Machine Learning, Data Science and Deep Learning with Python course will teach students the methods used by actual Data Scientists and Machine Learning practitioners in the IT industry, and it will get them ready to enter this job field if they have any programming or scripting experience.
This in-depth guide on machine learning contains over 100 courses spread across 15 hours of video, and the majority of the lectures include practical Python code examples that students can use as a guide and for practice. The courses are usually available at INR 3,499 on Udemy but you can click now to get 87% off and get Machine Learning, Data Science and Deep Learning with Python Course for INR 449.
Who all can opt for this course?
- Software engineers and programmers who want to know more about data science and machine learning
- Technologists interested in the actual operation of deep learning
- Data analysts who want to examine data without utilizing tools
Course Highlights
| Key Highlights | Details |
|---|---|
| Registration Link | Apply Now! |
| Price | INR 449 ( |
| Duration | 15 hours |
| Rating | 4.5/5 |
| Student Enrollment | 1,70,030 students |
| Instructor | Sundog Education by Frank Kane https://www.linkedin.com/in/sundogeducationbyfrankkane |
| Topics Covered | Deep Learning, Neural Networks, Data Visualization in Python with MatPlotLib and Seaborn, Sentiment Analysis |
| Course Level | Intermediate |
| Total Student Reviews | 28,169 |
Learning Outcomes
- Utilize Tensorflow and Keras to create artificial neural networks
- Implement machine learning with Apache Spark’s MLLib
- Utilize deep learning to categorize data, attitudes, and images
- Make predictions with multivariate, polynomial, and linear regression
- Data visualization using Seaborn and MatPlotLib
- Understand reinforcement learning and learn how to construct a Pac-Man bot
- Data classification techniques include K-Means clustering, SVM, KNN, decision trees, Naive Bayes, and PCA
- Use train/test and K-Fold cross-validation when selecting and fine-tuning your models,
- Create a collaborative filtering system for recommending movies based on items and users
- Cleancinput data to remove outliers
- Create and assess A/B tests using T-Tests and P-Values
Course Content
| S.No. | Module (Duration) | Topics |
|---|---|---|
| 1. | Getting Started (01 hour 06 minutes) | Introduction |
| Udemy 101: Getting the Most From This Course | ||
| Important note | ||
| Installation: Getting Started | ||
| [Activity] WINDOWS: Installing and Using Anaconda & Course Materials | ||
| [Activity] MAC: Installing and Using Anaconda & Course Materials | ||
| [Activity] LINUX: Installing and Using Anaconda & Course Materials | ||
| Python Basics, Part 1 [Optional] | ||
| [Activity] Python Basics, Part 2 [Optional] | ||
| [Activity] Python Basics, Part 3 [Optional] | ||
| [Activity] Python Basics, Part 4 [Optional] | ||
| Introducing the Pandas Library [Optional] | ||
| 2. | Statistics and Probability Refresher, and Python Practice (02 hours 02 minutes) | Types of Data (Numerical, Categorical, Ordinal) |
| Mean, Median, Mode | ||
| [Activity] Using mean, median, and mode in Python | ||
| [Activity] Variation and Standard Deviation | ||
| Probability Density Function; Probability Mass Function | ||
| Common Data Distributions (Normal, Binomial, Poisson, etc) | ||
| [Activity] Percentiles and Moments | ||
| [Activity] A Crash Course in matplotlib | ||
| [Activity] Advanced Visualization with Seaborn | ||
| [Activity] Covariance and Correlation | ||
| [Exercise] Conditional Probability | ||
| Exercise Solution: Conditional Probability of Purchase by Age | ||
| Bayes’ Theorem | ||
| 3. | Predictive Models (40 minutes) | [Activity] Linear Regression |
| [Activity] Polynomial Regression | ||
| [Activity] Multiple Regression, and Predicting Car Prices | ||
| Multi-Level Models | ||
| 4. | Machine Learning with Python (01 hour 39 minutes) | Supervised vs. Unsupervised Learning, and Train/Test |
| [Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression | ||
| Bayesian Methods: Concepts | ||
| [Activity] Implementing a Spam Classifier with Naive Bayes | ||
| K-Means Clustering | ||
| [Activity] Clustering people based on income and age | ||
| Measuring Entropy | ||
| [Activity] WINDOWS: Installing Graphviz | ||
| [Activity] MAC: Installing Graphviz | ||
| [Activity] LINUX: Installing Graphviz | ||
| Decision Trees: Concepts | ||
| [Activity] Decision Trees: Predicting Hiring Decisions | ||
| Ensemble Learning | ||
| [Activity] XGBoost | ||
| Support Vector Machines (SVM) Overview | ||
| [Activity] Using SVM to cluster people using scikit-learn | ||
| 5. | Recommender Systems (49 minutes) | User-Based Collaborative Filtering |
| Item-Based Collaborative Filtering | ||
| [Activity] Finding Movie Similarities using Cosine Similarity | ||
| [Activity] Improving the Results of Movie Similarities | ||
| [Activity] Making Movie Recommendations with Item-Based Collaborative Filtering | ||
| [Exercise] Improve the recommender’s results | ||
| 6. | More Data Mining and Machine Learning Techniques (01 hour 17 minutes) | K-Nearest-Neighbors: Concepts |
| [Activity] Using KNN to predict a rating for a movie | ||
| Dimensionality Reduction; Principal Component Analysis (PCA) | ||
| [Activity] PCA Example with the Iris data set | ||
| Data Warehousing Overview: ETL and ELT | ||
| Reinforcement Learning | ||
| [Activity] Reinforcement Learning & Q-Learning with Gym | ||
| Understanding a Confusion Matrix | ||
| Measuring Classifiers (Precision, Recall, F1, ROC, AUC) | ||
| 7. | Dealing with Real-World Data (01 hour 11 minutes) | Bias/Variance Tradeoff |
| [Activity] K-Fold Cross-Validation to avoid overfitting | ||
| Data Cleaning and Normalization | ||
| [Activity] Cleaning web log data | ||
| Normalizing numerical data | ||
| [Activity] Detecting outliers | ||
| Feature Engineering and the Curse of Dimensionality | ||
| Imputation Techniques for Missing Data | ||
| Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE | ||
| Binning, Transforming, Encoding, Scaling and Shuffling | ||
| 8. | Apache Spark: Machine Learning on Big Data (01 hour 32 minutes) | Warning about Java 11 and Spark 3! |
| Spark installation notes for MacOS and Linux users | ||
| [Activity] Installing Spark – Part 1 | ||
| [Activity] Installing Spark – Part 2 | ||
| Spark Introduction | ||
| Spark and the Resilient Distributed Dataset (RDD) | ||
| Introducing MLLib | ||
| Introduction to Decision Trees in Spark | ||
| [Activity] K-Means Clustering in Spark | ||
| TF / IDF | ||
| [Activity] Searching Wikipedia with Spark | ||
| [Activity] Using the Spark DataFrame API for MLLib | ||
| 9. | Experimental Design / ML in the Real World (41 minutes) | Deploying Models to Real-Time Systems |
| A/B Testing Concepts | ||
| T-Tests and P-Values | ||
| [Activity] Hands-on With T-Tests | ||
| Determining How Long to Run an Experiment | ||
| A/B Test Gotchas | ||
| 10. | Deep Learning and Neural Networks (03 hours 01 minutes) | Deep Learning Pre-Requisites |
| The History of Artificial Neural Networks | ||
| [Activity] Deep Learning in the Tensorflow Playground | ||
| Deep Learning Details | ||
| Introducing Tensorflow | ||
| [Activity] Using Tensorflow, Part 1 | ||
| [Activity] Using Tensorflow, Part 2 | ||
| [Activity] Introducing Keras | ||
| [Activity] Using Keras to Predict Political Affiliations | ||
| Convolutional Neural Networks (CNN’s) | ||
| [Activity] Using CNN’s for handwriting recognition | ||
| Recurrent Neural Networks (RNN’s) | ||
| [Activity] Using a RNN for sentiment analysis | ||
| [Activity] Transfer Learning | ||
| Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters | ||
| Deep Learning Regularization with Dropout and Early Stopping | ||
| The Ethics of Deep Learning | ||
| 11. | Generative Models (01 hour 12 minutes) | Variational Auto-Encoders (VAE’s) – how they work |
| Variational Auto-Encoders (VAE) – Hands-on with Fashion MNIST | ||
| Generative Adversarial Networks (GAN’s) – How they work | ||
| Generative Adversarial Networks (GAN’s) – Playing with some demos | ||
| Generative Adversarial Networks (GAN’s) – Hands-on with Fashion MNIST | ||
| Learning More about Deep Learning | ||
| 12. | Final Project (16 minutes) | Your final project assignment: Mammogram Classification |
| Final project review | ||
| 13. | You made it! (04 minutes) | More to Explore |
| Don’t Forget to Leave a Rating! | ||
| Bonus Lecture |
Resources Required
- A desktop computer that can run Anaconda 3 or newer on Windows, Mac, or Linux
- Prior coding or scripting experience
- High school-level Maths
Featured Review
Srdan Markovic (5/5): Very good course! I am very happy that I took it. It covers all that is needed to launch a person into Data Science field. Besides Statistical Methods and Machine Learning and Neural Network approaches for data analysis I managed to improve my data manipulation skills using Pandas and Numpy.
Pros
- Max Luckystar (5/5): It is an awesome course on Data Science and Machine Learning.
- Thomas Weight (5/5): The instructor walked us through the setup process and for once it worked perfectly.
- Khaled Mohamad (5/5): But this course is best have seen ever, so its combine all those 3 ares in one course.
- Bernadeta Devi (5/5): This is the best online lecture i’ve ever had on machine learning.
Cons
- Steve H Hearnt (2/5): I was disappointed with the total lack of rigor, in terms of theory, math, and Python explanations.
- Samuel Tanner (2/5): You made it into this whole crazy procedure where the Terminal was involved.
- Samuel Tanner (2/5): Come back when you at least know pandas.” Also, there was a lot of misinformation about- let’s call it:- installing python.
About the Author
The instructor of this course is Frank Kane. He is the founder of Sundog Education. Machine Learning Pro. With a 4.6 instructor rating and 1,37,662 reviews on Udemy, he offers 33 courses and has taught 6,64,949 students so far.
- The goal of Sundog Education is to open up big data, data science, and machine learning as highly valuable professional fields to everyone in the globe
- The educators offer access to their knowledge in these developing disciplines at costs that are affordable to everyone
- Frank Kane is the founder and CEO of Sundog Education, and Sundog Software LLC.
- Frank worked for nine years at Amazon and IMDb, creating and overseeing the technology that constantly sends hundreds of millions of customers recommendations for products and movies.
- In the areas of distributed computing, data mining, and machine learning, Frank has 17 issued patents.
- In 2012, Frank left to create his own prosperous business, Sundog Software, which specializes in big data analysis education and virtual reality environment technologies.
Comparison Table
| Parameters | Machine Learning, Data Science and Deep Learning with Python | Data Science: Deep Learning and Neural Networks in Python | Modern Deep Learning in Python |
|---|---|---|---|
| Offers | INR 455 ( | INR 455 ( | INR 455 ( |
| Duration | 15.5 hours | 11.5 hours | 11.5 hours |
| Rating | 4.5 /5 | 4.7 /5 | 4.8 /5 |
| Student Enrollments | 170,021 | 51,314 | 32,247 |
| Instructors | Sundog Education by Frank Kane | Lazy Programmer Inc. | Lazy Programmer Inc. |
| Register Here | Apply Now! | Apply Now! | Apply Now! |
Machine Learning, Data Science and Deep Learning with Python: FAQs
Ques. Is machine learning and data science the same?
Ans. Machine learning is the study of the development of techniques for using data to enhance performance or inform predictions while data science is the study of data and how to extract meaning from it. A subset of artificial intelligence is called machine learning.
Ques. Which is better data science or machine learning?
Ans. Both are excellent alternatives for careers, depending on the learner’s preferences. For those looking to begin a career in analytics, data analytics is a superior option. For individuals who desire to develop sophisticated machine learning models and algorithms, data science is a superior employment option.
Ques. Can Python be used for data science?
Ans. Python is a high-level, open-source, interpreted language that offers a fantastic approach to object-oriented programming. Data scientists utilize it as one of the best languages for a variety of projects and applications.
Ques. What is the main difference between machine learning and deep learning?
Ans. Machine learning refers to the use of algorithms by computers to learn from data and carry out tasks automatically without explicit programming. Deep learning employs a sophisticated set of algorithms that are designed by the humans. This makes it possible to process unstructured data, including text, photos, and documents.
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