Code, Train, Deploy: The Ai Engineer’S Path To Success

Everything you need to know about AI Engineering - Hands-on from Algorithms, Programming to Real Projects

What you'll learn

Master Python for AI: Write efficient Python code, essential for AI and ML programming tasks.

Data Preprocessing Skills: Prepare, clean, and transform data to enhance model performance.

Statistical Knowledge: Apply core statistics to understand data patterns and inform decisions.

Build Machine Learning Models: Develop and fine-tune ML models for classification, regression, and clustering.

Deep Learning Proficiency: Design and train neural networks, including CNNs and RNNs, for image and sequence tasks.

Utilize Transfer Learning: Adapt pre-trained models to new tasks, saving time and resources.

Deploy ML Models with APIs: Create scalable APIs to serve ML models in real-world applications.

Containerize with Docker: Package models for portable deployment across environments.

Monitor and Maintain Models: Track model performance, detect drift, and implement retraining pipelines.

Complete ML Lifecycle: Master end-to-end AI project skills, from data to deployment and ongoing maintenance.

Requirements

Foundational Math Skills: Understanding of algebra and basic calculus concepts (derivatives, functions) for ML.

Interest in AI and ML: A passion for learning AI, machine learning, and data-driven technologies.

Laptop/Computer: A device capable of running data processing and ML libraries like TensorFlow, PyTorch, and Docker.

Curiosity and Perseverance: Willingness to solve problems, experiment with data, and work through challenges.

Description

Welcome to the AI Mastery Bootcamp, a comprehensive, hands-on program designed to transform beginners into skilled AI engineers. Over the course of 16 weeks, you will learn how to build, train, and deploy machine learning models, step by step, using the latest tools and techniques. This bootcamp focuses on practical skills, empowering you to apply artificial intelligence to solve real-world problems and create innovative solutions.The course starts with the fundamentals, covering essential topics like Python programming, data preprocessing, and an introduction to machine learning. As you progress, you’ll dive deeper into advanced concepts such as neural networks, deep learning, and natural language processing. You will also explore powerful AI frameworks like TensorFlow, PyTorch, and Hugging Face, which are essential for modern AI development.This bootcamp is ideal for anyone passionate about artificial intelligence, whether you’re starting from scratch or looking to deepen your expertise. You don’t need any prior experience with AI—just a willingness to learn and explore. By the end of the program, you’ll have the skills and confidence to build AI solutions from the ground up, making you ready to take on industry challenges or pursue advanced AI research.Join us on this exciting journey and become a part of the future of technology!

Overview

Section 1: Week 1: Python Programming Basics

Lecture 1 Introduction to Week 1 Python Programming Basics

Lecture 2 Day 1: Introduction to Python and Development Setup

Lecture 3 Day 2: Control Flow in Python

Lecture 4 Day 3: Functions and Modules

Lecture 5 Day 4: Data Structures (Lists, Tuples, Dictionaries, Sets)

Lecture 6 Day 5: Working with Strings

Lecture 7 Day 6: File Handling

Lecture 8 Day 7: Pythonic Code and Project Work

Section 2: Week 2: Data Science Essentials

Lecture 9 Introduction to Week 2 Data Science Essentials

Lecture 10 Day 1: Introduction to NumPy for Numerical Computing

Lecture 11 Day 2: Advanced NumPy Operations

Lecture 12 Day 3: Introduction to Pandas for Data Manipulation

Lecture 13 Day 4: Data Cleaning and Preparation with Pandas

Lecture 14 Day 5: Data Aggregation and Grouping in Pandas

Lecture 15 Day 6: Data Visualization with Matplotlib and Seaborn

Lecture 16 Day 7: Exploratory Data Analysis (EDA) Project

Section 3: Week 3: Mathematics for Machine Learning

Lecture 17 Introduction to Week 3 Mathematics for Machine Learning

Lecture 18 Day 1: Linear Algebra Fundamentals

Lecture 19 Day 2: Advanced Linear Algebra Concepts

Lecture 20 Day 3: Calculus for Machine Learning (Derivatives)

Lecture 21 Day 4: Calculus for Machine Learning (Integrals and Optimization)

Lecture 22 Day 5: Probability Theory and Distributions

Lecture 23 Day 6: Statistics Fundamentals

Lecture 24 Day 7: Math-Driven Mini Project – Linear Regression from Scratch

Section 4: Week 4: Probability and Statistics for Machine Learning

Lecture 25 Introduction to Week 4 Probability and Statistics for Machine Learning

Lecture 26 Day 1: Probability Theory and Random Variables

Lecture 27 Day 2: Probability Distributions in Machine Learning

Lecture 28 Day 3: Statistical Inference - Estimation and Confidence Intervals

Lecture 29 Day 4: Hypothesis Testing and P-Values

Lecture 30 Day 5: Types of Hypothesis Tests

Lecture 31 Day 6: Correlation and Regression Analysis

Lecture 32 Day 7: Statistical Analysis Project – Analyzing Real-World Data

Section 5: Week 5: Introduction to Machine Learning

Lecture 33 Introduction to Week 5 Introduction to Machine Learning

Lecture 34 Day 1: Machine Learning Basics and Terminology

Lecture 35 Day 2: Introduction to Supervised Learning and Regression Models

Lecture 36 Day 3: Advanced Regression Models – Polynomial Regression and Regularization

Lecture 37 Day 4: Introduction to Classification and Logistic Regression

Lecture 38 Day 5: Model Evaluation and Cross-Validation

Lecture 39 Day 6: k-Nearest Neighbors (k-NN) Algorithm

Lecture 40 Day 7: Supervised Learning Mini Project

Section 6: Week 6: Feature Engineering and Model Evaluation

Lecture 41 Introduction to Week 6 Feature Engineering and Model Evaluation

Lecture 42 Day 1: Introduction to Feature Engineering

Lecture 43 Day 2: Data Scaling and Normalization

Lecture 44 Day 3: Encoding Categorical Variables

Lecture 45 Day 4: Feature Selection Techniques

Lecture 46 Day 5: Creating and Transforming Features

Lecture 47 Day 6: Model Evaluation Techniques

Lecture 48 Day 7: Cross-Validation and Hyperparameter Tuning

Section 7: Week 7: Advanced Machine Learning Algorithms

Lecture 49 Introduction to Week 7 Advanced Machine Learning Algorithms

Lecture 50 Day 1: Introduction to Ensemble Learning

Lecture 51 Day 2: Bagging and Random Forests

Lecture 52 Day 3: Boosting and Gradient Boosting

Lecture 53 Day 4: Introduction to XGBoost

Lecture 54 Day 5: LightGBM and CatBoost

Lecture 55 Day 6: Handling Imbalanced Data

Lecture 56 Day 7: Ensemble Learning Project – Comparing Models on a Real Dataset

Section 8: Week 8: Model Tuning and Optimization

Lecture 57 Introduction to Week 8 Model Tuning and Optimization

Lecture 58 Day 1: Introduction to Hyperparameter Tuning

Lecture 59 Day 2: Grid Search and Random Search

Lecture 60 Day 3: Advanced Hyperparameter Tuning with Bayesian Optimization

Lecture 61 Day 4: Regularization Techniques for Model Optimization

Lecture 62 Day 5: Cross-Validation and Model Evaluation Techniques

Lecture 63 Day 6: Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV

Lecture 64 Day 7: Optimization Project – Building and Tuning a Final Model

Section 9: Week 9: Neural Networks and Deep Learning Fundamentals

Lecture 65 Introduction to Week 9 Neural Networks and Deep Learning Fundamentals

Lecture 66 Day 1: Introduction to Deep Learning and Neural Networks

Lecture 67 Day 2: Forward Propagation and Activation Functions

Lecture 68 Day 3: Loss Functions and Backpropagation

Lecture 69 Day 4: Gradient Descent and Optimization Techniques

Lecture 70 Day 5: Building Neural Networks with TensorFlow and Keras

Lecture 71 Day 6: Building Neural Networks with PyTorch

Lecture 72 Day 7: Neural Network Project – Image Classification on CIFAR-10

Section 10: Week 10: Convolutional Neural Networks (CNNs)

Lecture 73 Introduction to Week 10 Convolutional Neural Networks (CNNs)

Lecture 74 Day 1: Introduction to Convolutional Neural Networks

Lecture 75 Day 2: Convolutional Layers and Filters

Lecture 76 Day 3: Pooling Layers and Dimensionality Reduction

Lecture 77 Day 4: Building CNN Architectures with Keras and TensorFlow

Lecture 78 Day 5: Building CNN Architectures with PyTorch

Lecture 79 Day 6: Regularization and Data Augmentation for CNNs

Lecture 80 Day 7: CNN Project – Image Classification on Fashion MNIST or CIFAR-10

Section 11: Week 11: Recurrent Neural Networks (RNNs) and Sequence Modeling

Lecture 81 Introduction to Week 11 Recurrent Neural Networks (RNNs) and Sequence Modeling

Lecture 82 Day 1: Introduction to Sequence Modeling and RNNs

Lecture 83 Day 2: Understanding RNN Architecture and Backpropagation Through Time (BPTT)

Lecture 84 Day 3: Long Short-Term Memory (LSTM) Networks

Lecture 85 Day 4: Gated Recurrent Units (GRUs)

Lecture 86 Day 5: Text Preprocessing and Word Embeddings for RNNs

Lecture 87 Day 6: Sequence-to-Sequence Models and Applications

Lecture 88 Day 7: RNN Project – Text Generation or Sentiment Analysis

Section 12: Week 12: Transformers and Attention Mechanisms

Lecture 89 Introduction to Week 12 Transformers and Attention Mechanisms

Lecture 90 Day 1: Introduction to Attention Mechanisms

Lecture 91 Day 2: Introduction to Transformers Architecture

Lecture 92 Day 3: Self-Attention and Multi-Head Attention in Transformers

Lecture 93 Day 4: Positional Encoding and Feed-Forward Networks

Lecture 94 Day 5: Hands-On with Pre-Trained Transformers – BERT and GPT

Lecture 95 Day 6: Advanced Transformers – BERT Variants and GPT-3

Lecture 96 Day 7: Transformer Project – Text Summarization or Translation

Section 13: Week 13: Transfer Learning and Fine-Tuning

Lecture 97 Introduction to Week 13 Transfer Learning and Fine-Tuning

Lecture 98 Day 1: Introduction to Transfer Learning

Lecture 99 Day 2: Transfer Learning in Computer Vision

Lecture 100 Day 3: Fine-Tuning Techniques in Computer Vision

Lecture 101 Day 4: Transfer Learning in NLP

Lecture 102 Day 5: Fine-Tuning Techniques in NLP

Lecture 103 Day 6: Domain Adaptation and Transfer Learning Challenges

Lecture 104 Day 7: Transfer Learning Project – Fine-Tuning for a Custom Task

Section 14: Machine Learning Algorithms and Implementations

Lecture 105 Whats Next???

Lecture 106 Introduction to Machine Learning Algorithms

Lecture 107 Linear Regression Implementation in Python

Lecture 108 Ridge and Lasso Regression Implementation in Python

Lecture 109 Polynomial Regression Implementation in Python

Lecture 110 Logistic Regression Implementation in Python

Lecture 111 K-Nearest Neighbors (KNN) Implementation in Python

Lecture 112 Support Vector Machines (SVM) Implementation in Python

Lecture 113 Decision Trees Implementation in Python

Lecture 114 Random Forests Implementation in Python

Lecture 115 Gradient Boosting Implementation in Python

Lecture 116 Naive Bayes Implementation in Python

Lecture 117 K-Means Clustering Implementation in Python

Lecture 118 Hierarchical Clustering Implementation in Python

Lecture 119 DBSCAN (Density-Based Spatial Clustering of Applications w Noise) Implementation

Lecture 120 Gaussian Mixture Models(GMM) Implementation in Python

Lecture 121 Principal Component Analysis (PCA) Implementation in Python

Lecture 122 t-Distributed Stochastic Neighbor Embedding (t-SNE) Implementation in Python

Lecture 123 Autoencoders Implementation in Python

Lecture 124 Self-Training Implementation in Python

Lecture 125 Q-Learning Implementation in Python

Lecture 126 Deep Q-Networks (DQN) Implementation in Python

Lecture 127 Policy Gradient Methods Implementation in Python

Lecture 128 One-Class SVM Implementation in Python

Lecture 129 Isolation Forest Implementation in Python

Lecture 130 Convolutional Neural Networks (CNNs) Implementation in Python

Lecture 131 Recurrent Neural Networks (RNNs) Implementation in Python

Lecture 132 Long Short-Term Memory (LSTM) Implementation in Python

Lecture 133 Transformers Implementation in Python

Section 15: Introduction to Machine Learning and TensorFlow

Lecture 134 What is Machine Learning?

Lecture 135 Introduction to TensorFlow

Lecture 136 TensorFlow vs. Other Machine Learning frameworks

Lecture 137 Installing TensorFlow

Lecture 138 Setting up your Development Environment

Lecture 139 Verifying the Installation

Section 16: Basics of TensorFlow

Lecture 140 Introduction to Tensors

Lecture 141 Tensor Operations

Lecture 142 Constants, Variables, and Placeholders

Lecture 143 TensorFlow Computational Graph

Lecture 144 Creating and Running a TensorFlow Session

Lecture 145 Managing Graphs and Sessions

Lecture 146 Building a Simple Feedforward Neural Network

Lecture 147 Activation Functions

Lecture 148 Loss Functions and Optimizers

Section 17: Intermediate TensorFlow

Lecture 149 Introduction to Keras API

Lecture 150 Building Complex Models with Keras

Lecture 151 Training and Evaluating Models

Lecture 152 Introduction to CNNs

Lecture 153 Building and Training CNNs with TensorFlow

Lecture 154 Transfer Learning with Pre-trained CNNs

Lecture 155 Introduction to RNNs

Lecture 156 Building and Training RNNs with TensorFlow

Lecture 157 Applications of RNNs: Language Modeling, Time Series Prediction

Section 18: Advanced TensorFlow

Lecture 158 Saving and Loading Models

Lecture 159 TensorFlow Serving for Model Deployment

Lecture 160 TensorFlow Lite for Mobile and Embedded Devices

Lecture 161 Introduction to Distributed Computing with TensorFlow

Lecture 162 TensorFlow's Distributed Execution Framework

Lecture 163 Scaling TensorFlow with TensorFlow Serving and Kubernetes

Lecture 164 Introduction to TFX

Lecture 165 Building End-to-End ML Pipelines with TFX

Lecture 166 Model Validation, Transform, and Serving with TFX

Section 19: Practical Applications and Projects

Lecture 167 Image Classification

Lecture 168 Natural Language Processing

Lecture 169 Recommender Systems

Lecture 170 Object Detection

Lecture 171 Building a Sentiment Analysis Model

Lecture 172 Creating an Image Recognition System

Lecture 173 Developing a Time Series Prediction Model

Lecture 174 Implementing a Chatbot

Section 20: Further Learning and Resources in TensorFlow

Lecture 175 Generative Adversarial Networks (GANs)

Lecture 176 Reinforcement Learning with TensorFlow

Lecture 177 Quantum Machine Learning with TensorFlow Quantum

Lecture 178 TensorFlow Documentation and Tutorials

Lecture 179 Online Courses and Books

Lecture 180 TensorFlow Community and Forums

Lecture 181 Summary of Key Concepts

Lecture 182 Next Steps in Your TensorFlow Journey

Section 21: Introduction to Learning PyTorch from Basics to Advanced

Lecture 183 1. Introduction to PyTorch

Lecture 184 2. Getting Started with PyTorch

Lecture 185 3. Working with Tensors

Lecture 186 4. Autograd and Dynamic Computation Graphs

Lecture 187 5. Building Simple Neural Networks

Lecture 188 6. Loading and Preprocessing Data

Lecture 189 7. Model Evaluation and Validation

Lecture 190 8. Advanced Neural Network Architectures

Lecture 191 9. Transfer Learning and Fine-Tuning

Lecture 192 10. Handling Complex Data

Lecture 193 11. Model Deployment and Production

Lecture 194 12. Debugging and Troubleshooting

Lecture 195 13. Distributed Training and Performance Optimization

Lecture 196 14. Custom Layers and Loss Functions

Lecture 197 15. Research-oriented Techniques

Lecture 198 16. Integration with Other Libraries

Lecture 199 17. Contributing to PyTorch and Community Engagement

Section 22: LangChain for Beginners

Lecture 200 1. Introduction to LangChain and Language Models

Lecture 201 2. Project 1: Simple Text-Based Question Answering Bot

Lecture 202 3. Project 2: Sentiment Analysis with LangChain

Lecture 203 4. Project 3: Document Summarization Tool

Lecture 204 5. Project 4: Keyword Extraction from Text

Lecture 205 6. Project 5: LangChain-Powered Chatbot

Section 23: AI Agents for Dummies

Lecture 206 Part 1.1: Understanding AI Agents - How AI Agents Function

Lecture 207 Part 1.2: - Introduction to AI Agents

Lecture 208 Part 1.3: Types of AI Agents

Lecture 209 Part 2.1: Technologies Behind AI Agents - Machine Learning and AI Agents

Lecture 210 Part 2.2: Natural Language Processing in AI Agents

Lecture 211 Part 2.3: AI Agents in Robotics

Lecture 212 Part 3.1: AI Agent Frameworks & Architectures - AI Agent Development Frameworks

Lecture 213 Part 3.2: Overview of AutoGPT for AI Agents

Lecture 214 Part 3.3: IBM Bee Framework for AI Agents

Lecture 215 Part 3.4: LangGraph for Stateful AI Agents

Lecture 216 Part 3.5: CrewAI for Collaborative AI Agents

Lecture 217 Part 4.1: Applications of AI Agents - AI Agents in Business Operations

Lecture 218 Part 4.2: AI Agents in Healthcare

Lecture 219 Part 4.3: AI Agents in Financial Systems

Lecture 220 Part 4.4: AI Agents in Entertainment

Lecture 221 Part 4.5: AI Agents in Smart Homes and IoT

Lecture 222 Part 5.1: Future Trends and Ethical Implications - The Future of AI Agents

Lecture 223 Part 5.2: Ethics in AI Agent Development

Lecture 224 Part 5.3: Legal and Regulatory Challenges for AI Agents

Lecture 225 Part 6.1: Broader Impact of AI Agents - Social and Economic Impacts of AI Agents

Lecture 226 Part 6.2: AI Agents and Human Collaboration

Lecture 227 Part 6.3: The Role of AI Agents in Scientific Research

Lecture 228 Part 6.4: AI Agents in Public Safety and National Defense

Section 24: AI Agents: A Comprehensive Overview

Lecture 229 1. Hands-on AutoGen | IBM Bee | LangGraph | CrewAI | AutoGPT

Lecture 230 2. Hands-on AutoGen

Lecture 231 3. Hands-on IBM Bee Framework

Lecture 232 4. Hands-on LangGraph

Lecture 233 5. Hands-on CrewAI

Lecture 234 6. Hands-on AutoGPT

Section 25: Miscellaneous Projects on AI for Daily Practice

Lecture 235 Day 1: Basic Calculator using Python

Lecture 236 Day 2: Image Classifier using Keras and TensorFlow

Lecture 237 Day 3: Simple Chatbot using predefined responses

Lecture 238 Day 4: Spam Email Detector using Scikit-learn

Lecture 239 Day 5: Handwritten Digit Recognition with MNIST dataset

Lecture 240 Day 6: Sentiment Analysis on text data using NLTK

Lecture 241 Day 7: Movie Recommendation System using cosine similarity

Lecture 242 Day 8: Predict House Prices with Linear Regression

Lecture 243 Day 9: Weather Forecasting using historical data

Lecture 244 Day 10: Basic Neural Network from scratch

Lecture 245 Day 11: Stock Price Prediction using historical data w/ simple Linear Regression

Lecture 246 Day 12: Predict Diabetes using logistic regression

Lecture 247 Day 13: Dog vs. Cat Classifier with CNN

Lecture 248 Day 14: Tic-Tac-Toe AI using Minimax Algorithm

Lecture 249 Day 15: Credit Card Fraud Detection using Scikit-learn

Lecture 250 Day 16: Iris Flower Classification using decision trees

Lecture 251 Day 17: Simple Personal Assistant using Python speech libraries

Lecture 252 Day 18: Text Summarizer using Gensim

Lecture 253 Day 19: Fake Product Review Detection using NLP techniques

Lecture 254 Day 20: Detect Emotion in Text using Natural Language Toolkit (NLTK)

Lecture 255 Day 21: Book Recommendation System using collaborative filtering

Lecture 256 Day 22: Predict Car Prices using Random Forest

Lecture 257 Day 23: Identify Fake News using Naive Bayes

Lecture 258 Day 24: Create a Resume Scanner using keyword extraction

Lecture 259 Day 25: Customer Churn Prediction using classification algorithms

Aspiring AI Engineers: Those looking to build a career in AI and gain hands-on, production-ready skills.,Data Scientists and Analysts: Professionals who want to expand their expertise to include machine learning, deep learning, and model deployment.,Software Engineers: Developers interested in applying programming skills to AI and machine learning projects.,Career Changers: Individuals from non-technical backgrounds with foundational Python knowledge, eager to transition into AI.,Graduate Students: Students in data science, computer science, or related fields wanting a practical, job-ready experience in AI engineering.,Tech Entrepreneurs: Founders and CTOs interested in understanding AI for building AI-driven products or managing AI teams.