Applications of Machine Learning
Neural Networks
Regressions
Reinforcement Learning
Introduction to Machine Learning
In this foundational module, you'll gain a comprehensive understanding of machine learning, its various types including supervised, unsupervised, and reinforcement learning, and its wide-ranging applications across industries.
Understanding Data
Learn the essential steps of data preprocessing and feature engineering, crucial for preparing datasets for machine learning models. Dive into techniques for handling missing data, scaling features, and encoding categorical variables.
Model Evaluation and Validation
Explore methods for evaluating and validating machine learning models to ensure their effectiveness and generalization to unseen data. Understand concepts such as bias-variance tradeoff, cross-validation, and hyperparameter tuning.
Supervised Learning Algorithms
Delve into the world of supervised learning algorithms, including linear regression for regression tasks, logistic regression for classification, decision trees, random forests, and ensemble learning techniques like boosting and bagging.
Unsupervised Learning Algorithms
Discover unsupervised learning techniques for exploring and uncovering patterns in unlabeled data. Learn about clustering algorithms such as K-means and hierarchical clustering, dimensionality reduction techniques like PCA, and anomaly detection algorithms.
Deep Learning Fundamentals
Get acquainted with the fundamentals of deep learning, including neural network architecture, activation functions, loss functions, and optimization techniques like backpropagation and gradient descent.
Advanced Deep Learning Techniques
Explore advanced deep learning concepts such as transfer learning, generative adversarial networks (GANs) for synthetic data generation, reinforcement learning with deep Q-networks (DQN), and specialized architectures like long short-term memory (LSTM) networks and attention mechanisms.
Real-World Applications
Apply your knowledge to real-world projects and case studies across various domains, including healthcare, finance, retail, and more. Gain practical experience in solving complex problems using machine learning techniques.
Capstone Project
Synthesize your learning by working on a capstone project that challenges you to apply machine learning algorithms to a real-world problem of your choice. Showcase your skills and expertise to potential employers or colleagues.
1 _ML_ Introduction to Python
2_ML_Introducion to ML
3_ML_part_2
4_ML_4th-Supervised ---4.1
5_ML_4th-Supervised ---4.2
6_ML_4th-Supervised ---4.3
7_ML_4th-Supervised ---4.4
8_ML-4th-Supervised ---4.5
9_ML-4th-Supervised ---4.6
10_ML-4th-Supervised ---4.7
11_ML-4th-Supervised ---4.8
12_ML-4th-Supervised ---4.9
13 ML-4th-Supervised ---4.10
14 ML-4th-Supervised ---4.11
15 ML-4th-Supervised ---4.12
16 ML-4th-Supervised ---4.13
17 ML-UN-Supervised -- 1
18 ML-UN-Supervised -- 2
19 ML-UN-Supervised -- 3
20 ML-UN-Supervised -- 4
21 ML agents
22 ML Neural Networks Intro
23 ML Neural Network Arc
24 ML MultiLayer NNA
25 ML Deep Learning
26 ML Intro to NLP
27 ML Text Pre-Processing P-1
28 ML Text Pre-Processing P-2
29 ML Advanced Neural net
30 ML nlp final
11 Reviews
8 months ago
absolutely wholesome classes and understandable lectures. love it
9 months ago
This platform has revolutionized the way we approach data analysis and model training. With its powerful optimization algorithms and hyperparameter tuning capabilities, we've been able to achieve unprecedented accuracy in our predictions. Absolutely love it.
9 months ago
Exceptional platform! Simplifies machine learning with user-friendly interface and powerful algorithms.
Industry Mentor With an Experience of 3+ Years in Machine Learning and Analytics
