Hands-On Machine Learning With Python: Real Projects

Master Machine Learning with Python: Build, Train & Deploy Models with Real-World Projects

What you'll learn

Implement Machine Learning algorithms in Python using libraries like scikit-learn and TensorFlow.

Preprocess and analyze datasets to build predictive models.

Evaluate model performance and select the best algorithms for various problems.

Develop and deploy real-world machine learning applications from scratch.

Requirements

Basic knowledge of Python programming is helpful but not mandatory.

No prior experience in Machine Learning required – we’ll start from the basics.

A computer with Python and essential libraries installed (instructions provided in the course).

Curiosity and a willingness to learn – the course is designed for all levels!

Description

Dive into the exciting world of Machine Learning with our comprehensive course designed for aspiring data scientists, Python developers, and AI enthusiasts. This course will equip you with the essential skills and practical knowledge to harness the power of Machine Learning using Python.You will begin with the fundamentals of Machine Learning, exploring its definition, types, and workflow, while setting up your Python environment. As you progress, you'll delve into data preprocessing techniques to ensure your datasets are clean and ready for analysis.The course covers supervised and unsupervised learning algorithms, including Linear Regression, Decision Trees, K-Means Clustering, and Principal Component Analysis. Each section features hands-on projects that reinforce your understanding and application of these concepts in Python.You will learn to evaluate and select models using metrics and hyperparameter tuning, ensuring your solutions are both effective and efficient. Our in-depth exploration of Deep Learning with TensorFlow will introduce you to neural networks and advanced architectures like Convolutional Neural Networks (CNN).Additionally, you'll discover the essentials of Natural Language Processing (NLP), mastering text preprocessing and word embeddings to extract insights from textual data. As you approach the course's conclusion, you will gain valuable skills in model deployment, learning how to create web applications using Flask and ensure your models are production-ready.Cap off your learning journey with a real-world capstone project where you will apply everything you’ve learned in an end-to-end Machine Learning workflow, culminating in a presentation and peer review.Whether you are a beginner eager to enter the field or a professional looking to enhance your skill set, this course provides the tools and knowledge necessary to succeed in the dynamic landscape of Machine Learning. Join us and take the first step toward mastering Machine Learning in Python today!

Overview

Section 1: Introduction to Machine Learning

Lecture 1 What is Machine Learning?

Lecture 2 Types of Machine Learning

Lecture 3 Machine Learning Workflow

Lecture 4 Python Libraries for Machine Learning

Lecture 5 Hands-on: Setting up Python Environment

Section 2: Data Preprocessing

Lecture 6 Data Cleaning

Lecture 7 Handling Missing Data

Lecture 8 Encoding Categorical Data

Lecture 9 Feature Scaling

Lecture 10 Hands-on: Preprocessing Data in Python

Section 3: Supervised Learning Algorithms

Lecture 11 Linear Regression

Lecture 12 Logistic Regression

Lecture 13 Decision Trees

Lecture 14 Support Vector Machines

Lecture 15 Hands-on: Implementing Algorithms in Python

Section 4: Unsupervised Learning Algorithms

Lecture 16 K-Means Clustering

Lecture 17 Hierarchical Clustering

Lecture 18 Principal Component Analysis (PCA)

Lecture 19 Association Rule Learning

Lecture 20 Hands-on: Clustering and Dimensionality Reduction in Python

Section 5: Model Evaluation and Selection

Lecture 21 Cross-Validation

Lecture 22 Evaluation Metrics

Lecture 23 Hyperparameter Tuning

Lecture 24 Model Selection

Lecture 25 Hands-on: Evaluating and Selecting Models in Python

Section 6: Deep Learning with TensorFlow

Lecture 26 Introduction to Neural Networks

Lecture 27 TensorFlow Basics

Lecture 28 Building Neural Networks in TensorFlow

Lecture 29 Convolutional Neural Networks (CNN)

Lecture 30 Hands-on: Implementing Deep Learning Models in TensorFlow

Section 7: Natural Language Processing (NLP)

Lecture 31 Text Preprocessing

Lecture 32 Bag of Words Model

Lecture 33 Word Embeddings

Lecture 34 Named Entity Recognition

Lecture 35 Hands-on: NLP Techniques in Python

Section 8: Deployment and Production

Lecture 36 Model Deployment

Lecture 37 Web Applications with Flask

Lecture 38 Scalability and Production Readiness

Lecture 39 Monitoring and Maintenance

Beginners interested in Machine Learning who want to learn through hands-on projects.,Python developers looking to expand their skills in data science and machine learning.,Data analysts and statisticians eager to apply machine learning techniques to real-world problems.,Anyone curious about AI and Machine Learning who wants to build practical models without prior experience.