Python for Machine Learning full Course | Learn AI

Python for Machine Learning full Course | Learn AI

Are you ready to dive into the exciting world of Machine Learning with Python? Join us for a comprehensive, hands-on, and FREE Python Machine Learning Training that's perfect for beginners and aspiring data analyst!

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Python, Machine Learning, Data Science, Artificial Intelligence, Deep Learning, Neural Networks, Algorithms, Data Analysis, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Natural Language Processing, Computer Vision, TensorFlow, PyTorch, Scikit-Learn, Keras, Pandas, NumPy, Matplotlib, Data Preprocessing, Feature Engineering, Cross-Validation, Hyperparameter Tuning, Classification, Regression, Clustering, Dimensionality Reduction, Overfitting, Underfitting, Decision Trees, Random Forest, Support Vector Machines, Gradient Descent, Convolutional Neural Networks, Recurrent Neural Networks, Transfer Learning, Gradient Boosting, K-Means, PCA (Principal Component Analysis), SVM (Support Vector Machine), Overfitting, Regularization, Jupyter Notebook, Feature Selection, Model Evaluation, ROC Curve, AUC (Area Under the Curve), Cross-Entropy, Precision-Recall, AutoML, Reinforcement Learning, XGBoost, LSTMs (Long Short-Term Memory), GANs (Generative Adversarial Networks), RNN (Recurrent Neural Network), Deep Reinforcement Learning, NLP (Natural Language Processing), Computer Vision, Time Series Analysis, Anomaly Detection, Recommendation Systems, Sentiment Analysis, Image Classification, Text Classification, Hyperparameter Optimization, Transfer Learning, Neural Architecture Search, Feature Scaling, Ensemble Learning, Regression Analysis, Data Visualization, Regularization Techniques, Naive Bayes, Stochastic Gradient Descent, Unstructured Data, Bias-Variance Tradeoff, One-Hot Encoding, Word Embeddings, Bag of Words, Batch Normalization, Data Augmentation, Grid Search, Cross-Validation, Mean Squared Error (MSE), L1 and L2 Regularization, Learning Rate, Support Vector Regression, Reinforcement Learning Algorithms, Natural Language Generation, Time Series Forecasting, Image Segmentation, Data Imputation, Model Deployment, AI Ethics, Explainable AI, Interpretability, Model Explainability, Bias and Fairness in AI.

Data Scientist, Data Analysis, Machine Learning, Python, R Programming, Statistical Analysis, Big Data, Predictive Modeling, Data Mining, Data Visualization, Artificial Intelligence, SQL, Data Engineering, Deep Learning, Statistics, Data Analytics, Pandas, NumPy, Scikit-Learn, Regression Analysis, Clustering, Classification, Natural Language Processing, Computer Vision, Feature Engineering, Model Evaluation, A/B Testing, Time Series Analysis, Hadoop, Spark, Data Wrangling, Tableau, Power BI, Data Cleaning, Data Transformation, Exploratory Data Analysis, Supervised Learning, Unsupervised Learning, Dimensionality Reduction, Data Preprocessing, Business Intelligence, Data Warehousing, Cloud Computing, AWS, Azure, Google Cloud, Statistical Models, Data Pipelines, Data Strategy, Data-driven Decision Making, Data Integration, Data Architecture, Data Quality, Data Governance, Predictive Analytics, Data Lakes, Time Series Forecasting, Feature Selection, Data Ethics, Data Privacy, Data Security, Data Exploration, Data Science Tools, Data Storage, Natural Language Understanding, Data Modeling, Data Storytelling, Business Insights, Data-Driven Insights, Data Management, ETL (Extract, Transform, Load), Data Warehouse.

[mrgeneral]

25:14:34 Data representation as binary for machine learning
25:24:18 Understanding the concept of vocabulary and term frequency
25:29:59 Understanding Term Frequency
25:37:34 Analyzing the importance of specific words in the documents for comparison
25:41:27 Term frequency divided by document frequency is the score of a word.
25:49:01 Introduction to tokenization and normalization
25:53:41 Pre-processing steps for NLP involve stop word removal and lemmatization.
26:02:03 Retrieving relevant documents to a query
26:05:56 Challenges in natural language processing
26:14:26 Tokenization process for words in English and Chinese
26:17:55 Introduction to machine learning concepts
26:25:52 Utilizing natural language processing in document matching for better search results.
26:29:19 Natural language processing requires understanding and tuning data for best results
26:37:11 Using TF-IDF score for document classification
26:41:14 Using machine learning to analyze consumer reviews and ratings
26:48:48 Understanding TF-IDF and feature vectors
26:53:35 Using cosine similarity in clustering
27:03:47 Understanding false positives and false negatives
27:08:37 Imbalanced data affects accuracy by skewing predictions towards the majority class
27:17:43 Different models have different characteristics and properties
27:21:06 Integration of NLP and deep learning for a comprehensive course
29:47:04 Deep learning's relationship to machine learning and artificial intelligence
29:51:11 Features are crucial for machine learning, but if they're not provided, deep learning can still generate and use them.
29:58:38 Facebook uses image features for caption generation
30:02:13 Deep learning for unstructured data and image generation.
30:09:25 TensorFlow for deep learning and AI
30:16:43 The input layer consists of 784 pixels representing the features of an image.
30:26:57 Feature reduction and combination for informative representation.
30:30:51 Explanation of neural network weights and connections.
30:39:44 Basics of machine learning are crucial for deep learning effectiveness
30:43:41 Discussing the use of different coefficients and features in the same form for deep learning
30:52:49 Understanding linear regression and backpropagation
30:58:10 Neural network weights and optimization
31:05:18 Deep learning does not have predefined features and instead uses pixel values to predict outputs
31:08:37 In image classification, the model uses past data to predict the brand of a car based on pixel features.
31:15:57 Deep learning allows pixels to talk to each other to understand the relative sense of space.
31:19:13 Pixel communication is crucial for machine learning to understand visual data.
31:26:11 Understanding probabilities and feature extraction in machine learning.
31:29:46 Features and pixels are used for input in the model to allow them to interact and predict the output.
31:37:20 Introduction to convolution networks and their benefits
31:44:27 Convolution reduces complexity and disconnects pixels far from the current pixel.
31:52:16 Overview of deep learning
31:55:29 Training process in machine learning
32:02:52 Deep learning involves learning various algorithms to extract features and predictions.
32:06:23 Word embeddings help understand contextual similarities and relationships.

[mrgeneral]

13:26:25 Pruning in machine learning reduces the complexity of decision trees
13:35:04 Decision trees can be used for regression with continuous variables.
13:39:38 Geni index minimizes classification, RMS minimizes regression
13:50:25 Ensemble learning helps in combining insights from different models.
13:54:56 Random Forest is made of a large number of trees employing the concept of bagging and random selection of predictors.
14:03:14 Random Forest creates randomness and uses bagging for constructing a large number of trees.
14:11:42 Introduction to hyperparameters in Random Forest
14:21:33 Series learning and boosting error
14:26:43 Boosting algorithms increase accuracy better than previous algorithms
14:38:09 Introduction to decision trees in machine learning
14:43:30 Partitioning the variable space for accurate decision-making
14:50:46 Entropy is the loss function for classification problems based on probabilities.
14:54:25 Entropy should be reduced as you go down the tree
15:01:32 Setting minimum samples and maximum leaf nodes in decision trees
15:04:54 Bagging combines decision trees to create a random forest model
15:12:32 Random Forest increases randomness and reduces cheating
15:17:37 Boosting for improved predictions
15:27:32 Feature selection is crucial for model performance.
15:32:33 Random Forest algorithm for feature selection
15:40:44 Classification and Confusion Matrix
15:45:43 Accuracy is not a good measure for classification with imbalanced classes
15:53:51 Harmonic mean is important for classification problems.
15:57:33 Understanding the theory is crucial for effective data modelling
16:04:31 Using Random Forest and Boruta in Machine Learning Model Building
16:09:19 Feature Selection and Model Performance
16:18:10 Random Forest and decision trees explained
16:21:53 Feature ranking in machine learning
16:29:32 Training random forest models and observing score changes
16:33:11 Optimizing parameters for Random Forest model
16:40:42 Changing model parameters can drastically affect the model's performance.
16:43:47 No single best model, learning all models is important.
16:51:18 Feature selection and engineering are crucial in machine learning modelling.
16:54:10 Understanding the model is critical for selecting the best model
17:01:21 R-squared is a measure of how well the model fits the data.
17:06:37 Understanding the concept of R square and modelling errors
17:14:48 Understanding Gradient Boosting Regressor for Machine Learning
17:17:49 Improved accuracy using boosting algorithm
17:24:46 Boosting is a combination of random forest and a process known as gradient boost.
17:27:37 Support Vector Machine is a valuable model with advantages and disadvantages.
17:38:19 Classifying data based on position relative to a line or plane
17:41:30 Support Vector Machine aims to maximize the margin between data points
17:48:35 Support Vector Machines (SVM) use kernels to transform the data for better separation
17:52:13 Understanding the objective function for defining classes
18:00:13 Introduction to introducing an error to allow for misclassifications
18:04:10 Understanding the concept of error and its impact on the model.
18:15:13 Understanding the n-to-p ratio is crucial for choosing the right model for your data analysis.
18:18:49 Multiclass algorithms use the one-versus rest-approach for classification.
18:27:58 Grid search helps find the best parameters for the boosting model.
18:31:31 Grid search helps find the best model parameters
18:40:49 Naive Bayes is a classifier dealing with probabilities, not for regression problems.
18:46:26 Calculating probability of X1=2 and X2=5 given Y
18:55:02 Assumption of Independence for Machine Learning
18:59:22 Using Bayes' theorem for probability calculations
19:09:09 Predicting gender based on height and weight using normal distribution
19:13:10 Explanation of deviation and probability calculation
19:21:19 Boosting and variable importance in machine learning
19:25:57 Analyzing the impact of changing input variables on price prediction
19:34:44 Unsupervised learning involves analyzing data scatter plots to identify groups and patterns
19:39:08 Iterative process to form clusters and stop when centres do not move
19:46:26 Managing inventory and reducing stale products.
19:49:39 Getting the right data for machine learning in a real business context
19:55:18 Using machine learning to improve business decisions
19:58:10 Quantification of expiry probability is crucial for business decisions
20:04:13 Model's ability to select important variables based on computational power
20:07:11 Identifying and handling outliers in machine learning
20:14:19 Detect and exclude outliers using standard deviation and normal distribution
20:18:03 Continuous learning and adaptation in machine learning models are crucial.
20:24:10 Supervised learning is associating every data row with a y.
20:27:48 Understanding supervised and unsupervised learning
20:35:09 Deep learning can handle both deep learning and neural network
20:38:17 Unsupervised learning involves a simple model called clustering
20:46:01 K-means clustering algorithm
20:49:50 K-nearest neighbours and clustering for predictors
20:57:44 Finding the optimal number of clusters using the elbow joint method.
21:01:15 Choosing initial cluster centres is crucial
21:09:17 Hierarchical clustering and its advantages
21:17:54 Linear discriminant analysis is a classification model dealing with probabilities.
21:27:32 Probability and decision boundary in machine learning
21:31:26 Understanding the components of the equation for clustering points.
21:41:20 Understanding probability calculation for continuous variables
21:45:06 Calculating probability using the formula
21:55:33 Decision boundary calculated based on probability and x value.
21:59:24 Understanding decision boundary and probability calculation
22:07:59 The Objective is to calculate the probability of high given a new point
22:11:05 Introduction to the complexities and basics of deep learning
22:16:57 Understanding the difference and applicability of deep learning and machine learning
22:20:05 The course covers data, programming, and statistics in three different domains.
22:28:54 Accuracy and confidence are independent factors in model evaluation.
22:32:15 Understanding the significance of P value in statistical tests.
22:42:19 Python libraries for machine learning
22:46:46 Linear regression vs polynomial regression and its advantages
22:56:36 Understanding theory is crucial in machine learning
23:02:02 Nonlinear regression involves fitting curves or partitions instead of a single line
23:09:10 Ensembling and stacking can improve accuracy in machine learning.
23:12:34 Cross-validation helps in selecting the best hyperparameter
23:20:16 Stacking is a method of combining multiple models to improve predictions.
23:24:16 Start with Logistic regression and then move to random forest and boosting
23:34:13 Training and testing data split
23:37:17 Label encode categorical variables and plot variable distributions
23:44:37 Data transformation and variable selection for better modelling
23:48:16 Visual importance and distribution of data
23:55:30 Understanding the distribution and sequence of variables in decision tree cuts.
23:58:57 Understanding the Min Max Scaler for scaling data
24:06:21 Using weighted average in regression based on distance
24:10:04 Understanding the importance of priorities in machine learning
24:17:52 Scaling in decision trees and logistic regression
24:20:54 Boosting is the best model for higher accuracy
24:27:36 Conditional distribution provides the best idea
24:30:54 Data collection in rural areas requires strict documentation.
24:37:52 Deep learning is a method that improves the accuracy of natural language processing and other types of data processing.
24:41:24 Python is not meant for visualization, it's for data analysis and modelling.
24:48:31 Machine learning models involve time dynamics and confidence levels.
24:51:27 Python can be integrated with backend systems like SAP Hana for data visualization and decision-making
24:58:22 Understanding structured and unstructured data
25:01:59 Text analytics involves associating numbers with text and representing it as vectors.
25:10:13 Natural language processing is the art of giving intelligence to words.

[arihantjainhant]

please make timestamp on topics please !!!

[subramanianchenniappan]

great . thanks . i am a java developer lead. wanted to learn this domain for new projects

[NurulHuda-ncss]

00:26:00 introduction
00:35:00 python

[kuntalchatterjee]

Appreciate the great teaching. How can I access the data files? It seems that there is no link for the data files. Without that it will be difficult to practice.

Sometimes also the notebook accessibility helps.

[joyprokash]

Need Time series and big data lectures also

[ganeropare]

Purely a waste of time . Some people don't have any prior knowledge and ask silly questions in the beginning of the video and wasting time.