CBTNuggets Introduction to Machine Learning

**XHORST** · Yesterday, 07:11 PM

26.36 GB | 00:15:24 | mp4 | 1920X1076 | 16:9
Genre:eLearning |Language:English

Files Included :
1 Introduction (35.71 MB)
2 What is Artificial Intelligence (163.06 MB)
3 Grand Search Auto (140.49 MB)
4 Explore the Frontier (61.61 MB)
5 Depth-First Search (69.64 MB)
6 Breadth-First Search (98.84 MB)
7 Greedy-Best First and A Search (73.91 MB)
1 Introduction (100.21 MB)
2 What is Feature Engineering (78.94 MB)
3 Handling Missing Data (104.77 MB)
4 Handling Outliers (70.61 MB)
5 One Hot Encoding (61.03 MB)
6 Define, Split and Scale Features (88.09 MB)
7 Measuring Survival Accuracy (32.44 MB)
1 Introduction (68.45 MB)
2 From Regression to Classification (85.54 MB)
3 Logistic Regression (65.45 MB)
4 Decision Trees (56.07 MB)
5 Random Forests (105.68 MB)
6 Support Vector Machines (43.69 MB)
7 Perceptrons (51.17 MB)
1 Introduction (150.75 MB)
2 What is Logistic Regression (64.34 MB)
3 The Sigmoid Formula and Function (49.36 MB)
4 Logistic Regression in 4 lines of Code (81.89 MB)
5 Implement Logistic Regression - Part 1 Data Preprocessing, Cleaning, and Encoding (160.35 MB)
6 Part 2 Implement Logistic Regression and Measure Performance (83.35 MB)
1 Introduction (85.85 MB)
2 Concepts Video (132.14 MB)
3 Entropy, Information Gain, and Gini Impurity (63.26 MB)
4 Import Libraries, Feature Engineering and One-Hot Encoding (155.72 MB)
5 Train, Test, Predict, and Measure Model Performance (121.13 MB)
1 Introduction (68.97 MB)
2 What is a Random Forest (58.31 MB)
3 Random Forest Concepts (73.81 MB)
4 Import Libraries, Feature Engineering and One-Hot Encoding (104.23 MB)
5 Train, Test, Predict, and Measure Model Performance (79.29 MB)
6 Bonus Hyperparameter Tuning Video (29.95 MB)
1 Introduction (90.19 MB)
2 What is Overfitting (78.82 MB)
3 Three Options for Handling Overfitting (75.07 MB)
4 Overfitting for Classification (60.34 MB)
5 Comparing Cost Functions (68.38 MB)
6 Perform Logistic Regression with Regularization (70.72 MB)
1 Introduction (78.5 MB)
2 What is a Support Vector Machine (73.25 MB)
3 Optimal Hyperplanes and the Margin (67.19 MB)
4 Data Loading and PreProcessing (151.34 MB)
5 Build and Evaluate the Model (73.41 MB)
6 Breast Cancer Wisconsin (Diagnostic) Dataset (42.67 MB)
1 Introduction (178.44 MB)
2 What is K-Nearest Neighbors (71.58 MB)
3 KNN vs Other Classifiers (68.2 MB)
4 What is Imbalanced Data (51.97 MB)
5 Data Loading and EDA (50.96 MB)
6 Data PreProcessing (81.02 MB)
7 Build and Evaluate the Model (80.38 MB)
1 Introduction (81.75 MB)
2 Neurons as the building blocks of neural networks (33.98 MB)
3 Perceptrons As Artificial Neurons (67.34 MB)
4 How Activation Functions Work (53.02 MB)
5 Why Linearly Separable Data Is Key (54.82 MB)
6 Build A Simple Binary Perceptron Classifier (111.95 MB)
7 Challenge Complete The Perceptron Function �� (63.38 MB)
8 Solution Video (75.45 MB)
1 Introduction (84 MB)
2 What is a Perceptron (35.05 MB)
3 The Perceptron Rule and Neurons (111.91 MB)
4 Implement a Perceptron from Scratch (141.9 MB)
5 The Perceptron Challenge (39.87 MB)
6 Solution Video (71.51 MB)
7 Bonus Resources (91.21 MB)
1 Introduction (29.83 MB)
2 Probability of Rolling One 6-sided Die (103.92 MB)
3 Die Roll Simulation (70.34 MB)
4 Die Roll Probabilities (58.76 MB)
5 Probability of Rolling Two 6-sided Dice (73.01 MB)
6 Probability Distribution of Rolling Two 6-sided Dice (20.68 MB)
1 Introduction (66.46 MB)
2 What Is PyTorch and Why It Is Useful (63.57 MB)
3 Set up a PyTorch Development Environment (44.32 MB)
4 Leverage Tensors Concepts (51.94 MB)
5 Leverage Tensors Programmatically (58.53 MB)
6 Challenge (46.37 MB)
1 Introduction (66.75 MB)
2 Tensor attributes (69.55 MB)
3 Tensor Math Operators (50.19 MB)
4 Matrix Multiplication (64.04 MB)
5 The PyTorch Double Challenge (71.77 MB)
1 Introduction (38.84 MB)
2 Review Matrix Multiplication Errors (97.2 MB)
3 Min, Max, Mean, and Sum (Tensor Aggregation) (54.58 MB)
4 Navigating Positional Min Max Values (41.33 MB)
5 The Challenge (73.99 MB)
6 Solution Video (50.99 MB)
7 Bonus Resources (36.86 MB)
1 Introduction (36.89 MB)
2 Reshape, View, and Stack Tensors (105.87 MB)
3 Squeeze and Unsqueeze Tensors (68.65 MB)
4 Permute Tensors (46.98 MB)
5 Index Tensors (59.24 MB)
6 Challenge Tensor Transformer (58.77 MB)
7 Solution Video (40.09 MB)
1 Introduction (110.78 MB)
2 Gradient Descent (16.38 MB)
3 Forward Propagation (53.99 MB)
4 Back Propagation (74.71 MB)
5 Training, Validation, and Test Datasets (41.5 MB)
6 Split The Train Test Datasets (162.34 MB)
7 Build a Linear Regression Model (106.67 MB)
1 Introduction (46.71 MB)
2 Device Agnostic Conditions & Load Data (41.76 MB)
3 Pre-Processing (36.11 MB)
4 Model Building (40.69 MB)
5 Mini-Challenge Model Training & Model Evaluation (66.46 MB)
6 Saving and Loading PyTorch Models (63.71 MB)
7 Challenge�� (53.38 MB)
1 Introduction (36.53 MB)
2 Review Sklearn Titanic Classification (45.68 MB)
3 Perform PyTorch Titanic Classification - Part1 Import Libraries, Define Model and Load the data (46.49 MB)
4 Perform PyTorch Titanic Classification - Part2 Build model (35.62 MB)
5 Part 3 Fit model (35.86 MB)
6 Challenge - Part 1 Evaluate the Model (85.28 MB)
7 Part 3 Bonus Self-Graded Take-Home Challenge (59.87 MB)
1 Introduction (58.62 MB)
2 Review Logistic Regression PyTorch Workflow (58.73 MB)
3 Load Make Moons Dataset & Pre-processing (64.81 MB)
4 Define Neural Network Architecture (65.38 MB)
5 Train and Evaluate Model (76.96 MB)
6 Visualize Decision Boundary with Probability (13.13 MB)
7 Challenge PyTorch Workflow (40.77 MB)
1 Introduction (22.05 MB)
2 Review Neural Network Classification Without Non-Linearity (80.36 MB)
3 Build a Neural Network Classification With Non-Linearity - Step 1 Load Dataset, Pre-processing, and Make Circles (59.91 MB)
4 Build a Neural Network Classification With Non-Linearity - Step 2 Define Neural Network Architecture (54.02 MB)
5 Step 3 Add Non-Linear Activation Function ReLu (53.82 MB)
6 Step 4 Train Model (77.01 MB)
7 Step 5 Evaluate Model (32.11 MB)
8 Challenge PyTorch Workflows �� (56.4 MB)
1 Introduction (15.16 MB)
2 Review of Binary Classification with PyTorch (105.84 MB)
3 Step 1 Setup and Prepare Data (53.03 MB)
4 Step 2 Visualize Data (EDA) (38.37 MB)
5 Step 3 Define Neural Network Architecture (39.94 MB)
6 Challenge �� (43.36 MB)
7 Solution Videos - Training Loop (44.08 MB)
8 Solution Video - Evaluation and Decision Boundary (37.94 MB)
1 Introduction (185.32 MB)
2 What is Machine Learning (160.63 MB)
3 What is Machine Learning (154.97 MB)
4 Unsupervised (59.59 MB)
5 Build an Image Classifier (142.54 MB)
6 Predicting Lumber Prices with Linear Regression (128.16 MB)
1 Introduction (11.27 MB)
2 Review Explore Multi-class Classification with PyTorch (54.75 MB)
3 Create, Preprocess, and Visualize the Spiral Dataset (54.34 MB)
4 Define Neural Network Architecture (25.67 MB)
5 Explore Hyperparameter Tuning (74.5 MB)
6 Explore Underfitting and Overfitting (43.95 MB)
7 Challenge �� (38.58 MB)
8 Solution Video (54.01 MB)
1 Introduction (78.9 MB)
2 Universal Device Setup in PyTorch 2 0 (35.92 MB)
3 Key Features of PyTorch 2 0 (67.98 MB)
4 Traditional PyTorch 1 0 Vs PyTorch 2 0 torch compile( ) (71.34 MB)
5 Challenge �� (44.38 MB)
6 Challenge �� Part 2 (22.6 MB)
1 Introduction (48.62 MB)
2 Introduction to TensorFlow Tensors (47.88 MB)
3 Part 2 (21.37 MB)
4 Create Tensors with TensorFlow (19.97 MB)
5 Create Random Tensors with Numpy (55.49 MB)
6 Challenge �� (62.5 MB)
1 Introduction (44.04 MB)
2 Why Shuffle Tensors (26.79 MB)
3 TensorFlow Seeds (22.62 MB)
4 Tensor Attributes (23.34 MB)
5 Tensor Indexing (14.49 MB)
6 Changing Tensor Data Types & Tensor Aggregation (32.58 MB)
7 Tensor Positional Methods (33.37 MB)
8 Challenge �� (23.8 MB)
9 Challenge �� Part 2 (28.66 MB)
1 Introduction (17.41 MB)
2 Basic Tensor Operation (16.92 MB)
3 TensorFlow Math Functions (26.8 MB)
4 Matrix Multiplication Foundations (58.27 MB)
5 Perform Matrix Multiplication (64.73 MB)
6 Challenge (58.79 MB)
1 Introduction (11.32 MB)
2 Review Matrix Multiplication (50.33 MB)
3 Altering Tensors (37.24 MB)
4 Transpose & Reshape Tensors (27.99 MB)
5 Tensor Expansion (47.85 MB)
6 Challenge �� (76.01 MB)
7 Part 1 (63.73 MB)
8 Part 2 (22.95 MB)
1 Introduction (26.66 MB)
2 Squeezing Tensors (74.13 MB)
3 One-Hot Encoding (38.79 MB)
4 Numpy = Friend ❤️ (52.86 MB)
5 GPU & TPU Tensor Optimization (52.48 MB)
6 Challenge �� (22.55 MB)
7 Challenge �� part 2 (63.83 MB)
1 Introduction (10.32 MB)
2 What is Regression Analysis (63.85 MB)
3 Neural Network Architecture (108.25 MB)
4 Build a Model (104.05 MB)
5 Challenge �� (58.31 MB)
6 Solution Video (94.75 MB)
1 Introduction (77.09 MB)
2 Build a Small Regression Model from Memory (59.15 MB)
3 Build Model From Scratch (108 MB)
4 Challenge Improve Model (108.61 MB)
5 Solution Part 1 (66.79 MB)
6 Solution Part 2 (59.12 MB)
1 Introduction (64.06 MB)
2 Regression Challenge (55.8 MB)
3 Preprocess Data (70.38 MB)
4 �� Challenge Build Model (49.67 MB)
5 Challenge Solution (114.58 MB)
1 Introduction (103.81 MB)
2 Locally (164.74 MB)
3 Starting and Ending a Session (74.23 MB)
4 Google Colab (143.88 MB)
5 Cloud Services AWS, GCP, and Azure (146.22 MB)
6 Vast ai the market leader in low-cost cloud GPU rental (84.23 MB)
1 Introduction (34.24 MB)
2 Generate Linear Transformation Data (71.79 MB)
3 Common Evaluation Metrics MAE, MSE, & Huber (78.14 MB)
4 Split Data for Train and Test Datasets (103.36 MB)
5 Define Basic Model Architecture (33.64 MB)
6 Make Predictions and Evaluate Model (56.35 MB)
7 Challenge (45.23 MB)
8 Solution Video (64.02 MB)
1 Introduction (73.42 MB)
2 Handle Imports & Load Dataset (35.97 MB)
3 One-hot Encode & Separate Features and Target (31.38 MB)
4 Perform TrainTest Split (24.03 MB)
5 Define Model Architecture (34.63 MB)
6 Evaluate Model and Visualize Loss (31.32 MB)
7 What is Normalization and Standardization (11.42 MB)
8 �� Challenge (63.51 MB)
9 Solution Video (47.61 MB)
1 Introduction (85.45 MB)
2 What is Classification (96.48 MB)
3 What is Binary Classification (54.21 MB)
4 What is Multi-Class Classification (38.31 MB)
5 What is Multi-Label Classification (60.53 MB)
6 Classification Code Example (62.66 MB)
7 �� Challenge (37 MB)
8 Solution (86.75 MB)
1 Introduction (41.84 MB)
2 Pseudocode Image Classification (25.53 MB)
3 Create Circles Dataset & EDA (61.14 MB)
4 Build, Compile, and Train Model (34.55 MB)
5 Visualize and Evaluate Model (65.7 MB)
6 �� Challenge (35.53 MB)
7 Solution Video (47.5 MB)
8 Bonus Video (39.53 MB)
1 Introduction (74.21 MB)
2 Create Circles DataSet (41.18 MB)
3 Create Second Model (70.72 MB)
4 Create Third Model (45.85 MB)
5 Create Fourth Model (90.15 MB)
6 �� Challenge (12.71 MB)
7 Solution (63.52 MB)
1 Review Learning Rates (64.36 MB)
2 Adaptive Learning Rates Part 1 (40.28 MB)
3 Part 2 (28.13 MB)
4 Part 3 (97.92 MB)
5 Big Five Evaluation Metrics (28.33 MB)
6 Solution Video (30.76 MB)
1 Compare Binary and Multi-Class Classification (62.21 MB)
2 Create a Teachable Machine Multi-Class Classifier (125.04 MB)
3 Review Model Building Steps (20.49 MB)
4 Load and Explore MNIST Fashion Dataset (99.74 MB)
5 �� Challenge (30.86 MB)
6 Solution Video (69.6 MB)
1 Introduction (55.48 MB)
2 Review MNIST Fashion Multi-Class Classifie (79.44 MB)
3 Load and Visualize Dataset (55.26 MB)
4 One-Hot Encode Features and Build Model (109.49 MB)
5 Softmax and Validation Exploration (50.36 MB)
6 �� Challenge (70.08 MB)
7 Solution Video (50.02 MB)
1 Introduction (29.94 MB)
2 Binary, Multi-Class, and Multi-Label Classification (195.79 MB)
3 Start Building a Multi-Label Classifier (54.29 MB)
4 Build a Sequential Multi-Label Model (46.41 MB)
5 Evaluate Model (51.63 MB)
6 �� Challenge (39.16 MB)
7 Solution Video (34.75 MB)
1 Introduction (59.17 MB)
2 What is a Large Language Model (LLM) (98.6 MB)
3 How do LLMs work (34.73 MB)
4 Two Kinds of LLMs Base and Instruction Tuned (51.85 MB)
5 System Messages and Tokens (37.72 MB)
6 System Messages and Tokens Part 2 (31.32 MB)
7 Challenge Connect Google Colab to ChatGPT via OpenAI's API (73.23 MB)
1 Introduction (84.76 MB)
2 What is a Machine Learning Model (120.08 MB)
3 Predicting Lumber Prices Data Collection (95.57 MB)
4 Predicting Lumber Prices Data Cleaning & Preprocessing (53.52 MB)
5 Predicting Lumber Prices Feature Extraction (169.7 MB)
1 Introduction (45.16 MB)
2 Web Chat Interfaces Vs Programmatic Notebooks (81.45 MB)
3 Route Queries Using Classification for Different Cases (131.11 MB)
4 Evaluate Inputs to Prevent Prompt Injections (21.58 MB)
5 Implement The OpenAI Moderation API (117.45 MB)
6 Sanitize and Validate Inputs Injection Attacks (95.9 MB)
7 Challenge Filter Inputs with a Chain of Thought Prompt Filter (130.08 MB)
1 Introduction (48.33 MB)
2 Iterative Prompt Engineering (206.89 MB)
3 Build a Summarizer for Interesting Topics (133.82 MB)
4 Implement Supervised Learning Through Inference (64.75 MB)
5 Challenge Build The AutoBot ChatBot To Manage Orders (170.34 MB)
1 Introduction (59.72 MB)
2 Compare Direct API Calls Vs API Calls Through LangChain (96.63 MB)
3 Leverage LangChain Templating for Complex Prompts (178.09 MB)
4 Leverage Power of Templating for DRY Code (76.54 MB)
5 Challenge (26.11 MB)
6 Solution (89.18 MB)
1 Introduction (52.72 MB)
2 ConversationBufferMemory (126.22 MB)
3 ConversationBufferWindowMemory (60.34 MB)
4 ConversationTokenBufferMemory (34.34 MB)
5 ConversationSummaryBufferMemory (76.89 MB)
6 The Power of Chaining LangChain Components (132.46 MB)
7 Challenge Implement LangChain Memory (143.43 MB)
1 Introduction (85.33 MB)
2 Chaining in LangChain (42.59 MB)
3 LLMChain (70.59 MB)
4 SimpleSequentialChain (53.28 MB)
5 SequentialChain (65.32 MB)
6 RouterChain (130.89 MB)
7 Challenge (79.3 MB)
1 Introduction (85.4 MB)
2 Leverage LangChain Agents (51.98 MB)
3 Perform math calculation using an Math LLM (65.31 MB)
4 Use Wikipedia to Find General Information (61.65 MB)
5 Program using a Python REPL tool (21.25 MB)
6 Create new custom agents and tooling (BabyAGI) (31.6 MB)
7 Debugging with LangChain (97.83 MB)
8 Challenge (73.66 MB)
1 Introduction (69.54 MB)
2 Retrieval Augmented Generation (RAG) over 2 Skills (46.69 MB)
3 Document Loaders (46.26 MB)
4 Document Separation (71.65 MB)
5 Embeddings (70.76 MB)
6 Vector Stores (97.66 MB)
1 Introduction (41.31 MB)
2 Similarity Search (51.86 MB)
3 Maximum Margin Relevance (77.15 MB)
4 ContextualCompressionRetriever + MMR (56.97 MB)
5 Chat Q&A (60.79 MB)
6 Chat Q&A Part 2 (70.08 MB)
7 Challenge (130.31 MB)
1 Introduction (82.19 MB)
2 What are Transformers (39.36 MB)
3 Attention Is All You Need (Optional) (157.76 MB)
4 Encoders (24.06 MB)
5 Decoders (29.59 MB)
6 Encoder-Decoders (16.42 MB)
7 What is HuggingFace Again (53.52 MB)
1 Introduction (108.55 MB)
2 What is HuggingFace �� (44.75 MB)
3 Models (133.82 MB)
4 Datasets (71.09 MB)
5 Spaces (147.05 MB)
6 ChatGPT Competitor HuggingChat �� (16.19 MB)
7 Challenge (112.96 MB)
1 Introduction (91.54 MB)
2 A Brief and Bizarre History of Linear Regression (82.18 MB)
3 Explore Linear Relationships Ordinary Least Squares (189.71 MB)
4 Seaborn Line of Best Fit (66.33 MB)
5 Ordinary Least Squares with Matlab's PolyFit (122.69 MB)
6 Challenge (71.83 MB)
1 Introduction (72.33 MB)
2 Mean Absolute Error (39.53 MB)
3 Mean Squared Error (34.19 MB)
4 Root Mean Squared Error (60.01 MB)
5 Cost Functions (62.08 MB)
6 Calculate Your Model's Performance (197.92 MB)
1 Introduction (68.92 MB)
2 Exploring Gradient Descent Concepts (72.78 MB)
3 Exploring The Gradient Descent Algorithm (73.02 MB)
4 Gradient Descent Behind the Scenes (85.42 MB)
5 Implementing The Gradient Descent Algorithm (123.26 MB)
1 Introduction (94.86 MB)
2 Multiple Linear Regression (70.91 MB)
3 Vectorization (65.88 MB)
4 Implementation Video (88.12 MB)
5 Non-Vectorized Operations (101.25 MB)
6 Interpreting the Weights (36.76 MB)

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