine Learning Model!: 00:01:00

•scikit-learn - Linear Regression Model (Insurance Data Project Overview): 00:08:00

•scikit-learn - Linear Regression Model (Insurance Data Project Solutions): 00:30:00

•Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc.: 00:10:00

•scikit-learn - Logistic Regression Model - Hands-on (Part 1): 00:17:00

•scikit-learn - Logistic Regression Model - Hands-on (Part 2): 00:20:00

•scikit-learn - Logistic Regression Model - Hands-on (Part 3): 00:11:00

•scikit-learn - Logistic Regression Model - Hands-on (Project Overview): 00:05:00

•scikit-learn - Logistic Regression Model - Hands-on (Project Solutions): 00:15:00

•Theory: K Nearest Neighbors, Curse of dimensionality .: 00:08:00

•scikit-learn - K Nearest Neighbors - Hands-on: 00:25:00

•scikt-learn - K Nearest Neighbors (Project Overview): 00:04:00

•scikit-learn - K Nearest Neighbors (Project Solutions): 00:14:00

•Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging.: 00:18:00

•scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1): 00:19:00

•scikit-learn - Decision Tree and Random Forests (Project Overview): 00:05:00

•scikit-learn - Decision Tree and Random Forests (Project Solutions): 00:15:00

•Support Vector Machines (SVMs) - (Theory Lecture): 00:07:00

•scikit-learn - Support Vector Machines - Hands-on (SVMs): 00:30:00

•scikit-learn - Support Vector Machines (Project 1 Overview): 00:07:00

•scikit-learn - Support Vector Machines (Project 1 Solutions): 00:20:00

•scikit-learn - Support Vector Machines (Optional Project 2 - Overview): 00:02:00

•Theory: K Means Clustering, Elbow method.: 00:11:00

•scikit-learn - K Means Clustering - Hands-on: 00:23:00

•scikit-learn - K Means Clustering (Project Overview): 00:07:00

•scikit-learn - K Means Clustering (Project Solutions): 00:22:00

•Theory: Principal Component Analysis (PCA): 00:09:00

•scikit-learn - Principal Component Analysis (PCA) - Hands-on: 00:22:00

•scikit-learn - Principal Component Analysis (PCA) - (Project Overview): 00:02:00

•scikit-learn - Principal Component Analysis (PCA) - (Project Solutions): 00:17:00

•Theory: Recommender Systems their Types and Importance: 00:06:00

•Python for Recommender Systems - Hands-on (Part 1): 00:18:00

•Python for Recommender Systems - - Hands-on (Part 2): 00:19:00

•Natural Language Processing (NLP) - (Theory Lecture): 00:13:00

•NLTK - NLP-Challenges, Data Sources, Data Processing ..: 00:13:00

•NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing: 00:19:00

•NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW.: 00:19:00

•NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes : 00:13:00

•NLTK - NLP - Pipeline feature to assemble several steps for cross-validation: 00:09:00