COURSE CURRICULUM
Module 01 : Introduction to Python for Data Science & Machine Learning From A-Z
- Who is this Course for?
- Data Science + Machine Learning Market Place
- Data Science Job Opportunities
- Data Science Job Roles
- What is a Data Scientist?
- How to Get a Data Science Job
- Data Science Projects Overview
Module 02 : Data Science & Machine Learning Concepts
- Why We Use Python
- What is Data Science?
- What is Machine Learning?
- Machine Learning Concepts & Algorithms
- What is Deep Learning?
- Machine Learning vs Deep Learning
Module 03 : Python for Data Science
- What is Programming?
- Why Python for Data Science?
- What is Jupyter?
- What is Google Colab?
- Python variables, booleans
- Getting Started with Google Colab
- Python Operators
- Python Strings
- Python Numbers & Booleans
- Python Conditional Statements
- Python for Loops and While Loops
- Python Lists
- More About Lists
- Python Tuples
- Python Dictionaries
- Python Sets
- Compound Data Types & When to Use Each One?
- Python Functions
- Object - Oriented Programming in Python
Module 04 : Statistics for Data Science
- Introduction to Statistics
- Descriptive statistics
- Measure of Variability
- Measure of Variability Continued
- Measures of Variable Relationship
- Inferential Statistics
- Measure of Asymmetry
- Sampling Distribution
Module 05 : Probability and Hypothesis Testing
- What Exactly is Probability?
- Expected Values
- Relative Frequency
- Hypothesis Testing Overview
Module 06 : NumPy Data Analysis
- Intro Numpy Array Data Types
- Numpy Arrays
- Numpy Arrays Basics
- Numpy Array Indexing
- Numpy Array Computations
- Broadcasting
Module 07 : Pandas Data Analysis
- Intro to Pandas
- Intro to Pandas Continued
Module 08 : Python Data Visualization
- Data Visualization Overview
- Different Data Visualization Libraries in Python
- Python Data Visualization Implementation
Module 09 : Introduction to Machine Learning
- Introduction to Machine Learning
Module 10 : Data Loading & Exploration
- Exploratory Data Analysis
Module 11 : Data Cleaning
- Feature Scaling
- Data Cleaning
Module 12 : Feature Selecting and Engineering
Module 13 : Linear and Logistic Regression
- Linear Regression Intro
- Gradient Descent
- Linear Regression + Correlation Methods
- Linear Regression Implementation
- Logistic Regression
Module 14 : K Nearest Neighbors
- Parametric Vs Non - Parametric Models
- Eda on Iris Dataset
- The KNN Intuition
- Implement the KNN Algorithm from Scratch
- Compare the Result with the Sklearn Library
- Hyperparameter Tuning Using the Cross - Validation
- The Decision Boundary Visualization
- Manhattan Vs Euclidean Distance
- Feature Scaling in KNN
- Curse of Dimensionality
- KNN Use Cases
- KNN Pros and Cons
Module 15 : Decision Trees
- Decision Trees Section Overview
- EDA on Adult Dataset
- What is Entropy and Information Gain?
- The Decision Tree ID3 Algorithm from Scratch Part 1
- The Decision Tree ID3 Algorithm from Scratch Part 2
- The Decision Tree ID3 Algorithm from Scratch Part 3
- ID3 - Putting Everything Together
- Evaluating our ID3 Implementation
- Compare with Sklearn Implementation
- Visualizing the Tree
- Plot the Important Features
- Decision Trees Hyper - Parameters
- Pruning
- [Optional] Gain Ration
- Decision Trees Pros and Cons
- Predict Whether Income Exceeds $50K/yr - Overview
Module 16 : Ensemble Learning and Random Forests
- Ensemble Learning Section Overview
- What is Ensemble Learning?
- What is Bootstrap Sampling?
- What is Bagging?
- Out-of-Bag Error (OOB Error)
- Implementing Random Forests from Scratch Part 1
- Implementing Random Forests from Scratch Part 2
- Compare with Sklearn Implementation
- Random Forests Hyper-Parameters
- Random Forests Pros and Cons
- What is Boosting?
- AdaBoost Part 1
- AdaBoost Part 2
Module 17 : Support Vector Machines
- SVM Outline
- SVM Intuition
- Hard vs Soft Margins
- C Hyper - Parameter
- Kernel Trick
- SVM - Kernel Types
- SVM with Linear Dataset (Iris)
- SVM with Non-Linear Dataset
- SVM with Regression
- [Project] Voice Gender Recognition Using SVM
Module 18 : K-Means
- Unsupervised Machine Learning Introduction
- Unsupervised Machine Learning Continued
- Data Standardization
Module 19 : PCA
- PCA Section Overview
- What is PCA?
- PCA Drawbacks
- PCA Algorithm Steps (Mathematics)
- Covariance Matrix vs SVD
- PCA - Main Applications
- PCA - Image Compression
- PCA Data Preprocessing
- PCA - Biplot and the Screen Plot
- PCA - Feature Scaling and Screen Plot
- PCA - Supervised vs Unsupervised
- PCA - Visualization
Module 20 : Data Science Career
- Creating a Data Science Resume
- Data Science Cover Letter
- How to Contact Recruiters
- Getting Started with Freelancing
- Top Freelance Websites
- Personal Branding
- Networking
- Importance of a Website
Method of Assessment
This is a knowledge-based course, and thus, will contain no method of assessment.
Certification
Upon the successful completion of the course, learners will be awarded an accredited ‘Certificate of Completion’ for ‘Python for Data Science & Machine Learning - Level 3’ by IAP.
About the Tutor
Juan Galvan is a visionary marketer, and digital entrepreneur. He has been effective in enabling digital businesses to reach the next level of success. He believes in continued education and wants to share his extensive knowledge and experience as a coach, consultant, and strategist with others. He aims to enable learners to expand their skillset in digital marketing, web development, programming, and e-commerce. Juan Galvan will guide you to make critical business decisions, develop unique ways to deliver products in the marketplace, and have clarity and confidence in your business.
