Machine Learning with Python and Scikit-Learn – Full Course

This course is a practical and hands-on introduction to Machine Learning with Python and Scikit-Learn for beginners with basic knowledge of Python and statistics.

We'll start with the basics of machine learning by exploring models like linear & logistic regression and then move on to tree-based models like decision trees, random forests, and gradient-boosting machines. We'll also discuss best practices for approaching and managing machine learning projects and build a state-of-the-art machine learning model for a real-world dataset from scratch. We'll also look at unsupervised learning & recommendations briefly and walk through the process of deploying a machine-learning model to the cloud using the Flask web framework.

By the end of this course, you'll be able to confidently build, train, and deploy machine learning models in the real world. To get the most out of this course, follow along & type out all the code yourself, and apply the techniques covered here to other real-world datasets & competitions that you can find on platforms like Kaggle.

⭐️ Topics & Notebooks ⭐️

⌨️ (00:00:00) Introduction
⌨️ (00:00:25) Lesson 1 - Linear Regression and Gradient Descent
⌨️ (02:17:30) Lesson 2 - Logistic Regression for Classification
⌨️ (04:53:26) Lesson 3 - Decision Trees and Random Forests
⌨️ (07:25:29) Lesson 4 - How to Approach Machine Learning Projects
⌨️ (10:06:13) Lesson 5 - Gradient Boosting Machines with XGBoost
⌨️ (12:20:57) Lesson 6 - Unsupervised Learning using Scikit-Learn
⌨️ (13:53:18) Lesson 7 - Machine Learning Project from Scratch
⌨️ (16:45:47) Lesson 8 - Deploying a Machine Learning Project with Flask

🎉 Thanks to our Champion and Sponsor supporters:
👾 davthecoder
👾 jedi-or-sith
👾 南宮千影
👾 Agustín Kussrow
👾 Nattira Maneerat
👾 Heather Wcislo
👾 Serhiy Kalinets
👾 Justin Hual
👾 Otis Morgan
👾 Oscar Rahnama

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[jovianhq]

If you have any questions or face issues, please post them in the comments and we'll help you out. Do check out our YouTube channel as well, where we're posting new tutorials every week.

Thanks @freecodecamp and Beau for hosting our course here! 🙏🏼

[naamnhibataunga]

sir i have just started data science doing your zerotopandas course, the way you explain topic is just magical, looking forward to learn many great topics from you..

[fkaraal]

That's a well-organized and great articulation for machine learning learners. Thanks for all efforts

[amitsahoo]

Great work Sir..i have just completed 30% of it so far but kind of getting addicted to it with each passing day.

[siddharthpangotra]

Wow Aakash! this is one of the best explained practical ML course. The course that intend to explain a beginner that how an ML professional would look and solve the ML problem

[steviej]

thank you so much! I am getting started with ML and this is a great intro!
Keep it up!

[mahapeyuw]

This is very wonderful. Thanks for this! The presence of projects is wild!

[biscotty]

Thank you for this video, Great content, and I'm enjoying it very much. At about 2:10 I start to get very different results. My results match the notebook I downloaded from jovian, but neither match the video. There are small differences in the loss, but the output of the feature/weight dataframe is entirely different.

[yashvishah]

I'm so excited for this course !!!
I've taken Jovian's data analysis and visualization course on FCC and it was amazing. 😍

[zahid]

Great work bro. Thanks a lot, and really grateful to you.

I believe, you could add some prerequisites for this course like (1) a basic understanding of the linear and logistics regression, (2) basic python (3) basic data management with excel. All these would have raised the scaffolding for the learners before they jump on to the bandwagon of Machine Learning, tempted by its sexy appeal, and get dissapointed by the complexity.😂

[hosseinrezazadeh]

i don't lnow how to say thanks and appreciate for this course... this course solved actually 99% of my questions. thanks a lot.

[akashojha]

Lot of thank provide full lecture of machine learning ❤❤

[ishantmehndiratta]

Please make a full course on mathematics and algorithm writting, include different sorting algorithms, rating/ranking algorithm, search algorithm, likes/dislikes algorithms used to suggest contents and basic to intermediate mathematics used in algorithms and include shors algoritm and also implementation of those algorithms and writing our own algorithm) plzzz use python as base language

[jawadfx]

Right on time was looking for an in-depth Machine Learning and Scikit-Learn course

[zeefudeking]

Akash was my goto guru for learning machine learning using python. Unfortunately due to work deadline i cudnt cope up with pace. Brilliant tutor!

[chykebabagaming]

Do we always have to scale features in logistic regression? Also can scaling be applied to features when using the Linear model?

[subramanianchenniappan]

wow great. I am a java developer . wanted to learn this😍😍

[aleksandartta]

Do we need in practice to check whether our train, validation and test datasets comparable? Some comment and recommendation please.
P.s. thank you for this great tutorial

[abdulkareem-nmbk]

This is very wonderful & Awesome. The way of explanation is very nice & everybody can understand. Can you please post similar way of Generative AI & LLM

[PonzhiAghan]

On the first chapter I applied mean squared error and it took me time to realise that the unusually large number was the square of what I was looking for.

Imagine expecting a few 10s of thousands and you're getting millions. You do everything you know right, even use AI and the number refused to go away. That wasn't pleasant 😕... at all.

I later took the sqrt and the problem was resolved!