AI Enginner 2026 Complete Course, GEN AI, Deep, Machine, LLM

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Free Download AI Enginner 2026 Complete Course, GEN AI, Deep, Machine, LLM
Published 1/2026
Created by Data Science Academy, School of AI
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 124 Lectures ( 18h 36m ) | Size: 12.6 GB

Master Machine Learning, Deep Learning, LLMs & AI Systems with hands-on, real-world projects
What you'll learn
✓ Build and evaluate Machine Learning models using regression, classification, clustering, and ensemble techniques with proper validation and optimization.
✓ Design, train, and debug Deep Learning models including fully connected networks, CNNs, and sequence models (RNNs, LSTMs, GRUs) using PyTorch or TensorFlow.
✓ Understand and implement Transformer-based Large Language Models (LLMs), including attention, embeddings, tokenization, and fine-tuning concepts.
✓ Create production-ready Generative AI applications using prompt engineering, embeddings, semantic search, and Retrieval-Augmented Generation (RAG).
✓ Develop agentic AI systems that perform multi-step reasoning, tool calling, and task execution with memory and control mechanisms.
✓ Apply AI engineering best practices such as feature engineering, model optimization, reproducibility, cost control, evaluation, and performance tuning.
✓ Integrate AI models into real applications by designing full-stack architectures that connect backends, APIs, and user interfaces with AI systems
Requirements
● Basic Python programming knowledge
● Curiosity to understand how AI works under the hood
● No prior experience in Machine Learning, Deep Learning, or Generative AI is required - everything is explained from first principles to production.
Description
"This course contains the use of artificial intelligence"
Artificial Intelligence is no longer about experimenting with isolated models or learning algorithms in theory. In 2026, companies are hiring AI Engineers who can work across the entire stack, from data understanding and machine learning to deep learning systems and Generative AI applications. If your goal is to land an AI Engineer job in 2026, this course is built for you.
This course is a complete Full-Stack AI Engineer program that brings together Machine Learning, Deep Learning, and Generative AI into one structured, end-to-end learning path. Instead of fragmented knowledge, you will gain a unified understanding of how modern AI systems are designed, trained, optimized, and deployed in real-world environments. Every concept in this course is taught with a strong focus on practical application, engineering mindset, and production readiness.
You will begin by building a solid foundation in Python for AI, data manipulation, and exploratory data analysis, learning how to understand data before modeling it. You will then move into core machine learning concepts, where you will work with regression, classification, ensemble methods, and unsupervised learning, while understanding critical ideas such as bias-variance tradeoff, model evaluation, feature engineering, and hyperparameter tuning. These skills form the backbone of real AI systems and are essential for any AI Engineer role.
As the course progresses, you will transition into Deep Learning, where you will learn how neural networks actually work under the hood. You will understand forward propagation, backpropagation, gradient descent, activation functions, and loss functions, and then implement these ideas using PyTorch or TensorFlow. You will build deep neural networks, work with convolutional neural networks for computer vision, and apply sequence models such as RNNs, LSTMs, and GRUs for time-series and text-based problems. You will also learn deep learning engineering best practices, including regularization, monitoring training behavior, reproducibility, and model versioning.
The course then takes you into the most in-demand area of AI today: Generative AI and Large Language Models. You will gain a clear understanding of transformer architecture, self-attention, embeddings, tokenization, and context windows, so you know how LLMs actually work rather than treating them as black boxes. You will learn how to work with modern models such as GPT, Claude, Gemini, and open-source LLMs, and understand their capabilities, limitations, cost considerations, and safety concerns.
You will also develop strong skills in Prompt Engineering, learning how to design prompts that are reliable, controllable, and robust, while avoiding common failure modes such as hallucinations and prompt injection. Beyond prompting, you will build embedding-based semantic search systems, implement Retrieval-Augmented Generation (RAG) pipelines to ground LLMs in real data, and design tool-calling and function-based LLM applications that interact with external systems.
Finally, you will explore Agentic AI systems, where models can plan, reason, use tools, and execute multi-step tasks. You will learn how modern AI agents are structured, how memory and state are managed, and how these systems are used in real products. You will also understand evaluation strategies, cost optimization, latency tradeoffs, security risks, and responsible AI practices, ensuring you can build systems that are not only powerful but also safe and scalable.
This course is designed for anyone serious about becoming an AI Engineer, including software engineers transitioning into AI, data professionals upgrading their skill set, and students preparing for AI-focused roles. No prior experience in machine learning or deep learning is required, as everything is taught from first principles to production-level understanding.
By the end of this course, you will not just understand AI concepts. You will be able to design, build, and reason about real AI systems with confidence. If your goal is to secure an AI Engineer role in 2026 and beyond, this course provides the skills, structure, and depth required to get there.
Who this course is for
■ Aspiring AI Engineers
■ Software Engineers transitioning into AI
■ Data Analysts & Data Scientists upgrading to AI systems
■ ML Engineers wanting Deep Learning and LLM expertise
■ Students & professionals preparing for AI-focused roles
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12.6 GB | 13min 12s | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

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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