Hands-On Machine Learning with Python: Real Projects

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1 - Introduction to Machine Learning

1 -What is Machine Learning.mp4

05:43

2 -Types of Machine Learning.mp4

03:29

3 -Machine Learning Workflow.mp4

03:12

4 -Python Libraries for Machine Learning.mp4

03:50

5 -Hands-on Setting up Python Environment.mp4

02:26

2 - Data Preprocessing

1 -Data Cleaning.mp4

04:28

2 -Handling Missing Data.mp4

02:54

3 -Encoding Categorical Data.mp4

06:28

4 -Feature Scaling.mp4

04:48

5 -Hands-on Preprocessing Data in Python.mp4

02:58

3 - Supervised Learning Algorithms

1 -Linear Regression.mp4

05:52

2 -Logistic Regression.mp4

07:21

3 -Decision Trees.mp4

05:27

4 -Support Vector Machines.mp4

03:26

5 -Hands-on Implementing Algorithms in Python.mp4

03:00

4 - Unsupervised Learning Algorithms

1 -K-Means Clustering.mp4

05:20

2 -Hierarchical Clustering.mp4

05:08

3 -Principal Component Analysis (PCA).mp4

06:55

4 -Association Rule Learning.mp4

05:10

5 -Hands-on Clustering and Dimensionality Reduction in Python.mp4

02:11

5 - Model Evaluation and Selection

1 -Cross-Validation.mp4

03:23

2 -Evaluation Metrics.mp4

03:38

3 -Hyperparameter Tuning.mp4

03:57

4 -Model Selection.mp4

04:14

5 -Hands-on Evaluating and Selecting Models in Python.mp4

03:07

6 - Deep Learning with TensorFlow

1 -Introduction to Neural Networks.mp4

04:23

2 -TensorFlow Basics.mp4

05:40

3 -Building Neural Networks in TensorFlow.mp4

04:06

4 -Convolutional Neural Networks (CNN).mp4

05:59

5 -Hands-on Implementing Deep Learning Models in TensorFlow.mp4

04:50

7 - Natural Language Processing (NLP)

1 -Text Preprocessing.mp4

03:23

2 -Bag of Words Model.mp4

04:18

3 -Word Embeddings.mp4

05:28

4 -Named Entity Recognition.mp4

04:55

5 -Hands-on NLP Techniques in Python.mp4

05:33

8 - Deployment and Production

1 -Model Deployment.mp4

04:38

2 -Web Applications with Flask.mp4

10:09

3 -Scalability and Production Readiness.mp4

06:29

4 -Monitoring and Maintenance.mp4

06:40

Description

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.

Who is this for?

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.

What You Need to Know?

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!

More details

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 youâ€™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!
Who this course is for:
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.

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