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Market Research 101: The Ultimate Guide for Beginners

Step-by-step market research for beginners: define your market, analyze competitors, and validate demand with real data.

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Generative AI for Beginners: Fundamentals, Tools & Prompts

Learn ChatGPT, Claude, Perplexity, Gemini & DeepSeek. Master Prompt Engineering with real-world Generative AI use cases.

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Django for Python Developers

Django for Python Developers - Master Django and Create Python Web Applications in Simple Steps

Java Programming Masterclass updated to Java 17

Learn Java In This Course And Become a Computer Programmer. Obtain valuable Core Java Skills And Java Certification

Created by Tim Buchalka’s Learn Programming Academy | 80 hours on-demand video course

This Java Programming Masterclass updated to Java 17 is designed to give you the Java skills you need to get a job as a Java developer. By the end of the course, you will understand Java extremely well and be able to build your own Java apps and be productive as a software developer. Lots of students have been successful in getting their first job or promotion after going through the course. Here is just one example of a student who lost her job and despite having never coded in her life previously, got a full-time software developer position in just a few months after starting this course. She didn’t even complete the course!

What you’ll learn

Learn the core Java skills needed to apply for Java developer positions in just 14 hours.
Be able to sit for and pass the Oracle Java Certificate exam if you choose.
Be able to demonstrate your understanding of Java to future employers.
Learn industry “best practices” in Java software development from a professional Java developer who has worked in the language for 18 years.
Acquire essential java basics for transitioning to the Spring Framework, Java EE, Android development and more.
Obtain proficiency in Java 8 and Java 11.

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Ensemble Machine Learning in Python: Random Forest, AdaBoost

Ensemble Machine Learning in Python: Random Forest, AdaBoost - Ensemble Methods: Boosting, Bagging, Boostrap, and Statistical Machine Learning for Data Science in Python

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In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning.

Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts.

Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning.

Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

Google famously announced that they are now "machine learning first", and companies like NVIDIA and Amazon have followed suit, and this is what's going to drive innovation in the coming years.

Machine learning is embedded into all sorts of different products, and it's used in many industries, like finance, online advertising, medicine, and robotics.

It is a widely applicable tool that will benefit you no matter what industry you're in, and it will also open up a ton of career opportunities once you get good.

Machine learning also raises some philosophical questions. Are we building a machine that can think? What does it mean to be conscious? Will computers one day take over the world?

This course is all about ensemble methods.

We've already learned some classic machine learning models like k-nearest neighbor and decision tree. We've studied their limitations and drawbacks.

But what if we could combine these models to eliminate those limitations and produce a much more powerful classifier or regressor?

In this course you'll study ways to combine models like decision trees and logistic regression to build models that can reach much higher accuracies than the base models they are made of.

In particular, we will study the Random Forest and AdaBoost algorithms in detail.

To motivate our discussion, we will learn about an important topic in statistical learning, the bias-variance trade-off. We will then study the bootstrap technique and bagging as methods for reducing both bias and variance simultaneously.

We'll do plenty of experiments and use these algorithms on real datasets so you can see first-hand how powerful they are.

Since deep learning is so popular these days, we will study some interesting commonalities between random forests, AdaBoost, and deep learning neural networks.

All the materials for this course are FREE. You can download and install Python, Numpy, and Scipy with simple commands on Windows, Linux, or Mac.

This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.

"If you can't implement it, you don't understand it"

Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".

My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch

Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?

After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...

Suggested Prerequisites:

Calculus (derivatives)

Probability

Object-oriented programming

Python coding: if/else, loops, lists, dicts, sets

Numpy coding: matrix and vector operations

Simple machine learning models like linear regression and decision trees

WHAT ORDER SHOULD I TAKE YOUR COURSES IN?:

Check out the lecture "Machine Learning and AI Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)

UNIQUE FEATURES

Every line of code explained in detail - email me any time if you disagree

No wasted time "typing" on the keyboard like other courses - let's be honest, nobody can really write code worth learning about in just 20 minutes from scratch

Not afraid of university-level math - get important details about algorithms that other courses leave out

Who this course is for:

Understand the types of models that win machine learning contests (Netflix prize, Kaggle)
Students studying machine learning
Professionals who want to apply data science and machine learning to their work
Entrepreneurs who want to apply data science and machine learning to optimize their business
Students in computer science who want to learn more about data science and machine learning
Those who know some basic machine learning models but want to know how today's most powerful models (Random Forest, AdaBoost, and other ensemble methods) are built

2023 Natural Language Processing in Python for Beginners

2023 Natural Language Processing in Python for Beginners - Text Cleaning, Spacy, NLTK, Scikit-Learn, Deep Learning, word2vec, GloVe, LSTM for Sentiment, Emotion, Spam & CV Parsing

Created by Laxmi Kant

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Welcome to KGP Talkie's Natural Language Processing (NLP) course. It is designed to give you a complete understanding of Text Processing and Mining with the use of State-of-the-Art NLP algorithms in Python.

We will learn Spacy in detail and we will also explore the uses of NLP in real life. This course covers the basics of NLP to advance topics like word2vec, GloVe, Deep Learning for NLP like CNN, ANN, and LSTM. I will also show you how you can optimize your ML code by using various tools of sklean in python. At the end part of this course, you will learn how to generate poetry by using LSTM. Multi-Label and Multi-class classification is explained. At least 12 NLP Projects are covered in this course. You will learn various ways of solving edge-cutting NLP problems.

What you'll learn

Learn complete text processing with Python
Learn how to extract text from PDF files
Use Regular Expressions for search in text
Use SpaCy and NLTK to extract complete text features from raw text
Use Latent Dirichlet Allocation for Topic Modelling
Use Scikit-Learn and Deep Learning for Text Classification
Learn Multi-Class and Multi-Label Text Classification
Use Spacy and NLTK for Sentiment Analysis
Understand and Build word2vec and GloVe based ML models
Use Gensim to obtain pretrained word vectors and compute similarities and analogies
Learn Text Summarization and Text Generation using LSTM and GRU