data science

The Data Science 2022: Complete Data Science & Machine Learning Course offers a comprehensive curriculum for individuals interested in learning data science and machine learning. The course includes 11 projects, over 250 lectures, and more than 25 hours of material covering everything from the advanced mathematics behind machine learning to advanced statistics for data science, data processing, and deep learning.

The course covers real-world projects such as Kaggle Bike Demand Prediction, process automation for loan approval, IRIS Classification, adult income predictions from the US Census dataset, telemarketing predictions for banks, and more. This course is ideal for those looking to enter industries such as automotive, banking, healthcare, media, telecom, and more. The courses are usually available at INR 3,499 on Udemy but you can click now to get 87% off and get The Data Science 2022: Complete Data Science & Machine Learning Course for INR 499.

Who all can opt for this course?

  • Programmers of all levels who wish to work in the fields of data science and machine learning

Course Highlights

Key HighlightsDetails
Registration LinkApply Now!
PriceINR 449 (INR 3,49987 % off
Duration25 Hours
Student Enrollment17,196 students
InstructorJitesh Khurkhuriya
Topics CoveredPython programming, advanced mathematics for machine learning, advanced statistics for data science, data visualization & processing, deep learning
Course LevelBeginner
Total Student Reviews2,565

Learning Outcomes

  • Learn the entire range of advanced concepts and skills necessary to become a data scientist
  • Learn Python programming from the fundamentals up to the point where it is needed for data science and machine learning
  • Learn all of the mathematics necessary for data science and machine learning, including linear algebra, calculus, vectors, and matrices
  • Learn all about descriptive and inferential statistics
  • Learn how to analyze data using all the necessary charts and plots for data visualization
  • Utilize ScikitLearn and Pandas to process data with all of its assumptions and parameters, the master regression
  • Discover how to attain the top 1 percentile by completing a Kaggle assignment
  • Learn several categorization methods, such as Support Vector Machines, Decision Trees, Random Forests, and Logistic Regression
  • Learn all there is to know about deep learning using Keras and TensorFlow
  • Learn Dimensionality Reduction and feature selection to become a Pro

Course Content

S.No.Module (Duration)Topics
1.Introduction (12 minutes)Course Introduction
How to Claim your FREE Gift
Download Course Material
Udemy Reviews – Important Message
2.— Part 1: Essential Python Programming — (01 hour 43 minutes)Install Anaconda, Spyder
Keyboard Shortcut – Must view for beginners
Hands On – Hello Python and Know the environment
Hands On – Variable Types and Operators
Hands On – Decision Making – If-Else
Python Loops explained
Hands On – While Loops
Hands On – For Loops
Python Lists Explained
Hands On – Lists Basic Operations
Hands On – Lists Operations Part 2
Multidimensional Lists Explained
Hands On – Slicing Multidimensional lists
Hands On – Python Tuples
Python Dictionary Explained
Hands On – Access the Dictionary Data
Hands On – Dictionary Methods and functions
File processing – Open and Read files
File Processing – Process Data and Write to Files
File Processing – Process Data using Loops
Project 1 – Calculate the average temperature per city
Solution – Project 1 calculate the average temperature per city
Essential Python Programming
3.— Part 2: Essential Mathematics — (02 hours 30 minutes)What you will learn in this Part?
Algebraic Equations
Exponents and Logs
Polynomial Equations
Quadratic Equations
Algebra Foundations
Calculus Foundation
Rate of Change and Limits
Differentiation and Derivatives
Derivative Rules and Operations
Double Derivatives and finding Maxima
Double Derivatives example
Partial Derivatives and Gradient Descent
Integration and Area Under the Curve
Vector Basics – What is a Vector and vector operations
Vector Arithmetic
Matrix Foundation
Matrix Arithmetic
Identity, Inverse, Determinant and Transpose Matrix
Matrix Transformation
Change of Basis and Axis using Matrix Transformation
Eigenvalues and Eigenvectors
Linear Algebra
Understanding probability in simple terms
Probability Terms
Conditional Probability
Random Processes and Random Variables
Probability Foundation
4.What is Data Science and Machine Learning? (40 minutes)Need for Data Science and Machine Learning
Types of Analytics
Decoding Data Science and Machine Learning
Data Science Project Lifecycle Part 1
Data Science Project Lifecycle Part 2
Data Science Project Lifecycle Part 3
Data Science Project Lifecycle Part 4
What does a Data Scientist do and the skills required?
Data Science Basics
5.— Part 3: Essential Statistics — (24 seconds)What you will learn in this part?
6.Descriptive Statistics (28 minutes)What is Data? Understanding the Data and its elements.
Measure of Central Tendency using Mean, Median, mode
Measure of Dispersion using Standard Deviation and variance
Hands on – Get Statistical Summary
Measure of Dispersion using Percentile, Range and IQR
7.Data Visualization (01 hour 30 minutes)Importance of Data Visualization
Data Visualization – Frequency Table, Histogram and Bar Chart
Understanding Boxplot for Numerical Data
What is a Plot?
Hands On – Create Line Plots
Hands On – Understand Plot Figure Menu
Hands On – Create your first Bar Chart
Hands On – Create Histogram of Data
Hands On – Plotting Boxplot
Data Visualization for Categorical Data
Hands On – Pie Charts Part 1
Hands On – Pie Charts Part 2
Hands On – Scatter Plots
Hands On – MatplotLib Figures for creating multiple plots
Hands On – Subplots for plotting multiple plots in one figure
Hands On – Customization of Plot elements Part 1
Hands On – Customization of Plot elements Part 2
Hands On – Customization of Plot elements Part 3
Hands On – Customization of Plot elements Part 4
Claim your reward now.
8.Inferential Statistics, Distributions and Hypothesis (02 hours 11 minutes)Understand Population Vs Samples
What is a Sample Bias?
What is Correlation and Causality?
What is Covariance and Covariance Matrix?
Probability Density Function and Distributions
Normal Distributions
Standard Normal Distributions
Sampling Distributions
Central Limit Theorem
Confidence Interval – Part 1
Confidence Interval – Part 2
What is Hypothesis and Null Vs Alternate Hypothesis?
What is Statistical Significance
Hypothesis Testing Examples
9.— Part 4: Data Pre-Processing — (01 hour 12 minutes)Hands On – Import Library to Read and Slice the data
Hands On – Understand the data you are dealing with
Hands On – Handling Missing Values
Label-Encoding for Categorical Data
Hands On Label Encoding
Hot-Encoding for Categorical Data Explained
Hands On – Hot-Encoding for Categorical Data
Data normalization – Understand the reasons.
Hands On – Data Normalization using Standard Scaler
Hands On – Data Normalization using minmax
Train and Test Data Split explained
Hands On – Train and Test Data Split
10.— Part 5: Regression ——– (08 seconds)What you will learn in this section?
11.Simple Linear Regression (46 minutes)What is Simple Linear Regression
Ordinary Least Square and Regression Errors
Project 2 – Data Processing
Project 2 – Train and Test Model
Test the model and Predict Y Values
Project 2 – R-Squared and its Importance
Project 2 – Score and Get coefficients
Project 2 – Calculate RMSE (Root Mean Squared Error)
Project 2 – Plot the predictions
12.Multiple Linear Regression (01 hour 10 minutes)Understanding the Multiple Linear Regression
Project 3 – Multiple Linear Regression Predictions
Issues to deal with for Multiple Linear Regression
Degrees of Freedom
Adjusted R-Squared
Assumptions of Multiple Linear Regression
Linearity and Multicollinearity Assumption
Assumption of Autocorrelation
Hands on – Plot Autocorrelation
Hands on – Create shifted or TimeLag Data
Endogeneity Assumption
Normality of Residuals
Assumption of Homoscadasticity
Dummy Variable trap
13.Project 4 – Kaggle Bike Demand Predictions (01 hour 36 minutes)Let’s understand the problem
Steps required to solve the problem
Read and Prepare Data
Basic Analysis of Data
Data Visualization of the Continuous Variables
Data Visualization of the Categorical Variables
Summarize Data Visualization Findings
Check for Outliers
Test the Multicollinearity Assumption
Test Auto-correlation in Demand
Solving the problem of Normality
Solving the problem of Autocorrelation
Create Dummy Variables
Train-Test Split for the Time-Series Data
Create the Model and measure RMSE
Calculate and measure RMSLE for Kaggle
14.— Part 6: Classification ——— (20 seconds)What you will learn in this section?
15.Logistic Regression (37 minutes)What is Logistic Regression?
Project 5 – Predict Loan Approval Problem Understanding
Project 5 – Predict Loan Approval Part 1
Project 5 – Predict Loan Approval Part 2
Project 5 – Predict Loan Approval Part 3
Project 5 – Predict Loan Approval – Build Logistic Regressor
Project 5 – Predict Loan Approval – Confusion Martix
Create and Analyse Confusion Matrix
16.Support Vector Machines (SVM) (55 minutes)Common Sensical Intuition of SVM
Mathematical Intuition of SVM Part 1
Mathematical Intuition of SVM Part 2
Hands on – Simple Implementation of SVM
SVM Kernel Functions Part 1
SVM Kernel Functions Part 2
SVM Kernel Function Types
Project 6 – IRIS Classification Problem
Project 6 – Data Processing
Project 6 – Train and create Model
Project 6 – Multiple Model Creation and comparison
17.Decision Trees (39 minutes)Intuition Behind Decision Trees
Project 7 – Adult Income Prediction Problem Understanding
Project 7 – Data Processing
Project 7 – Split data and Import Classifier
Project 7 – Decision Trees – Parameters Part 1
Project 7 – Decision Trees – Parameters Part 2
Project 7 – Run and Evaluate Model
18.Random Forest (13 minutes)Ensemble Learning and Random Forests
Bagging and Boosting
Hands on – Implement Random Forest
19.Evaluate Classification Models (49 minutes)Need for Evaluation and Accuracy Paradox
Classification Evaluation Measures
Hands on – Evaluation Metrics for Loan Prediction projects
What is Threshold and Adjusting Thresholds
Hands on – Adjusting Thresholds
Hands On – AUC ROC Curve using Python
Drawing the AUC ROC Curve
20.— Part 7: Feature Selection —— (18 seconds)What You will learn in this Part?
21.Univariate Feature Selection (01 hour 12 minutes)Feature Selection Importance
What is Univariate Feature Selection?
F-Test for Regression and Classification
Hands on F-test – Problem Statement
Hands On F-test – Regression without feature selection
Hands on F-test – Print and analyse Pvalues
Hands on F-test – Compare Results with and without Feature Selection
Chi-Squared Intuition
Scikitlearn – What are Feature Selection Transforms
Hands on – SelectKBest Part 1
Hands on – SelectKBest Part 2
Hands on – SelectPercentile
Hands on – Generic Univariate Select
22.Recursive Feature Elimination (30 minutes)What is Recursive Feature Elimination (RFE)?
Project 8 – Bank Telemarketing Predictions Problem Understanding
Project 8 – Build Prediction model without RFE
Project 8 – Configure RFE and Compare results
Project 8 – Get Feature Importance Score
23.— Part 8: Dimensionality Reduction — (34 minutes)Why to reduce dimensions and Importance of PCA?
Mathematical Intuition of PCA and Steps to calculate PCA
Project 9 – Model Implementation without PCA
Project 9 – Convert the Dimensions to PCA
Project 9 – Compare results after PCA Implementation
24.—- Part 9 – Regularization —- (01 hour 07 minutes)Regularization Introduction.
What is Bias Variance Trade-off?
Ridge Regression or L2 Penalty
Hands on – Implement Ridge Regression
Hands on – Plot Ridge Regression Line
Hands On – Effect of Lambda/Alpha
Note about attached code
Lasso Regression or L1 Penalty – Hands on
Part 1 – L1 and L2 for Multicollinearity and Feature Selection
Part 2 – L1 and L2 for Multicollinearity and Feature Selection
Part 3 – L1 and L2 for Multicollinearity and Feature Selection
Elasticnet Regularization
25.—- Part 10 – Model Selection —– (39 seconds)Model Selection Introduction
26.Cross Validation for Model Selection (26 minutes)What is Cross Validation?
How Cross Validation Works
Hands On – Prepare for Cross Validation
Hands On – Parameter and implementation of Cross Validation
Hands On – Understand the results of Cross Validation
Hands On – Analyse the Result
27.Hyperparameter Tuning for Model Selection (58 minutes)What is Hyperparameter Tuning?
Grid Search and Randomized Search Approach
Part 1 – GridSearchCV Parameters Explained
Part 2 – Create GirdSearchCV Object
Part 3 – Fit data to GridSearchCV
Part 4 – Understand GridSearchCV Results
Part 5 – GridSearchCV using Logistic Regression
Part 6 – GridSearchCV using Support Vector
Part 7 – Select Best Model
Part 8 – Randomized Search
Model Selection Summary
28.— Part 11: Deep Learning —- (02 hours 33 minutes)What is Neuron and Artificial Neural Network?
How Artificial Neural Network works?
What is Keras and Tensorflow?
What is a Tensor in Tensorflow?
Installing Keras, backend and Tensorflow
Keras Model Building and Steps
Layers – Overview and Parameters
Activation Functions
Layers – Softmax Activation Function
What is a Loss Function?
Cross Entropy Loss Functions
Optimization – What is it?
Optimization – Gradient Descent
Optimization – Stochastic Gradient Descent
Optimization – SGD with Momentum
Optimization – SGD with Exponential Moving Average
Optimization – Adagrad and RMSProp for Learning rate decay
Optimization – Adam
Initializers – Vanishing and Exploding Gradient Problem
Layers – Initializers explained
Project 10 – Understand the Problem
Project 10 – Read and process the data
Project 10 – Define the Keras Neural Network Model
Project 10 – Compile the Keras Neural Network Model
Project 10 – Evaluate the result
29.—- Part 12 – Clustering or Cluster Analysis —- (01 hour 04 minutes)What is Clustering?
How the clusters are formed?
Project 11 – Problem Understanding
Project 11 – Get, Visualize and Normalize the data
Project 11 – Import KMeans and Understand Parameters
Project 11 – Understanding KMeans++ Initialization Method
Project 11 – Create Clusters
Project 11 -Visualize and create different number of clusters
Understand Elbow Method to Decide number of Cluster
Project 11 – Implement Elbow Method
How to use clustering for business?
30.Way Forward. (30 seconds)Bonus Lecture and Get Certified.

Resources Required

No prerequisites. The instructor will teach right from the basics of Python to advanced Deep Learning.

Featured Review

James C (5/5): This is without a doubt the best Machine learning course I could find on Udemy. Being a Software Engineer and trying to get into this new field of Machine Learning has been very daunting because of the speculation around it, being very complex to learn, needing to have some advanced degree in Maths, etc But this course delivers the right balance of both, the Maths and ML model details. Very well presented making it really easy to understand and dispelling all the speculations around ML. After taking this course, I have already started applying ML to practical real world scenarios with success. Thank you for making this amazing course. The great starting point for beginners and now I find myself very easily able to pick up/read more advanced concepts about ML and continue my journey to become a data scientist.


  • Neven Dujmovi (5/5): I found an especially impressive section about math, including linear algebra & calculus, and how it is applied in machine learning algorithms and data science.
  • Thabo Thobejane (5/5): This is probably the best intro to data science course on Udemy.
  • Aditya Das (5/5): It is the best course on Data science one can find, for beginners or for people with some experience all have a lot to learn.
  • Cesar Rojas Carrasco (5/5): Excellent course, covers all the needed topics for a Data Scientist and perfectly explained


  • AddixData (3.5/5): Sometimes explanations are very detailled while some others (as cross validation) are only seen associated to other notions
  • Raj Mohan R. (3/5): The content of this course is gold but the reason I gave it 3 stars is due to the fake accent of the instructor. I just wish that he had stuck to this natural accent. As in any course, I would replay the lessons several times and listening to the fake accent gave me a headache. I really feel bad about leaving 3 stars but hope that this would motivate him to be his true self.
  • Savio D. (2.5/5): The speech flow of the instructor can be better. I find it difficult to concentrate due to the “flow of speech”. This manner of speaking may be alright for some promotional item – but it is not very helpful while providing training.
  • Nishar Ahamed K A. (1/5): The way of teaching is very much appreciated. However, the use cases for each of the video would have been good. The current session sounds more like a typical coding section.

About the Author

Jitesh Khurkhuriya is the instructor of the course. He is a Data Scientist and Digital Transformation Consultant. With a 4.6 instructor rating and 9,172 reviews on Udemy, he offers 5 courses and has taught 54,592 students so far.

  • Jitesh has over 20 years of expertise in technology and has held positions as a data scientist, product head, and programmer.
  • Jitesh has collaborated with numerous Fortune 500 firms and international authorities.
  • He was a key member of the high-profile team that made recommendations for tax reforms and revisions in the areas of VAT, Customs, and Income Tax based on research into fraud patterns, national data mining and analysis, and business process security analysis.
  • This not only aided in a radical transformation of the tax system but also decreased tax and customs fraud.
  • Jitesh has designed and implemented strategies that produced strong top and bottom-line income streams as a seasoned leader in digital transformation.

Comparison Table

ParametersData Science 2022: Complete Data Science & Machine Learning2023 Python for Machine Learning & Data Science MasterclassComplete Machine Learning & Data Science Bootcamp 2023
OffersINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% offINR 455 (INR 3,499) 87% off
Duration26 hours44 hours44 hours
Rating4.6 /54.6 /54.6 /5
Student Enrollments17,19479,14780,313
InstructorsJitesh KhurkhuriyaJose PortillaAndrei Neagoie
Register HereApply Now!Apply Now!Apply Now!

Leave feedback about this

  • Rating