Machine Learning Course: Data Science and Deep Learning with Python

September 13, 2022
Updated 2022/09/13 at 6:13 PM
7 Min Read
Machine Learning Course
Machine Learning Course

This machine learning course is a complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks.

Machine learning is an area of artificial intelligence and computer science that covers topics such as supervised learning and unsupervised learning.

What you’ll learn

  • Build artificial neural networks with Tensorflow and Keras
  • Implement machine learning at a massive scale with Apache Spark’s MLLib
  • Classify images, data, and sentiments using deep learning
  • Make predictions using linear regression, polynomial regression, and multivariate regression
  • Data Visualization with MatPlotLib and Seaborn
  • Understand reinforcement learning – and how to build a Pac-Man bot
  • Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
  • Use train/test and K-Fold cross-validation to choose and tune your models
  • Build a movie recommender system using item-based and user-based collaborative filtering
  • Clean your input data to remove outliers
  • Design and evaluate A/B tests using T-Tests and P-Values


  • You’ll need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or newer. The course will walk you through installing the necessary free software.
  • Some prior coding or scripting experience is required.
  • At least high school-level math skills will be required


New! Refreshed with the additional substance on generative models: variational auto-encoders (VAEs) and generative antagonistic models (GANs)

AI and man-made consciousness (computer-based intelligence) are all over; if you need to realize how organizations like Google, Amazon, and even Udemy separate significance and bits of knowledge from enormous informational collections, this machine learning course will give you the essentials you want.

Information Researchers appreciate one of the top-paying positions, with a typical compensation of $120,000 as indicated by Glassdoor and For sure. That is only normal! What’s more, it’s not just about cash – it’s fascinating work as well!

On the off chance that you make them program or prearranging experience, this machine learning course will show you the strategies involved by genuine information researchers and AI experts in the tech business – and set you up for a move into this hot vocation way.

This far-reaching AI instructional exercise incorporates more than 100 talks spread over 15 hours of video, and most themes are remembered by hand-for Python code models you can use for reference and for training.

I’ll draw on my 9 years of involvement with Amazon and IMDb to direct you through what makes a difference, and what doesn’t.

Every idea is presented in plain English, trying not to confound numerical documentation and language. It’s then shown utilizing Python code you can explore different avenues regarding and expand upon, alongside notes you can save for future reference.

You won’t track down scholar, profoundly numerical inclusion of these calculations in this machine learning course- the attention is on reasonable comprehension and utilization of them. Eventually, you’ll be given the last venture to apply what you’ve realized!

The points in this machine learning course come from an examination of genuine necessities in information researcher work postings from the greatest tech businesses.

We’ll cover the A-Z of AI, simulated intelligence, and information mining strategies genuine bosses are searching for, including:

  • Deep Learning / Neural Networks (MLPs, CNNs, RNNs) with TensorFlow and Keras
  • Creating synthetic images with Variational Auto-Encoders (VAEs) and Generative Adversarial Networks (GANs)
  • Data Visualization in Python with MatPlotLib and Seaborn
  • Transfer Learning
  • Sentiment analysis
  • Image recognition and classification
  • Regression analysis
  • K-Means Clustering
  • Principal Component Analysis
  • Train/Test and cross-validation
  • Bayesian Methods
  • Decision Trees and Random Forests
  • Multiple Regression
  • Multi-Level Models
  • Support Vector Machines
  • Reinforcement Learning
  • Collaborative Filtering
  • K-Nearest Neighbor
  • Bias/Variance Tradeoff
  • Ensemble Learning
  • Term Frequency / Inverse Document Frequency
  • Experimental Design and A/B Tests
  • Feature Engineering
  • Hyperparameter Tuning

Also, considerably more! There’s likewise a whole segment on AI with Apache Flash, which allows you to increase these methods to “enormous information” investigated on a registering bunch.

On the off chance that you’re new to Python, you can definitely relax – the course begins with a compressed lesson. Assuming you’ve done some programming previously, you ought to get it rapidly. This course tells you the best way to get set up on Microsoft Windows-based computers, Linux work areas, and Macintoshes.

On the off chance that you’re a software engineer hoping to switch to an intriguing new vocation track, or an information investigator hoping to make the progress in the tech business – this course will show you the essential methods utilized by genuine industry information researchers.

These are subjects any fruitful technologist totally has to be aware of, so the thing would you say you are hanging tight for? Enlist now!

  • “I began doing your course In the long run, I got intrigued and never felt that I will be working for corporate before a companion extended me this employment opportunity. I’m gaining some significant experience that was difficult to learn in the scholarly community and appreciate it completely. As far as I might be concerned, your course is the one that assisted me with understanding how to function with corporate issues.
  • The most effective method to remember to progress in corporate computer-based intelligence research. I find you the most noteworthy teacher in ML, straightforward yet persuading.” – Kanad Basu, PhD

Who this course is for:

  • Programming engineers or developers who need to change into the rewarding information science and AI vocation will gain some useful knowledge from this course.
  • Technologists are inquisitive about how profound advancement truly functions
  • Information examiners in finance or other non-tech ventures who need to progress into the tech business can utilize this course to figure out how to break down information utilizing code rather than devices. In any case, you’ll require some related knowledge in coding or prearranging to find success.
  • In the event that you have no earlier coding or prearranging experience, you shouldn’t accept this course – yet. Go take a basic Python course first.

Find Us on Socials

Share this Article
Leave a comment