12.6 GB | 13min 12s | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English
Files Included :
1 1 1 Welcome to the Full Stack AI Engineer Master Program.mp4 (42.76 MB)
2 1 2 Who Is a Full Stack AI Engineer in 2026.mp4 (59.21 MB)
3 1 3 How This Course Is Structured End to End.mp4 (53.04 MB)
4 1 4 Tools Stack & Skills You'll Use Throughout.mp4 (51.03 MB)
5 1 5 How to Stay Consistent & Finish Strong.mp4 (50.56 MB)
6 2 1 Python Refresher for AI Engineers.mp4 (69.32 MB)
7 2 2 NumPy for Numerical Computing.mp4 (89.21 MB)
8 2 3 Data Handling with Pandas.mp4 (91.06 MB)
9 2 4 Writing Clean Modular ML Code.mp4 (93.25 MB)
10 2 5 Hands On Python Warm Up Lab.mp4 (69.62 MB)
11 3 1 Understanding Dataset Structure.mp4 (85.65 MB)
12 3 2 Missing Values Noise & Outliers.mp4 (84.18 MB)
13 3 3 Visualizing Data with Matplotlib.mp4 (94.25 MB)
14 3 4 Feature Relationships & Correlations.mp4 (93.18 MB)
15 3 5 Hands On EDA Mini Project.mp4 (87.14 MB)
16 4 1 What Is Machine Learning.mp4 (87.01 MB)
17 4 2 Supervised vs Unsupervised Learning.mp4 (104.72 MB)
18 4 3 Regression vs Classification Problems.mp4 (86.72 MB)
19 4 4 Train Validation Test Splits.mp4 (92.86 MB)
20 4 5 End to End ML Workflow.mp4 (145.67 MB)
21 5 1 Linear Regression Intuition.mp4 (142.3 MB)
22 5 2 Linear Regression Math (Simplified).mp4 (137.4 MB)
23 5 3 Implementing Linear Regression in Python.mp4 (145.52 MB)
24 5 4 Model Evaluation MSE RMSE & R².mp4 (125.49 MB)
25 5 5 Bias-Variance Tradeoff.mp4 (115.83 MB)
26 5 6 Mini Project Continuous Value Prediction.mp4 (137.35 MB)
27 6 1 Logistic Regression Explained.mp4 (140.47 MB)
28 6 2 Implementing Logistic Regression.mp4 (152.6 MB)
29 6 3 K Nearest Neighbors (KNN).mp4 (152.84 MB)
30 6 4 Decision Trees & Split Logic.mp4 (148.33 MB)
31 6 5 Classification Metrics Deep Dive.mp4 (171.71 MB)
32 6 6 Mini Project Binary Classification System.mp4 (180.54 MB)
33 7 1 Why Single Models Break.mp4 (149.86 MB)
34 7 2 Random Forests Explained.mp4 (180.04 MB)
35 7 3 Gradient Boosting Intuition.mp4 (175.68 MB)
36 7 4 Feature Importance & Interpretability.mp4 (188.62 MB)
37 7 5 Hands On Boosting Model Performance.mp4 (185.71 MB)
38 8 1 Understanding Unsupervised Learning.mp4 (141.43 MB)
39 8 2 K Means Clustering.mp4 (147.35 MB)
40 8 3 Choosing Optimal Number of Clusters.mp4 (142.78 MB)
41 8 4 Dimensionality Reduction with PCA.mp4 (152.99 MB)
42 8 5 Industry Use Cases of Clustering.mp4 (167.83 MB)
43 9 1 Feature Scaling & Normalization.mp4 (91.68 MB)
44 9 2 Encoding Categorical Variables.mp4 (115 MB)
45 9 3 Feature Selection Strategies.mp4 (110.42 MB)
46 9 4 Cross Validation Explained.mp4 (99.53 MB)
47 9 5 Hyperparameter Tuning (Grid & Random Search).mp4 (108.1 MB)
48 10 1 Building ML Pipelines.mp4 (97.83 MB)
49 10 2 Preventing Data Leakage.mp4 (99.81 MB)
50 10 3 Reproducibility in Machine Learning.mp4 (95.57 MB)
51 10 4 Common ML Mistakes to Avoid.mp4 (119.72 MB)
52 11 1 What Is Deep Learning & Why It Matters.mp4 (73.55 MB)
53 11 2 Machine Learning vs Deep Learning.mp4 (69.54 MB)
54 11 3 Deep Learning Use Cases in Industry.mp4 (72.65 MB)
55 11 4 Deep Learning Roadmap for AI Engineers.mp4 (90.92 MB)
56 11 5 Tools & Frameworks (PyTorch TensorFlow).mp4 (102.22 MB)
57 12 1 Biological Inspiration of Neural Networks.mp4 (128.97 MB)
58 12 2 Artificial Neurons & Perceptrons.mp4 (108.15 MB)
59 12 3 Layers Weights & Biases.mp4 (95.97 MB)
60 12 4 Forward Propagation Explained.mp4 (107.58 MB)
61 12 5 Hands On Neural Network from Scratch.mp4 (114.3 MB)
62 13 1 Why Activation Functions Matter.mp4 (106.44 MB)
63 13 2 Sigmoid Tanh ReLU & Variants.mp4 (106.64 MB)
64 13 3 Choosing the Right Activation.mp4 (89.95 MB)
65 13 4 Loss Functions for Regression & Classification.mp4 (87.93 MB)
66 13 5 Hands On Visualizing Activations & Loss.mp4 (80.45 MB)
67 14 1 Gradient Descent Intuition.mp4 (87.48 MB)
68 14 2 Backpropagation (Simplified).mp4 (112.38 MB)
69 14 3 Learning Rate & Convergence.mp4 (87.9 MB)
70 14 4 Optimizers SGD Momentum Adam.mp4 (99.91 MB)
71 15 1 Bias-Variance in Deep Learning.mp4 (59.71 MB)
72 15 2 Overfitting in Neural Networks.mp4 (81.57 MB)
73 15 3 L1 & L2 Regularization.mp4 (84.39 MB)
74 15 4 Dropout & Batch Normalization.mp4 (85.91 MB)
75 16 1 Tensors & Computation Graphs.mp4 (73.72 MB)
76 16 2 Building Networks Using Modules.mp4 (68.42 MB)
77 16 3 Training Loops & Evaluation.mp4 (84.17 MB)
78 16 4 GPU Acceleration Basics.mp4 (74.81 MB)
79 17 1 Why CNNs Beat Dense Networks.mp4 (80.77 MB)
80 17 2 Convolutions Filters & Feature Maps.mp4 (69.2 MB)
81 17 3 Pooling Layers Explained.mp4 (70.76 MB)
82 17 4 CNN Architecture Walkthrough.mp4 (77.27 MB)
83 18 1 Why Sequential Data Is Different.mp4 (75.48 MB)
84 18 2 Recurrent Neural Networks (RNNs).mp4 (82.02 MB)
85 18 3 LSTM & GRU Intuition.mp4 (68.52 MB)
86 18 4 Use Cases Time Series & Text.mp4 (66.64 MB)
87 19 1 Weight Initialization Strategies.mp4 (92.85 MB)
88 19 2 Debugging Deep Learning Models.mp4 (88.69 MB)
89 19 3 Monitoring Training & Validation Curves.mp4 (97.35 MB)
90 19 4 Reproducibility in Deep Learning.mp4 (98.88 MB)
91 19 5 Saving Loading & Versioning Models.mp4 (80.63 MB)
92 20 1 What Is Generative AI.mp4 (94.49 MB)
93 20 2 Evolution of Generative Models.mp4 (101.72 MB)
94 20 3 Generative AI Landscape.mp4 (111.2 MB)
95 21 1 Anatomy of Transformers.mp4 (99.01 MB)
96 21 2 Tokens Embeddings & Context Windows.mp4 (98.26 MB)
97 21 3 How LLMs Are Trained.mp4 (114.78 MB)
98 22 1 Popular LLM Families.mp4 (124.54 MB)
99 22 2 LLM Capabilities & Limitations.mp4 (104.56 MB)
100 22 3 Using LLM APIs.mp4 (95.96 MB)
101 23 1 Prompt Design Fundamentals.mp4 (93.08 MB)
102 23 2 Advanced Prompting Techniques.mp4 (116.76 MB)
103 23 3 Prompt Robustness & Safety.mp4 (103.76 MB)
104 24 1 What Are Embeddings.mp4 (107.9 MB)
105 24 2 Building Semantic Search Pipelines.mp4 (112.98 MB)
106 24 3 Vector Databases.mp4 (121.53 MB)
107 25 1 Why RAG is Needed.mp4 (100.14 MB)
108 25 2 RAG Architecture.mp4 (110.72 MB)
109 25 3 Advanced RAG Techniques.mp4 (89.81 MB)
110 26 1 Tool Using LLMs.mp4 (105.03 MB)
111 26 2 Designing Tools for LLMs.mp4 (91.88 MB)
112 26 3 Multi Step Reasoning with Tools.mp4 (93.8 MB)
113 27 1 What Are AI Agents.mp4 (102.41 MB)
114 27 2 Agent Architectures.mp4 (107.74 MB)
115 27 3 Building Practical Agents.mp4 (107.43 MB)
116 28 1 Backend Architecture for LLM Apps.mp4 (112.91 MB)
117 28 2 Frontend → LLM Integration.mp4 (108.94 MB)
118 28 3 State Memory & Context Management.mp4 (116.09 MB)
119 29 1 Evaluating LLM Outputs.mp4 (106.78 MB)
120 29 2 Cost Optimization.mp4 (112.4 MB)
121 29 3 Latency & Scaling Considerations.mp4 (114.66 MB)
122 30 1 Ethical Considerations in Generative AI.mp4 (88.79 MB)
123 30 2 Security Risks.mp4 (93.87 MB)
124 30 3 Guardrails & Governance.mp4 (104.18 MB)]
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