Python Machine Learning: From Beginner To Pro

Machine Learning Tutorial: Python-Based Predictive Analytics

What you'll learn

Gain a solid understanding of Python programming, including syntax, data structures, and control flow.

Explore the core principles and algorithms of machine learning, such as supervised and unsupervised learning.

Learn techniques for cleaning, preparing, and transforming data for machine learning models.

Discover methods for creating new features or selecting relevant features for model building.

Requirements

No experience required

Description

Are you eager to dive into the exciting world of machine learning and harness the power of Python? This comprehensive course is designed to guide you from a beginner to a proficient machine learning practitioner.Key Learning Objectives:Master Python Fundamentals: Gain a solid understanding of Python programming, essential for machine learning.Explore Machine Learning Concepts: Learn the core principles and algorithms of machine learning, including supervised and unsupervised learning.Work with Real-World Datasets: Practice data cleaning, preprocessing, and feature engineering using real-world datasets.Build Predictive Models: Develop various machine learning models, such as linear regression, logistic regression, decision trees, random forests, and neural networks.Evaluate Model Performance: Learn to assess model accuracy, precision, recall, and other metrics.Apply Machine Learning in Practice: Discover real-world applications of machine learning in fields like finance, healthcare, and marketing.Course Highlights:Hands-On Projects: Engage in practical exercises and projects to reinforce your learning.Step-by-Step Guidance: Follow clear explanations and coding examples.Real-World Examples: Explore real-world use cases of machine learning.Expert Instruction: Learn from experienced machine learning professionals.Lifetime Access: Enjoy unlimited access to course materials.Who This Course is For:Beginners in machine learning who want to learn Python.Data analysts or scientists looking to enhance their skills.Professionals seeking to apply machine learning to their work.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Introduction to Machine Learning with Python

Lecture 3 AI vs ML vs Deep Learning

Lecture 4 How does Machine Learning Work

Lecture 5 Types of Machine Learning

Lecture 6 Supervised Learning & Examples

Lecture 7 Unsupervised Learning & Examples

Lecture 8 Reinforcement Learning & Examples

Lecture 9 Examples of AI

Lecture 10 Deep Learning & Examples

Lecture 11 Jupyter & Installation

Lecture 12 Machine Learning Tutorial & Algorithms

Lecture 13 Demo of Iris Dataset

Lecture 14 Linear Regression & Value of R2

Lecture 15 Statistics and Probability Concepts-

Lecture 16 Category of Data for Machine Learning

Lecture 17 Qualitative data and Quantitative data in machine learning

Lecture 18 Information gain & Entropy and Confusion Matrix

Lecture 19 Types of event and Probability of Distribution

Lecture 20 How to Import Datasets in Jupyter

Lecture 21 Data Analysis

Lecture 22 Train & Test Data

Lecture 23 Logistic Regression Curve

Lecture 24 Decision Tree

Lecture 25 Class project 1

Lecture 26 Class project 2

Beginners in machine learning who want to learn the fundamentals using Python.