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 Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 25 Hours |
| Rating | 4.6/5 |
| Student Enrollment | 17,196 students |
| Instructor | Jitesh Khurkhuriya https://www.linkedin.com/in/jiteshkhurkhuriya |
| Topics Covered | Python programming, advanced mathematics for machine learning, advanced statistics for data science, data visualization & processing, deep learning |
| Course Level | Beginner |
| Total Student Reviews | 2,565 |
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 | ||
| Factoring | ||
| Quadratic Equations | ||
| Functions | ||
| 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 | ||
| Calculus | ||
| 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.
Pros
- 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
Cons
- 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
| Parameters | Data Science 2022: Complete Data Science & Machine Learning | 2023 Python for Machine Learning & Data Science Masterclass | Complete Machine Learning & Data Science Bootcamp 2023 |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 26 hours | 44 hours | 44 hours |
| Rating | 4.6 /5 | 4.6 /5 | 4.6 /5 |
| Student Enrollments | 17,194 | 79,147 | 80,313 |
| Instructors | Jitesh Khurkhuriya | Jose Portilla | Andrei Neagoie |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
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