babymore87
MyBoerse.bz Pro Member
Complete AI bootcamp
Published 9/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 7h 21m | Size: 2.57 GB
Learn Python, ML Algorithms, Statistics, Data Science, Model Evaluation, and Deep Learning from the Ground Up
What you'll learn
Understand the fundamentals of Reinforcement Learning, including agents, environments, states, actions, rewards, and policies.
Derive and apply the Bellman equation to understand value functions, rewards, returns, and decision-making in RL.
Understand Markov Decision Processes and how states, actions, transitions, rewards, and discount factors model sequential decisions.
Distinguish optimal policies from fixed plans and understand how Temporal Difference learning updates value estimates from experience.
Requirements
No prior Reinforcement Learning experience is required. Basic Python and familiarity with fundamental AI or machine learning concepts are helpful but not mandatory.
Description
Want to learn Machine Learning and Artificial Intelligence from the ground up?
This course takes you on a structured journey from the fundamentals of AI and Machine Learning to practical Machine Learning algorithms and Deep Learning concepts.
You don't need to be an expert in mathematics or Machine Learning to get started. We begin with the foundations and progressively build your knowledge step by step, so you understand not only how to use Machine Learning algorithms, but also why they work.
You will start by understanding the difference between Artificial Intelligence, Machine Learning, and Deep Learning. You'll learn about supervised and unsupervised learning, the complete Machine Learning workflow, parameters and hyperparameters, training, validation and testing, bias and variance, overfitting, and underfitting.
Next, you'll build the mathematical foundation required for Machine Learning. This includes probability, statistics, distributions, Bayes' theorem, hypothesis testing, linear algebra, vectors, matrices, eigenvalues, eigenvectors, and more.
You'll then learn how to work with real-world data using Python, NumPy, and Pandas. You'll learn data preprocessing, missing-value handling, outlier detection, encoding, normalization, standardization, feature scaling, feature engineering, and data leakage.
The course then moves into exploratory data analysis and visualization before covering core Machine Learning algorithms including Linear Regression, Logistic Regression, K-NN, Naive Bayes, Decision Trees, SVM, Random Forest, Bagging, Gradient Boosting, and other ensemble techniques.
You'll also learn how to evaluate Machine Learning models using confusion matrices, accuracy, precision, recall, F1-score, cross-validation, regularization, and bias-variance analysis.
The course continues into unsupervised learning with K-Means, hierarchical clustering, DBSCAN, and dimensionality reduction with PCA.
Finally, you'll build the foundation for Deep Learning by learning neural networks, perceptrons, weights, biases, activation functions, forward propagation, loss functions, backpropagation, gradient descent, and CNN fundamentals.
By the end of the course, you'll have a structured understanding of the complete Machine Learning journey-from data and mathematics to algorithms, model evaluation, and Deep Learning.
This course is designed to help you build strong fundamentals rather than simply memorize algorithms.
AI USE DISCLOSURE: Artificial intelligence tools may be used to assist with aspects of course creation, such as brainstorming, structuring, editing, or production support. All course material should be reviewed and validated by the instructor for accuracy and quality.
Who this course is for
This course is designed for students, programmers, AI and machine learning enthusiasts, and anyone who wants to build a strong conceptual foundation in Reinforcement Learning. It is suitable for beginners as well as learners who know basic machine learning and want to understand how intelligent agents make sequential decisions using Bellman equations, Markov Decision Processes, policies, and Temporal Difference learning.
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