Machine Learning, Data Science and Generative AI with Python (1 2025)

[0dayddl]

7.57 GB | 21min 15s | mp4 | 1280X720 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

1 Introduction.mp4 (18.75 MB)
10 Activity Python Basics, Part 3 Optional.mp4 (2.44 MB)
11 Activity Python Basics, Part 4 Optional.mp4 (5.72 MB)
12 Introducing the Pandas Library Optional.mp4 (44.15 MB)
2 Udemy 101 Getting the Most From This Course.mp4 (17.4 MB)
5 Activity WINDOWS Installing and Using Anaconda & Course Materials.mp4 (101.97 MB)
6 Activity MAC Installing and Using Anaconda & Course Materials.mp4 (95.78 MB)
7 Activity LINUX Installing and Using Anaconda & Course Materials.mp4 (60.14 MB)
8 Python Basics, Part 1 Optional.mp4 (26.9 MB)
9 Activity Python Basics, Part 2 Optional.mp4 (20.62 MB)
1 Types of Data (Numerical, Categorical, Ordinal).mp4 (73.1 MB)
10 Activity Covariance and Correlation.mp4 (69.48 MB)
11 Exercise Conditional Probability.mp4 (93.95 MB)
12 Exercise Solution Conditional Probability of Purchase by Age.mp4 (15.01 MB)
13 Bayes' Theorem.mp4 (56.12 MB)
2 Mean, Median, Mode.mp4 (15.96 MB)
3 Activity Using mean, median, and mode in Python.mp4 (44.5 MB)
4 Activity Variation and Standard Deviation.mp4 (103.39 MB)
5 Probability Density Function Probability Mass Function.mp4 (6.92 MB)
6 Common Data Distributions (Normal, Binomial, Poisson, etc).mp4 (28.25 MB)
7 Activity Percentiles and Moments.mp4 (42.56 MB)
8 Activity A Crash Course in matplotlib.mp4 (88.69 MB)
9 Activity Advanced Visualization with Seaborn.mp4 (96.14 MB)
1 Activity Linear Regression.mp4 (92.96 MB)
2 Activity Polynomial Regression.mp4 (60.55 MB)
3 Activity Multiple Regression, and Predicting Car Prices.mp4 (94.14 MB)
4 Multi-Level Models.mp4 (27.22 MB)
1 Supervised vs Unsupervised Learning, and TrainTest.mp4 (56.68 MB)
10 Activity LINUX Installing Graphviz.mp4 (2.48 MB)
11 Decision Trees Concepts.mp4 (81.5 MB)
12 Activity Decision Trees Predicting Hiring Decisions.mp4 (57.79 MB)
13 Ensemble Learning.mp4 (36.96 MB)
14 Activity XGBoost.mp4 (79.28 MB)
15 Support Vector Machines (SVM) Overview.mp4 (16.35 MB)
16 Activity Using SVM to cluster people using scikit-learn.mp4 (38.49 MB)
2 Activity Using TrainTest to Prevent Overfitting a Polynomial Regression.mp4 (21.62 MB)
3 Bayesian Methods Concepts.mp4 (9.83 MB)
4 Activity Implementing a Spam Classifier with Naive Bayes.mp4 (81.39 MB)
5 K-Means Clustering.mp4 (26.02 MB)
6 Activity Clustering people based on income and age.mp4 (21.99 MB)
7 Measuring Entropy.mp4 (12.14 MB)
8 Activity WINDOWS Installing Graphviz.mp4 (949.33 KB)
9 Activity MAC Installing Graphviz.mp4 (9.07 MB)
1 User-Based Collaborative Filtering.mp4 (81.69 MB)
2 Item-Based Collaborative Filtering.mp4 (23.2 MB)
3 Activity Finding Movie Similarities using Cosine Similarity.mp4 (82.67 MB)
4 Activity Improving the Results of Movie Similarities.mp4 (56.06 MB)
5 Activity Making Movie Recommendations with Item-Based Collaborative Filtering.mp4 (124.11 MB)
6 Exercise Improve the recommender's results.mp4 (28 MB)
1 K-Nearest-Neighbors Concepts.mp4 (14.04 MB)
2 Activity Using KNN to predict a rating for a movie.mp4 (85.54 MB)
3 Dimensionality Reduction Principal Component Analysis (PCA).mp4 (38.13 MB)
4 Activity PCA Example with the Iris data set.mp4 (65.77 MB)
5 Data Warehousing Overview ETL and ELT.mp4 (58.71 MB)
6 Reinforcement Learning.mp4 (125.18 MB)
7 Activity Reinforcement Learning & Q-Learning with Gym.mp4 (62.79 MB)
8 Understanding a Confusion Matrix.mp4 (7.38 MB)
9 Measuring Classifiers (Precision, Recall, F1, ROC, AUC).mp4 (11.67 MB)
1 BiasVariance Tradeoff.mp4 (23.63 MB)
10 Binning, Transforming, Encoding, Scaling, and Shuffling.mp4 (42.72 MB)
2 Activity K-Fold Cross-Validation to avoid overfitting.mp4 (56.9 MB)
3 Data Cleaning and Normalization.mp4 (73.09 MB)
4 Activity Cleaning web log data.mp4 (31.01 MB)
5 Normalizing numerical data.mp4 (10.32 MB)
6 Activity Detecting outliers.mp4 (27.15 MB)
7 Feature Engineering and the Curse of Dimensionality.mp4 (14.56 MB)
8 Imputation Techniques for Missing Data.mp4 (18.2 MB)
9 Handling Unbalanced Data Oversampling, Undersampling, and SMOTE.mp4 (17.43 MB)
10 Activity Searching Wikipedia with Spark.mp4 (84.01 MB)
11 Activity Using the Spark DataFrame API for MLLib.mp4 (65.11 MB)
3 Activity Installing Spark.mp4 (141.36 MB)
4 Spark Introduction.mp4 (24.96 MB)
5 Spark and the Resilient Distributed Dataset (RDD).mp4 (22.3 MB)
6 Introducing MLLib.mp4 (14.65 MB)
7 Introduction to Decision Trees in Spark.mp4 (133.95 MB)
8 Activity K-Means Clustering in Spark.mp4 (116.14 MB)
9 TF IDF.mp4 (65.66 MB)
1 Deploying Models to Real-Time Systems.mp4 (17.22 MB)
2 AB Testing Concepts.mp4 (32.02 MB)
3 T-Tests and P-Values.mp4 (14.08 MB)
4 Activity Hands-on With T-Tests.mp4 (47.77 MB)
5 Determining How Long to Run an Experiment.mp4 (9.75 MB)
6 AB Test Gotchas.mp4 (91.73 MB)
1 Deep Learning Pre-Requisites.mp4 (70.4 MB)
10 Convolutional Neural Networks (CNN's).mp4 (58.73 MB)
11 Activity Using CNN's for handwriting recognition.mp4 (52.82 MB)
12 Recurrent Neural Networks (RNN's).mp4 (32.81 MB)
13 Activity Using a RNN for sentiment analysis.mp4 (73.55 MB)
14 Activity Transfer Learning.mp4 (111.05 MB)
15 Tuning Neural Networks Learning Rate and Batch Size Hyperparameters.mp4 (8.5 MB)
16 Deep Learning Regularization with Dropout and Early Stopping.mp4 (19.84 MB)
17 The Ethics of Deep Learning.mp4 (120.5 MB)
2 The History of Artificial Neural Networks.mp4 (68.87 MB)
3 Activity Deep Learning in the Tensorflow Playground.mp4 (55.69 MB)
4 Deep Learning Details.mp4 (30.9 MB)
5 Introducing Tensorflow.mp4 (46.63 MB)
6 Activity Using Tensorflow, Part 1.mp4 (107.7 MB)
7 Activity Using Tensorflow, Part 2.mp4 (95.13 MB)
8 Activity Introducing Keras.mp4 (72.03 MB)
9 Activity Using Keras to Predict Political Affiliations.mp4 (66.82 MB)
1 Variational Auto-Encoders (VAE's) - how they work.mp4 (42.88 MB)
2 Variational Auto-Encoders (VAE) - Hands-on with Fashion MNIST.mp4 (148.84 MB)
3 Generative Adversarial Networks (GAN's) - How they work.mp4 (15.24 MB)
4 Generative Adversarial Networks (GAN's) - Playing with some demos.mp4 (88.6 MB)
5 Generative Adversarial Networks (GAN's) - Hands-on with Fashion MNIST.mp4 (126.11 MB)
6 Learning More about Deep Learning.mp4 (20.21 MB)
1 The Transformer Architecture (encoders, decoders, and self-attention ).mp4 (19.78 MB)
11 Activity Fine Tuning GPT with the IMDb dataset.mp4 (85.2 MB)
3 Applications of Transformers (GPT).mp4 (9.58 MB)
4 How GPT Works, Part 1 The GPT Transformer Architecture.mp4 (30.27 MB)
5 How GPT Works, Part 2 Tokenization, Positional Encoding, Embedding.mp4 (14.76 MB)
6 Fine Tuning Transfer Learning with Transformers.mp4 (5.05 MB)
7 Activity Tokenization with Google CoLab and HuggingFace.mp4 (67.74 MB)
8 Activity Positional Encoding.mp4 (6.46 MB)
1 Activity The OpenAI Chat Completions API.mp4 (65.41 MB)
2 Activity Using Tools and Functions in the OpenAI Chat Completion API.mp4 (81.12 MB)
3 Activity The Images (DALL-E) API in OpenAI.mp4 (25.79 MB)
4 Activity The Embeddings API in OpenAI Finding similarities between words.mp4 (28.96 MB)
5 The Legacy Fine-Tuning API for GPT Models in OpenAI.mp4 (11.68 MB)
6 Demo Fine-Tuning OpenAI's Davinci Model to simulate Data from Star Trek.mp4 (166.5 MB)
7 The New OpenAI Fine-Tuning API Fine-Tuning GPT-3 5 to simulate Commander Data!.mp4 (318.98 MB)
8 Activity The OpenAI Moderation API.mp4 (16.21 MB)
9 Activity The OpenAI Audio API (speech to text).mp4 (12.98 MB)
1 Retrieval Augmented Generation (RAG) How it works, with some examples.mp4 (92.89 MB)
10 Activity Building a Cdr Data chatbot with LLM Agents, web search & math tools.mp4 (263.83 MB)
2 Demo Using Retrieval Augmented Generation (RAG) to simulate Data from Star Trek.mp4 (72.49 MB)
3 RAG Metrics The RAG Triad, relevancy, recall, precision, accuracy, and more.mp4 (25.05 MB)
4 Activity Evaluating our RAG-based Cdr Data using RAGAS and langchain.mp4 (270.77 MB)
5 Advanced RAG Pre-Retrieval chunking semantic chunking data extraction.mp4 (29.46 MB)
6 Advanced RAG Query Rewriting.mp4 (8.07 MB)
7 Advanced RAG Prompt Compression, and More Tuning Opportunities.mp4 (21.48 MB)
8 Activity Simulating Cdr Data with Advanced RAG and langchain.mp4 (264.42 MB)
9 LLM Agents and Swarms of Agents.mp4 (24.66 MB)
1 Your final project assignment Mammogram Classification.mp4 (51.6 MB)
2 Final project review.mp4 (64.49 MB)
1 More to Explore.mp4 (33.99 MB)

]
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[0dayddl]

Machine Learning, Data Science and Generative AI with Python
Last updated 4/2024
Duration: 18h50m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 7.21 GB
Genre: eLearning | Language: English​

Complete hands-on machine learning and GenAI tutorial with data science, Tensorflow, GPT, OpenAI, and neural networks

What you'll learn
Build artificial neural networks with Tensorflow and Keras
Implement machine learning at 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

Requirements
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.

Description
Unlock the Power of Machine Learning & AI: Master the Art of Turning Data into Insight
Discover the Future of Technology with Our Comprehensive Machine Learning & AI Course - Featuring Generative AI, Deep Learning, and Beyond!
In an era where Machine Learning (ML) and Artificial Intelligence (AI) are revolutionizing industries across the globe, understanding how giants like Google, Amazon, and Udemy leverage these technologies to extract meaningful insights from vast data sets is more critical than ever. Whether you're aiming to join the ranks of top-tier AI specialists-with an average salary of $159,000 as reported by Glassdoor-or you're driven by the fascinating challenges this field offers, our course is your gateway to an exciting new career trajectory.
Designed for individuals with programming or scripting backgrounds, this course goes beyond the basics, preparing you to stand out in the competitive tech industry. Our curriculum, enriched with over 130 lectures and 18+ hours of video content, is crafted to provide hands-on experience with Python, guiding you from the fundamentals of statistics to the cutting-edge advancements in generative AI.
Why Choose This Course?
Updated Content on Generative AI:
Dive into the latest in AI with modules on transformers, GPT, ChatGPT, the OpenAI API, Retrieval Augmented Generation (RAG), and self-attention based neural networks.
Real-World Application:
Learn through Python code examples based on real-life scenarios, making the abstract concepts of ML and AI tangible and actionable.
Industry-Relevant Skills:
Our curriculum is designed based on the analysis of job listings from top tech firms, ensuring you gain the skills most sought after by employers.
Diverse Topics Covered:
From neural networks, TensorFlow, and Keras to sentiment analysis and image recognition, our course covers a wide range of ML models and techniques, ensuring a well-rounded education.
Accessible Learning:
Complex concepts are explained in plain English, focusing on practical application rather than academic jargon, making the learning process straightforward and engaging.
Course Highlights:
Introduction to Python and basic statistics, setting a strong foundation for your journey in ML and AI.
Deep Learning techniques, including MLPs, CNNs, and RNNs, with practical exercises in TensorFlow and Keras.
Extensive modules on the mechanics of modern generative AI, including transformers and the OpenAI API, with hands-on projects like fine-tuning GPT.
A comprehensive overview of machine learning models beyond GenAI, including SVMs, reinforcement learning, decision trees, and more, ensuring you have a broad understanding of the field.
Practical data science applications, such as data visualization, regression analysis, clustering, and feature engineering, empowering you to tackle real-world data challenges.
A special section on Apache Spark, enabling you to apply these techniques to big data, analyzed on computing clusters.
No previous Python experience? No problem! We kickstart your journey with a Python crash course to ensure you're well-equipped to tackle the modules that follow.
Transform Your Career Today
Join a community of learners who have successfully transitioned into the tech industry, leveraging the knowledge and skills acquired from our course to excel in corporate and research roles in AI and ML.
"I started doing your course. and it was pivotal in helping me transition into a role where I now solve corporate problems using AI. Your course demystified how to succeed in corporate AI research, making you the most impressive instructor in ML I've encountered." - Kanad Basu, PhD
Are you ready to step into the future of technology and make a mark in the fields of machine learning and artificial intelligence?
Enroll now and embark on a journey that transforms data into powerful insights, paving your way to a rewarding career in AI and ML.
Who this course is for:
Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course.
Technologists curious about how deep learning really works
Data analysts in the finance or other non-tech industries who want to transition into the tech industry can use this course to learn how to analyze data using code instead of tools. But, you'll need some prior experience in coding or scripting to be successful.
If you have no prior coding or scripting experience, you should NOT take this course - yet. Go take an introductory Python course first.

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