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Python Essentials 2023: Machine Learning, Data Science, and Deep Learning

Development > Data Science > Data Science

This comprehensive tutorial provides a practical, step-by-step approach to python essentials like machine learning, data science, artificial intelligence, neural networks, and Tensorflow, allowing you to gain hands-on experience in these fields.

Created by:- Sundog Education by Frank Kane, Frank Kane, and Sundog Education Team

What you will learn in python essentials

  • Build artificial neural networks using the Tensorflow and Keras frameworks.
  • Apply machine learning techniques at a large scale using Apache Spark’s MLLib.
  • Utilize deep learning to classify images, data, and sentiments.
  • Perform predictions using linear regression, polynomial regression, and multivariate regression techniques.
  • Visualize data using MatPlotLib and Seaborn.
  • Gain an understanding of reinforcement learning and build a Pac-Man bot.
  • Classify data using K-Means clustering, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Decision Trees, Naive Bayes, and Principal Component Analysis (PCA).
  • Utilize train/test and K-Fold cross-validation techniques to select and optimize models.
  • Construct a movie recommender system using item-based and user-based collaborative filtering.
  • Clean input data to remove outliers.
  • Create and assess A/B tests utilizing T-Tests and P-Values.
python essentials

Course Content:

  1. Introduction and Getting Started
  2. Statistics and Probability Refresher, and Python Practice
  3. Predictive Models
  4. Machine Learning with Python
  5. Recommender Systems
  6. Advanced Data Mining and Machine Learning Techniques
  7. Dealing with Real-World Data Challenges
  8. Apache Spark: Machine Learning on Big Data
  9. Experimental Design and Machine Learning in the Real World
  10. Deep Learning and Neural Networks
  11. Generative Models
  12. Final Project
  13. Course Conclusion and Next Steps

Requirements

  1. Desktop Computer: You will need a desktop computer (Windows, Mac, or Linux) that is capable of running Anaconda 3 or a newer version. The course will guide you through the process of installing the necessary free software.
  2. Coding or Scripting Experience: Some prior coding or scripting experience is required to fully benefit from this course. Familiarity with programming concepts and syntax will be beneficial.
  3. Math Skills: At least high school level math skills will be necessary to understand and apply the concepts covered in the course. A basic understanding of topics such as algebra, statistics, and probability will be helpful.

Description

New! Updated with extra content on generative models: variational auto-encoders (VAE’s) and generative adversarial models (GAN’s)

Introduction to Machine Learning and Artificial Intelligence (AI): Explore the World of Data Science

Machine Learning and artificial intelligence (AI) have become ubiquitous in today’s world. If you’re curious about how companies like Google, Amazon, and even Udemy derive meaningful insights from vast amounts of data, this comprehensive data science course is perfect for you. As a Data Scientist, you can enjoy one of the highest-paying professions, with an average salary of $120,000 according to Glassdoor and Indeed. But it’s not just about the money; the work itself is incredibly fascinating and engaging.

Unlock the Techniques Used by Real Data Scientists and Machine Learning Practitioners

Are you ready to embark on a career in the thriving field of data science? If you already have some programming or scripting experience, this course is tailor-made to equip you with the essential techniques employed by industry professionals. With over 100 lectures spanning 15 hours of video, this comprehensive machine learning tutorial provides hands-on examples and practical Python code for you to reference and practice. Drawing from my 9 years of experience at Amazon and IMDb, I will guide you through the crucial aspects of this field, separating the important from the trivial.

In this course, complex concepts are presented in a clear and accessible manner, avoiding convoluted mathematical notation and jargon. Each concept is illustrated with Python code, allowing you to experiment and expand upon it. You will also receive comprehensive notes for future reference. Rather than focusing on abstract mathematical coverage, this course emphasizes a practical understanding and application of the algorithms. To further solidify your knowledge, you will undertake a final project that allows you to apply what you have learned.

This comprehensive course is designed based on real requirements gathered from data scientist job listings at major tech companies. It covers the A-Z of machine learning, AI, and data mining techniques that are in high demand among employers. The points which are covered in this course:

  • Deep Learning / Neural Networks: Learn about Multi-Layer Perceptrons (MLP’s), Convolutional Neural Networks (CNN’s), and Recurrent Neural Networks (RNN’s) using TensorFlow and Keras.
  • Synthetic Image Generation: Explore the creation of synthetic images using Variational Auto-Encoders (VAE’s) and Generative Adversarial Networks (GAN’s).
  • Data Visualization: Master the art of data visualization in Python using MatPlotLib and Seaborn.
  • Transfer Learning: Understand how to leverage pre-trained models for new tasks.
  • Sentiment Analysis: Dive into the analysis of sentiment in text data.
  • Image Recognition and Classification: Learn techniques for image recognition and classification tasks.
  • Regression Analysis: Gain expertise in regression analysis for predictive modeling.
  • K-Means Clustering: Explore the K-Means clustering algorithm for unsupervised learning.
  • Principal Component Analysis: Understand how to extract key features using Principal Component Analysis (PCA).
  • Train/Test and Cross-Validation: Learn methods for evaluating and validating machine learning models.
  • Bayesian Methods: Explore Bayesian statistical methods and their application in machine learning.
  • Decision Trees and Random Forests: Understand decision tree algorithms and ensemble methods like Random Forests.
  • Multiple Regression: Master techniques for regression analysis with multiple independent variables.
  • Multi-Level Models: Gain insights into models for hierarchical or nested data structures.
  • Support Vector Machines: Learn about Support Vector Machines (SVM) for classification tasks.
  • Reinforcement Learning: Explore the foundations of reinforcement learning.
  • Collaborative Filtering: Understand collaborative filtering techniques for recommendation systems.
  • K-Nearest Neighbor: Dive into the K-Nearest Neighbor (KNN) algorithm for classification and regression.
  • Bias/Variance Tradeoff: Learn how to navigate the tradeoff between bias and variance in machine learning models.
  • Ensemble Learning: Discover techniques for combining multiple models to improve performance.
  • Term Frequency / Inverse Document Frequency: Understand the importance of TF-IDF in text mining and natural language processing.
  • Experimental Design and A/B Tests: Learn the principles of experimental design and conduct A/B tests.
  • Feature Engineering: Gain insights into feature engineering techniques for improving model performance.
  • Hyperparameter Tuning: Understand how to optimize model performance by tuning hyperparameters.

Additionally, the course includes an entire section dedicated to machine learning with Apache Spark, enabling you to scale up these techniques for analyzing “big data” on distributed computing clusters.

If you’re new to Python, there’s no need to worry. The course begins with a crash course in Python, making it accessible even for beginners. If you have previous programming experience, you’ll be able to catch on quickly. The course covers setup instructions for Microsoft Windows-based PCs, Linux desktops, and Macs, ensuring compatibility across different platforms.

Whether you’re a programmer looking to transition into an exciting new career or a data analyst aiming to enter the tech industry, this course is designed to teach you the essential techniques used by real-world data scientists. These topics are vital for any technologist seeking success in their field. Don’t hesitate any longer, enroll now and start your learning journey!

  • “I began taking your course with a casual interest, never expecting that it would lead me to a corporate job opportunity. As I delved deeper into the material, I realized how much I was learning, far beyond what academia had taught me. I am thoroughly enjoying the experience, and your course has been instrumental in helping me grasp the intricacies of solving corporate problems and thriving in AI research within a corporate setting. Your teaching style is remarkable, delivering complex concepts in a simple yet convincing manner. I find you to be the most impressive instructor in the field of machine learning.” – Kanad Basu, PhD

You can also check other courses as well like: Mastering Machine Learning and Deep Learning in Python and R


This course is designed for:

  1. Software developers or programmers seeking to switch to the rewarding career path of data science and machine learning. The course provides valuable insights and knowledge in this field.
  2. Technologists who are curious about gaining a deeper understanding of how deep learning works and its applications.
  3. Data analysts working in finance or non-tech industries who aspire to transition into the tech industry. This course equips them with the skills to analyze data using coding techniques instead of relying solely on tools. However, some prior experience in coding or scripting is necessary for success.
  4. Individuals without any coding or scripting background are advised not to take this course immediately. It is recommended to first complete an introductory Python course before diving into this material.

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Frequently Asked Questions (FAQs)

Q: Is prior coding or scripting experience required for this course?

A: Yes, some prior coding or scripting experience is required to fully benefit from this course. Familiarity with programming concepts and syntax will be beneficial.

Q: What level of math skills are necessary to understand the course material?

A: At least high school level math skills will be necessary to understand and apply the concepts covered in the course. A basic understanding of topics such as algebra, statistics, and probability will be helpful.

Q: Do I need to have experience in Python programming?

A: If you’re new to Python, there’s no need to worry. The course includes a crash course in Python at the beginning, making it accessible even for beginners. However, having some programming experience will help you pick it up quickly.

Q: Can I take this course if I have no coding or scripting experience?

A: It is not recommended to take this course if you have no prior coding or scripting experience. It is advised to first complete an introductory Python course before diving into this material.

Q: Will this course teach me practical skills used by data scientists and machine learning practitioners in the industry?

A: Yes, this course is designed to teach you the essential techniques used by real-world data scientists and machine learning practitioners. It provides practical examples and hands-on experience in machine learning, data science, and artificial intelligence.

Q: Are there any specific requirements for the computer I’ll be using?

A: You will need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or a newer version. The course will guide you through the process of installing the necessary free software.

Q: Can I apply what I learn in this course to real-world problems?

A: Yes, the course covers a wide range of topics and techniques used in industry, including deep learning, data visualization, regression analysis, clustering, and more. You will have the opportunity to work on a final project to apply what you’ve learned.

Q: Does the course cover machine learning with big data?

A: Yes, there is a dedicated section in the course that focuses on machine learning with Apache Spark, which allows you to scale up the techniques for analyzing big data on distributed computing clusters.

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