1 00:00:00,050 --> 00:00:03,140 Lesson, machine learning basics and training methods. 2 00:00:03,170 --> 00:00:09,170 Machine learning is a core discipline within the broader field of artificial intelligence, focusing 3 00:00:09,170 --> 00:00:15,320 on the development of algorithms that enable computers to learn from and make decisions based on data. 4 00:00:15,920 --> 00:00:22,010 Unlike traditional programming, where a developer explicitly codes instructions for specific tasks, 5 00:00:22,010 --> 00:00:28,490 machine learning leverages statistical techniques to identify patterns within large data sets, enabling 6 00:00:28,490 --> 00:00:33,500 the system to improve its performance over time without direct human intervention. 7 00:00:34,010 --> 00:00:39,410 This lesson delves into the fundamental concepts of machine learning and the primary methods used for 8 00:00:39,410 --> 00:00:45,620 training ML models, providing a detailed explanation suitable for professionals seeking to gain a deep 9 00:00:45,650 --> 00:00:47,960 understanding of these critical areas. 10 00:00:50,090 --> 00:00:56,210 At the heart of machine learning lies the concept of a model, which is a mathematical representation 11 00:00:56,210 --> 00:00:58,370 of a real world process. 12 00:00:58,670 --> 00:01:05,210 The process of learning involves adjusting the parameters of this model to minimize errors in its predictions. 13 00:01:05,750 --> 00:01:11,090 This is typically achieved through a training process where the model is exposed to a substantial amount 14 00:01:11,090 --> 00:01:11,840 of data. 15 00:01:12,710 --> 00:01:17,810 One of the most commonly used types of machine learning is supervised learning, where the model is 16 00:01:17,810 --> 00:01:19,010 trained on labeled data. 17 00:01:19,040 --> 00:01:24,290 Datasets that include both input variables and the corresponding output variables. 18 00:01:24,650 --> 00:01:30,230 The objective is to learn a mapping from inputs to outputs that can be used to predict the outputs for 19 00:01:30,230 --> 00:01:31,970 new, unseen inputs. 20 00:01:32,540 --> 00:01:38,420 Examples of supervised learning algorithms include linear regression, logistic regression, support 21 00:01:38,420 --> 00:01:40,850 vector machines, and neural networks. 22 00:01:42,230 --> 00:01:48,020 Linear regression, one of the simplest forms of supervised learning, is used for predicting a continuous 23 00:01:48,020 --> 00:01:51,650 output variable based on one or more input features. 24 00:01:52,190 --> 00:01:58,160 The goal is to find the linear relationship that best fits the data, typically using the method of 25 00:01:58,160 --> 00:02:04,370 least squares to minimize the sum of the squared differences between the observed and predicted values. 26 00:02:05,210 --> 00:02:10,890 Logistic regression, on the other hand, is used for binary classification problems where the output 27 00:02:10,890 --> 00:02:13,860 variable can take on one of two possible values. 28 00:02:13,890 --> 00:02:20,790 It uses the logistic function to model the probability that a given input belongs to a particular class. 29 00:02:22,290 --> 00:02:27,900 Support vector machines are another powerful supervised learning algorithm used for classification and 30 00:02:27,900 --> 00:02:29,160 regression tasks. 31 00:02:29,850 --> 00:02:35,700 SVMs work by finding the hyperplane that best separates the data into different classes, with the goal 32 00:02:35,700 --> 00:02:38,250 of maximizing the margin between the classes. 33 00:02:38,460 --> 00:02:44,460 This is achieved by solving an optimization problem that balances the margin width and the classification 34 00:02:44,460 --> 00:02:45,090 error. 35 00:02:45,660 --> 00:02:51,360 Neural networks inspired by the human brain consist of layers of interconnected nodes. 36 00:02:52,170 --> 00:02:57,600 Each connection has an associated weight, which is adjusted during training to minimize the prediction 37 00:02:57,600 --> 00:02:58,170 error. 38 00:02:59,400 --> 00:03:05,430 Deep learning A subset of machine learning, involves neural networks with many layers, and is particularly 39 00:03:05,430 --> 00:03:09,300 effective for complex tasks such as image and speech recognition. 40 00:03:10,400 --> 00:03:13,280 While supervised learning requires labeled data. 41 00:03:13,310 --> 00:03:19,160 Unsupervised learning deals with unlabeled data, where the goal is to discover the underlying structure 42 00:03:19,160 --> 00:03:20,900 or patterns within the data. 43 00:03:21,650 --> 00:03:26,840 Clustering and dimensionality reduction are two common types of unsupervised learning. 44 00:03:27,290 --> 00:03:33,350 Clustering algorithms such as K-means and Hierarchical Clustering group similar data points together 45 00:03:33,350 --> 00:03:35,960 based on a predefined similarity measure. 46 00:03:36,590 --> 00:03:42,560 Dimensionality reduction techniques such as principal component analysis and t-distributed stochastic 47 00:03:42,560 --> 00:03:48,050 neighbor embedding reduce the number of input features while preserving the essential information, 48 00:03:48,050 --> 00:03:51,920 making it easier to visualize and analyze high dimensional data. 49 00:03:54,380 --> 00:03:59,360 Reinforcement learning is another important area of machine learning, where an agent learns to make 50 00:03:59,360 --> 00:04:02,150 decisions by interacting with its environment. 51 00:04:02,750 --> 00:04:09,020 The agent receives rewards or penalties based on its actions, and aims to maximize the cumulative reward 52 00:04:09,020 --> 00:04:10,010 over time. 53 00:04:10,460 --> 00:04:16,770 RL has been successfully applied to various domains, including game playing, robotics, and autonomous 54 00:04:16,770 --> 00:04:17,520 driving. 55 00:04:18,180 --> 00:04:24,030 The training process in RL involves exploring the environment, learning from the outcomes of actions, 56 00:04:24,030 --> 00:04:27,660 and exploiting the acquired knowledge to make better decisions. 57 00:04:28,920 --> 00:04:35,430 Training a machine learning model involves several key steps, starting with data collection and pre-processing. 58 00:04:36,090 --> 00:04:42,420 High quality data is crucial for building accurate models, and this often requires cleaning and transforming 59 00:04:42,420 --> 00:04:45,300 raw data to ensure it is suitable for analysis. 60 00:04:45,330 --> 00:04:51,990 This may involve handling missing values, normalizing numerical features, encoding categorical variables, 61 00:04:51,990 --> 00:04:54,780 and splitting the data into training and test sets. 62 00:04:55,290 --> 00:05:00,930 The next step is feature engineering, where relevant features are selected or created to improve the 63 00:05:00,930 --> 00:05:02,250 model's performance. 64 00:05:02,850 --> 00:05:08,700 Feature selection techniques such as recursive feature elimination and mutual information help identify 65 00:05:08,700 --> 00:05:10,410 the most important features. 66 00:05:10,410 --> 00:05:16,350 While feature creation involves generating new features based on domain knowledge or through automated 67 00:05:16,350 --> 00:05:18,930 methods like polynomial feature expansion. 68 00:05:21,300 --> 00:05:26,100 Once the data is prepared, the model is trained using an appropriate algorithm. 69 00:05:26,580 --> 00:05:33,300 This involves selecting a learning algorithm, initializing the model parameters, and iteratively updating 70 00:05:33,300 --> 00:05:36,030 the parameters to minimize the prediction error. 71 00:05:37,200 --> 00:05:42,540 The most common optimization technique used in training machine learning models is gradient descent, 72 00:05:42,540 --> 00:05:47,550 which updates the model parameters in the direction of the negative gradient of the loss function. 73 00:05:48,240 --> 00:05:54,060 Variants of gradient descent, such as stochastic gradient descent and mini batch gradient descent, 74 00:05:54,090 --> 00:05:58,470 offer trade offs between computational efficiency and convergence speed. 75 00:05:59,850 --> 00:06:05,250 Regularization techniques are often employed during training to prevent overfitting, where the model 76 00:06:05,250 --> 00:06:09,270 performs well on the training data but poorly on unseen data. 77 00:06:09,810 --> 00:06:16,830 Regularization methods such as L1 and L2 regularization add a penalty term to the loss function to constrain 78 00:06:16,830 --> 00:06:19,980 the model's complexity and improve generalization. 79 00:06:20,250 --> 00:06:26,070 Cross-validation is another important technique used to evaluate the model's performance and ensure 80 00:06:26,070 --> 00:06:27,210 its robustness. 81 00:06:27,750 --> 00:06:33,630 In k fold cross-validation, the data is split into k subsets, and the model is trained and evaluated 82 00:06:33,660 --> 00:06:39,720 k times, each time using a different subset as the validation set and the remaining subsets as the 83 00:06:39,720 --> 00:06:40,620 training set. 84 00:06:41,190 --> 00:06:46,260 The final performance metric is obtained by averaging the results from all K iterations. 85 00:06:47,850 --> 00:06:53,430 Hyperparameter tuning is a critical step in the training process, as the choice of hyperparameters 86 00:06:53,430 --> 00:06:56,610 can significantly impact the model's performance. 87 00:06:57,420 --> 00:07:02,820 Hyperparameters are settings that control the learning process, such as the learning rate, the number 88 00:07:02,820 --> 00:07:06,750 of layers in a neural network, or the regularization strength. 89 00:07:07,680 --> 00:07:13,170 Grid search and randomized search are common methods for systematically exploring the hyperparameter 90 00:07:13,170 --> 00:07:19,650 space, while more advanced techniques such as Bayesian optimization offer a more efficient approach 91 00:07:19,650 --> 00:07:24,460 by modeling the relationship between hyperparameters and the objective function. 92 00:07:25,840 --> 00:07:31,300 Once the model is trained and validated, it can be deployed for making predictions on new data. 93 00:07:31,780 --> 00:07:37,750 However, it is essential to continuously monitor the model's performance in production as changes in 94 00:07:37,750 --> 00:07:43,420 the data distribution or the emergence of new patterns can lead to model degradation over time. 95 00:07:43,990 --> 00:07:50,620 Model maintenance involves periodically retraining the model with updated data, fine tuning the hyperparameters, 96 00:07:50,620 --> 00:07:53,260 and incorporating new features as needed. 97 00:07:54,340 --> 00:07:59,710 In summary, machine learning is a powerful tool that enables computers to learn from data and make 98 00:07:59,710 --> 00:08:01,030 informed decisions. 99 00:08:01,540 --> 00:08:07,210 The training process involves several steps, including data collection and pre-processing, feature 100 00:08:07,210 --> 00:08:14,170 engineering, model selection and training, regularization, cross-validation, hyperparameter tuning, 101 00:08:14,170 --> 00:08:15,700 and model deployment. 102 00:08:16,330 --> 00:08:21,490 By understanding the fundamental concepts and methods used in machine learning, professionals can develop 103 00:08:21,490 --> 00:08:27,070 robust models that drive innovation and solve complex problems across various domains.