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Learn Machine Learning from an AI entrepreneur with extensive industry experience and a popular YouTube channel (Codebasics) with 1 million subscribers. This course takes you from beginner to advanced levels, providing deep intuition on algorithms, engaging cinematic experiences, end-to-end projects, and hands-on coding practice. Designed for easy understanding, even for high school students, all at an affordable price.
5.0
(17 Verified ratings)
Last Updated: Jul 17, 2024 2:28 AM
|English
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Start from scratch
and build up
Learn at your
own pace
Acquire essential
job skills
Created by:
Free Lifetime Access
Start from scratch
and build up
Learn at your
own pace
Acquire essential
job skills
Basics of Python Programming Language
Pandas and Exploratory Data Analysis
Math and Statistics for Machine Learning and Data Science
Supervised Machine Learning (Regression & Classification)
Unsupervised Learning
Feature Engineering
Model Evaluation Techniques
End to End Projects in Healthcare and Finance
Machine Learning Ops (ML Ops)
Practicing through Exercises, Quizzes, and Certificate Upon Course Completion
199 Lectures | 24hr : 37min
43 Lectures
MUST WATCH: Go through this chapter ONLY IF
Skip This Chapter - Quiz
Setup Environment (Local Python and Google Colab)
Variables
Variables - Quiz
Variables - Exercise
Numbers
Numbers - Quiz
Numbers- Exercise
Strings
Strings - Quiz
Strings - Exercise
Lists
Lists - Quiz
Lists - Exercise
Install Pycharm
If Condition
If Condition - Quiz
If Condition - Exercise
For Loop
For loop - Quiz
For loop - Exercise
Functions
Functions -Quiz
Functions - Exercise
Dictionary and Tuples
Dictionary and Tuples - Quiz
Dictionary and Tuples - Exercise
Modules and Pip
Modules and Pip - Quiz
Modules and Pip - Exercise
File Handling
File Handling - Quiz
File handling - Exercise
Classes and Objects
Classes and Objects - Quiz
Classes and Objects - Exercise
Inheritance
Inheritance - Quiz
Inheritance - Exercise
Exception Handling
Exception Handling - Quiz
Exception Handling - Exercise
12 Lectures
MUST WATCH: Go through this chapter ONLY IF
Skip This Chapter - Quiz
Pandas Introduction and Installation
Dataframe Basics
Read, Write Excel and CSV Files
Handle Missing Data - Part 1
Handle Missing Data - Part 2
Grouping Data
Data Concatenation and Merging
Data Visualization Using Matplotlib and Seaborn
Data God Showing the way
Quiz
46 Lectures
MUST WATCH: Go through this chapter ONLY IF
Skip This Chapter - Quiz
Descriptive vs. Inferential Statistics
Measures of Central Tendency: Mean, Median, Mode
Percentile
Analysis: Shoe Sales (Using Mean, Median, Percentile)
Quiz
Exercise
Measures of Dispersion: Range, IQR
Box or Whisker Plot
Outlier Treatment Using IQR and Box Plot
Quiz
Exercise
Measures of Dispersion: Variance and Standard Deviation
Analysis: Stock Returns Volatility (Using Variance and Std Dev)
Correlation
Correlation vs Causation
Quiz
Exercise
Probability Basics
Quiz
Addition and Multiplication Rule
Quiz
Conditional Probability and Bayes Theorem
Quiz
What Is a Distribution?
Skewness
Normal Distribution
Detect Outliers Using Normal Distribution
Quiz
Exercise
Z Score
Standard Normal Distribution (SND)
Quiz
Exercise
Random Sampling & Sample Bias
The Law of Large Numbers
Central Limit Theorem, Sampling Distribution
Case Study: Solar Panels
Standard Error
Quiz
Z Score Table (Z-Table)
Quiz
Confidence Interval
Confidence Interval: Estimate Car Miles
Exercise
29 Lectures
Simple Linear Regression
Multiple Linear Regression
Quiz
Exercise
Cost Function
Derivatives and Partial Derivatives
Chain Rule
Quiz
Exercise
Gradient Descent Theory
Gradient Descent: Python Implementation
Why MSE (and not MAE)?
Model Evaluation: Train, Test Split
Model Evaluation: Metrics
Peter Pandey Flexes his ML skills on LinkedIn
Quiz
Exercise
Data Preprocessing: One Hot Encoding
Quiz
Polynomial Regression
Quiz
Exercise
Overfitting and Underfitting
Reasons and Remedies For Overfitting / Underfitting
L1 and L2 Regularization
Bias Variance Trade Off
Quiz
Exercise
Chapter Summary
30 Lectures
Introduction to Classification
Logistic Regression: Binary Classification
Model Evaluation: Accuracy, Precision and Recall
Quiz
Exercise
Model Evaluation: F1 Score, Confusion Matrix
Logistic Regression: Multiclass Classification
Cost Function: Log Loss
Quiz
Exercise
Support Vector Machine (SVM)
Data Pre-processing: Scaling
Sklearn Pipeline
Quiz
Exercise
Naive Bayes: Theory
Naive Bayes: SMS Spam Classification
Quiz
Exercise
Decision Tree: Theory
Decision Tree: Salary Classification
I Need a Favour
Quiz
Exercise
Handle Class Imbalance: Theory
Handle Class Imbalance Using imblearn: Churn Prediction
Quiz
Exercise
Get inspired by Peter Pandey
Chapter Summary
20 Lectures
What is Ensemble Learning?
Majority Voting, Average and Weighted Average
Bagging
Bagging: Random Forest
Random Forest: Raisin Classification
Quiz
Exercise
Boosting: AdaBoost
Gradient Boosting: Regression Walk Through
Gradient Boosting: Regression Math
Gradient Boosting: Revenue Prediction
Quiz
Exercise
Gradient Boosting: Classification
XGBoost: Walk Through
XGBoost: California Housing Prediction
XGBoost: Synthetic Data Classification
XGBoost: Benefits
Quiz
Exercise
15 Lectures
Introduction
Model Evaluation: ROC Curve & AUC
Cost Benefit Analysis Using ROC in Sklearn
Quiz
Exercise
K Fold Cross Validation
Stratified K Fold Cross Validation
Hyperparameter Tuning: GridsearchCV
Hyperparameter Tuning: RandomizedSearchCV
Quiz
Exercise
Model Selection Guide
Luck favors the LinkedIn post
Selecting the Right Evaluation Metric
Quiz
16 Lectures
The Rise of AtliQ AI
Project Charter Meeting
Scope of Work, Task Planning in JIRA
Data Collection
Data Cleaning & EDA - Part 1
Data Cleaning & EDA - Part 2
Feature Engineering
Model Training, Fine Tunning
98% Model Accuracy, Really?
Error Analysis
Model Segmentation
Request More Data
Model Retraining
Build App Using Streamlit
Deployment
Exercise
19 Lectures
Peter's Promotion: New Project
Domain Understanding: NBFC & Credit Approvals
Scope of Work & Tech Architecture
Data Collection
Quick Intro to Data Leakage
Data Cleaning
Exploratory Data Analysis (EDA)
Feature Engineering – Part 1
Weight of Evidence (WOE), Information Value (IV)
Feature Engineering – Part 2
Model Training & Evaluation
Introduction to Optuna
Model Fine Tuning Using Optuna
Intro To Rank Ordering & KS Statistic
Model Evaluation Using KS Statistic & Gini Coefficient
Streamlit App
Business Presentation
Deployment
Exercise
22 Lectures
What is ML Ops?
Importance of ML Ops in Your Career
ML Flow: Purpose and Overview
ML Flow: Experiment Tracking
ML Flow: Model Registry
ML Flow: Centralized Server Using Dagshub
Quiz
What is API?
FastAPI Basics
Build FastAPI Server For Credit Risk Project
Quiz
Git Version Control System
Introduction to ML Cloud Platforms
AWS Sagemaker: Account Setup
AWS Sagemaker: Sagemaker Studio
AWS Sagemaker: 4 Ways to Train Model
AWS Sagemaker: Built In Algorithms
AWS Sagemaker: Script Mode
Quiz
Data Drift Detection Using PSI & CSI
PSI & CSI: Practical Implementation
Quiz
Our content is rated 5.0/5 from 1111+ Learners
I have been taking a machine learning course on Codebasics, and I absolutely love it. The course strikes the perfect balance between theory and practical application, making it incredibly engaging and educational. The facilitator is clearly experienced and brings real-life scenarios into the lessons, which enhances the learning experience.
As someone who is not a complete beginner but wants to quickly brush up on some concepts, I find the course to be incredibly valuable. It is structured in a way that is accessible for beginners while still being highly beneficial for those with some prior knowledge.
One of my favorite aspects of the course is the dramatization used to emphasize key points. For example, the facilitator's advice, "Don't watch without practicing—it's like eating food non-stop; you'll get diarrhea. Stop and reflect on what you are learning," really resonated with me. It highlights the importance of hands-on practice and reflection in the learning process.
I am so impressed with this course that I will be encouraging my team to take it as well. It offers excellent value for money compared to other courses out there. Overall, this course is a fantastic investment in your machine learning education.
Dear Dhaval Patel,
I wanted to take a moment to express my gratitude for the knowledge and support you’ve shared with me. Your explanations are incredibly clear and accessible, making even complex topics easy to understand. I particularly appreciate how you ensure that the information is presented in a way that even beginners can grasp and master.
Please continue to share your insights and updates; they are invaluable to my learning process. Once again, thank you for your guidance and dedication.
Best regards,
Saravanan S
"I have completed 40% of my machine learning course. Please upload GenAI tutorials. I love your teaching; it’s the most amazing tutorial I have ever seen."
QA Automation Engineer
Dear Dhaval Sir,
Your course has helped me immensely, and I truly appreciate your way of explanation. I can hardly believe that this entire course is available for just Rs. 1800. It’s a boon for so many people. You are doing a great service that will benefit countless individuals.
Your efforts are priceless, and we are fortunate to have you as our teacher. Thank you so much.
I have one small request: you have already shared so much of your knowledge, but I would be even more grateful if you could design a course on time series analysis.
Thank you again, Sir.
It is an excellent course that covers the fundamentals and advanced concepts of machine learning. It’s well-structured, with clear explanations and practical examples, making complex topics accessible to learners of all levels. Perfect for those looking to build a strong foundation in machine learning.
Master's in Artificial Intelligence
Data Entrepreneur (12+ Years),
YouTuber,
Ex - Bloomberg, NVIDIA
I have 17 years of experience in Programming and Data Science working for big tech companies like NVIDIA and Bloomberg. I also run a famous YouTube channel called Codebasics where I pursue my passion for teaching.
You receive a ‘Certificate of Completion’ signed and addressed personally by me, your guide and mentor. – Dhaval Patel
Add and share this certificate with your Resume/CV or on your LinkedIn profile.
You can use our private discord channel for daily chat support & networking.
We understand that you could get confused before buying a course. To support your buying decision, we have provided all the necessary information and made several videos available for free. However, Codebasics is all about caring for the learner experience - if you think this course is not for you, you can get no questions asked refund (as per the policy, answered in the next question)
Yes, adding the learnings from this course along with certificate will add weight to you resume.
Python basics, pandas and data manipulation, math and statistics basics, machine learning basics to advanced with end to end projects and ML Ops.
It is obvious that industries are being transformed through AI. Machine learning is a crucial part of AI revolution and learning this skill will help you grow exponentially in your career.
On YouTube and online courses, many times an instructor lacks either real industry experience or teaching skills. This course addresses this issue because it is taught by Dhaval Patel who has industry experience of helping clients with AI projects through his company AtliQ Technologies. He is also a YouTuber with 1 million plus subscribers known for his teaching style. You can check our YouTube channel (codebasics) and read comments in our videos to get an understanding of our teaching quality.
We believe “job gurantee” is a marketing gimmick ed-tech companies use to increase their sales. Whether a person gets a job or not depends on how well they learn and perform during interviews. It also depends on current job market. Honest, gimmic free teaching is our core value hence we stay away from “job gurantee” but still many of our students have got job by following taking our courses. Please read job success stories on this website to see names of those people.
The sessions are recorded, so it is self-paced.
Yes, there will be periodical updates to this course which you can enjoy until your course validity period.
Take this course if you have interest in doing coding and learning math.
Do not take this course if you do not want to do coding or learn math.
Yes. Everything is taught from scratch including basics of Python programming
No prerequisites are needed. You need to have curiosity and passion to learning coding as well as math
This course uses a project-based learning approach to teach you Python using two real-life projects (1) Hospitality domain data analysis and (2) Medical data extraction. Learning Python programming through projects helps you understand real-life applications of this awesome programming language. You will also have two solid projects that you can add to your resume and you work on end-to-end implementation. Total beginners, as well as people familiar with the language, will benefit from this Python course.
Learn the key concepts of Math and Statistics that lay the foundations for a strong data science career. This course is carefully curated to simulate real-time organizational experience to prepare you for the current job market and at the same time provides you with an ultimate learning experience through storytelling and intuitive explanations.
This AI Course will teach you the fundamentals and know-hows of AI like it is taught to an 8-year-old and help you to align your career with AI. This will be helpful for anyone who wants to add “AI” in their resume with confidence and without learning to code necessarily.
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