12-Week Machine Learning For Beginners - ML Skills Academy

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Course Content

WEEK 1: Introduction to Machine Learning

Getting Started With Machine Learning

Applications of Machine Learning

Solving Problems With Machine Learning

WEEK 2: Introduction to Python For Machine Learning

Getting started With Python for Machine Learning

Setting up Python Environment, Jupyter notebooks

Basics of Data Types, Variables, and Operations in Python

Data Structures: Lists, Tuples, Dictionaries, and Sets

Control Flow: If Statements, Loops, and Functions

Exception Handling, File Handling

Other Python Concepts

Setting Up Your GitHub Account

Hands-on: Python

WEEK 3: Introduction to Numpy, Pandas, Scikit-learn and Matplotlib

Introduction to NumPy

NumPy for Numerical Computing in Python

Other NumPy Concepts

Introduction to Pandas

Datatypes in Data Analysis

Pandas for Reading Data, Data Manipulation and Analysis

Creating Dataframes and Performing Basic Manipulation Functions

Visualisations Using Pandas

Hands-on: NumPy and Pandas

Introduction to Matplotlib

Advanced Matplotlib Features

Introduction to Scikit-learn

Hands-On: Matplotlib and Scikit-Learn

WEEK 4: Essential Statistics For Machine Learning

Introduction Statistics for Machine Learning

Key Concepts of Descriptive Statistics

Key Concepts of Inferential Statistics

Introduction to Statistical Tests

Statistical Tests: t-test and ANOVA

Statistical tests: Chi-Square Test

Hands-on: Descriptive and Inferential Statistics

WEEK 5: Introduction to Probability for Machine Learning

Introduction to Probability

Applications of Probability in Machine Learning

Probabilistic Modelling

Hands-on: Probability and Naïve Bayes

WEEK 6: Fundamental Supervised Machine Learning Techniques

Introduction to Supervised Machine Learning

Applications of Supervised Machine Learning

Introduction to Linear Regression

Assumptions for Linear Regression

Hands-On: Linear Regression

Logistic Regression

Hands-On: Logistic Regression

WEEK 7: Advanced Supervised Learning Techniques

K-Nearest Neighbour Algorithm

Hands On: K-Nearest Neighbour

Confusion Matrix

Hands-On: Decision Tree and Random Forests

WEEK 8: Fundamental Unsupervised Learning Techniques

Introduction to Unsupervised Learning Algorithms

Applications of Unsupervised Learning Algorithms

K-Means Clustering

K-Means Clustering Examples

Hierarchical Clustering

Hierarchical Clustering Examples

Dimensionality reduction with PCA

Hands On: Fundamental Unsupervised Learning Techniques

WEEK 9: Advanced Unsupervised Learning Techniques

Density-Based Clustering (DBSCAN)

Association Rules

Anomaly Detection

Hands-on: Advanced Unsupervised Learning Techniques

WEEK 10: Cross Validation and Hyperparameter Tuning

Introduction to Cross Validation

Cross Validation Examples

Hyperparameter Tuning with Grid Search and Random Search

Hyperparameter Tuning Examples

Hands-On Project – Cross Validation and Hyperparameter Tuning in a Real-World Dataset

Week 11-12: Build Your Machine Learning Portfolio

Introduction to Machine Learning Capstone Projects

Finance Projects

Ecommerce Projects

Healthcare Projects

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