Become a certified Machine Learning and Deep Learning professional with Nexskill's comprehensive Advance Machine Learning & Deep Learning course in Lahore. Master Python, Data Science, Machine Learning, PyTorch, NLP, Transformers, Generative AI, Large Language Models and Computer Vision through practical, industry-focused training.
Course Flow or Roadmap
Kickstart your career in artificial intelligence with Nexskill's Advance Machine Learning & Deep Learning course. This comprehensive 16-week program provides hands-on experience in building, training, evaluating, fine-tuning and deploying intelligent AI solutions. Starting with Python programming and data science fundamentals, you will progress through mathematical foundations, classical machine learning, neural networks, PyTorch, NLP, Transformers, Generative AI, LLMs and computer vision. Learn to work with industry-relevant tools such as NumPy, Pandas, Scikit-learn, XGBoost, PyTorch, Hugging Face, LangChain and FastAPI. With 48 practical classes, real-world projects and a final capstone, this program prepares you to develop and deploy modern AI applications.
Module Topics
A Machine Learning and Deep Learning Engineer designs, develops and deploys intelligent systems that learn from data and solve real-world problems. These professionals work with Python, statistical methods, machine learning algorithms and neural networks to build predictive models and AI-powered applications. They use frameworks such as Scikit-learn and PyTorch to train and optimize models, while applying NLP and Transformer architectures to language-related tasks. Modern AI engineers also work with Large Language Models, fine-tuning, Retrieval-Augmented Generation, AI agents and computer vision technologies. Their responsibilities include data preparation, model evaluation, deployment, performance optimization and maintaining reliable AI solutions in production environments.
Course Curriculum Overview
1Module 1 — Python for AI: Python Core Fundamentals, OOP, Advanced Python, Async Programming, APIs and Git Project Setup
2Module 2 — Data Science Toolkit & Math for ML: NumPy, Pandas, Data Visualization, Exploratory Data Analysis, Linear Algebra, Probability, Statistics and Calculus
3Module 3 — Machine Learning & Model Deployment: Regression, Classification, Decision Trees, Ensembles, Clustering, Feature Engineering, Model Evaluation and Deployment
4Module 4 — Deep Learning & PyTorch: Neural Networks, Backpropagation, Optimizers, Training Stability, PyTorch, GPU Acceleration and Sequence Models
5Module 5 — NLP & Transformers: Text Processing, Embeddings, NLP Tasks, Attention, Transformers, Hugging Face and Semantic Search
6Module 6 — Generative AI & Large Language Models: LLM Foundations, Prompt Engineering, Fine-Tuning, LoRA, Quantization, RAG, AI Agents and Speech AI
7Module 7 — Computer Vision, Generative Vision & Capstone: CNNs, Transfer Learning, Vision Transformers, CLIP, Diffusion Models, Multimodal AI and Final Capstone
Course Duration & Mode
Duration: 16 Weeks
Total Classes: 48
Weekly Schedule: 3 Classes Per Week
Modes: Online & Physical
Campuses: Available at Nexskill campuses
Sessions: Weekday & Weekend Batches Available
Comprehensive Course Modules
1Module 1 — Python for AI: Python Core Fundamentals, OOP, Advanced Python, Async Programming, APIs and Git Project Setup
Module 1
2Module 2 — Data Science Toolkit & Math for ML: NumPy, Pandas, Data Visualization, Exploratory Data Analysis, Linear Algebra, Probability, Statistics and Calculus
Module 2
3Module 3 — Machine Learning & Model Deployment: Regression, Classification, Decision Trees, Ensembles, Clustering, Feature Engineering, Model Evaluation and Deployment
Module 3
4Module 4 — Deep Learning & PyTorch: Neural Networks, Backpropagation, Optimizers, Training Stability, PyTorch, GPU Acceleration and Sequence Models
Module 4
5Module 5 — NLP & Transformers: Text Processing, Embeddings, NLP Tasks, Attention, Transformers, Hugging Face and Semantic Search
Module 5
6Module 6 — Generative AI & Large Language Models: LLM Foundations, Prompt Engineering, Fine-Tuning, LoRA, Quantization, RAG, AI Agents and Speech AI
Module 6
7Module 7 — Computer Vision, Generative Vision & Capstone: CNNs, Transfer Learning, Vision Transformers, CLIP, Diffusion Models, Multimodal AI and Final Capstone
Module 7
Who Should Attend?
Nexskill offers the Advance Machine Learning & Deep Learning program with flexible enrollment and payment options. Contact us for the latest fee structure, available batches and installment plans. Enroll online or visit your nearest campus to begin your journey into artificial intelligence, machine learning and deep learning. Scholarships and discounts may be available for eligible students.
Detailed Weekly Learning Schedule
📅 Schedule: 2 Interactive Sessions Per Week
Total Lectures: 48 | Duration: 2 hours each
Module 1 — Python Core Fundamentals
Weeks 1–2Learning Objectives:
- •Class 1 — Setup & Syntax: Installing Python and VS Code, running scripts, variables and data types, arithmetic, comparison, logical and bitwise operators, input and output.
- •Class 2 — Control Flow: if-elif-else conditions, for and while loops, break and continue, nested logic and logic-building exercises.
- •Class 3 — Functions: Defining functions, arguments and return values, *args and **kwargs, default parameters, lambda functions and variable scope.
- •Class 4 — Data Structures: Lists, tuples, sets and dictionaries in depth, list, dictionary and set comprehensions, choosing the right structure.
- •Class 5 — Strings, Files & Errors: String manipulation, regex parsing, file handling with context managers and exception handling using try-except-finally.
- •Class 6 — Modules & Environments: Modules, packages and imports, virtual environments (venv, conda, poetry), pip and dependency locking.
Module 1 — OOP, Advanced Python & APIs
Weeks 3–4Learning Objectives:
- •Class 7 — OOP Foundations: Classes and objects, constructors, inheritance (single, multiple, MRO), encapsulation and private attributes.
- •Class 8 — OOP Advanced: Polymorphism and duck typing, abstract base classes, dataclasses and immutability, type hints and static checking.
- •Class 9 — Advanced Mechanics: Iterators and the iterable protocol, generators and yield, decorators with state and custom context managers.
- •Class 10 — Async Python: asyncio event loop, async/await patterns, tasks, gather and semaphores, non-blocking API calls.
- •Class 11 — APIs & Your First LLM Call: HTTP/REST fundamentals, JSON serialization, environment variables (.env) and calling the Claude API from Python.
- •Class 12 — Git & Project Setup: Git/GitHub workflow, Python project structure and packaging, mini project: a command-line AI assistant.
Module 2 — Data Science Toolkit & Math for ML
Weeks 5–6Learning Objectives:
- •Class 13 — NumPy: Arrays and tensors, vectorized operations, broadcasting, indexing and slicing, random sampling.
- •Class 14 — Pandas: DataFrames, loading CSV and Excel data, cleaning and filtering, groupby and merges, handling missing values.
- •Class 15 — Visualization & EDA: Matplotlib and Seaborn, distributions, correlations and outliers, an exploratory data analysis workflow in Google Colab.
- •Class 16 — Linear Algebra: Scalars, vectors, matrices and tensors, dot product, matrix multiplication, vector norms, eigenvalues and eigenvectors.
- •Class 17 — Probability & Statistics: Conditional probability, Bayes theorem, distributions, mean, variance and correlation, hypothesis testing and p-values.
- •Class 18 — Calculus & AI-Assisted Data Science: Derivatives, gradients and the chain rule, convexity, using Claude Code and Cursor for data analysis notebooks.
Module 3 — Machine Learning & Model Deployment
Weeks 7–8Learning Objectives:
- •Class 19 — ML Foundations & Regression: Supervised vs unsupervised learning, the ML lifecycle, linear and polynomial regression, MAE, MSE, RMSE and R².
- •Class 20 — Classification: Logistic regression, KNN, Naive Bayes, SVM, confusion matrix, precision, recall, F1 and ROC-AUC.
- •Class 21 — Trees & Ensembles: Decision trees, random forest and bagging, gradient boosting with XGBoost, LightGBM and CatBoost.
- •Class 22 — Clustering & Dimensionality Reduction: K-Means, hierarchical clustering, DBSCAN, PCA and a customer segmentation project.
- •Class 23 — Feature Engineering & Validation: Encoding and scaling, missing values and outliers, data leakage prevention, K-fold and stratified cross validation.
- •Class 24 — Tuning, Pipelines & Deployment: Hyperparameter tuning, L1/L2 regularization, Scikit-learn pipelines, serving models with FastAPI and Docker.
Module 4 — Deep Learning & PyTorch
Weeks 9–10Learning Objectives:
- •Class 25 — Neural Network Basics: Artificial neurons, perceptrons, forward propagation and loss functions (MSE, cross-entropy).
- •Class 26 — Backpropagation & Optimizers: Computation graphs, gradient descent and SGD, momentum, Adam and AdamW, learning rate scheduling.
- •Class 27 — Training Stability: Activation functions (ReLU, GELU, softmax), Xavier and He initialization, batch and layer normalization, dropout.
- •Class 28 — PyTorch Fundamentals: Tensors and autograd, building models with nn.Module, Dataset and DataLoader, training and validation loops.
- •Class 29 — PyTorch in Practice: GPU acceleration (CUDA), mixed precision training, checkpointing and experiment tracking.
- •Class 30 — Sequence Models: RNNs, LSTM and GRU, Seq2Seq encoder-decoder models and the road to attention.
Module 5 — NLP & Transformers
Weeks 11–12Learning Objectives:
- •Class 31 — Text Processing & Embeddings: Tokenization, stemming and lemmatization, TF-IDF, Word2Vec and GloVe, contextual embeddings.
- •Class 32 — NLP Tasks: Text classification, sentiment analysis, named entity recognition, intent recognition and topic modeling.
- •Class 33 — Attention & Transformers: Scaled dot-product and multi-head attention, positional encoding, encoder vs decoder models (BERT vs GPT).
- •Class 34 — Tokenization Deep Dive: BPE, WordPiece and SentencePiece, byte-level tokenizers (tiktoken, LLaMA), special tokens and vocabularies.
- •Class 35 — Hugging Face Transformers: Transformers and Datasets libraries, pipelines, pre-trained models, fine-tuning DistilBERT for classification.
- •Class 36 — Sentence Embeddings & Semantic Search: Sentence Transformers, cosine similarity, vector databases (FAISS, Qdrant), building a semantic search engine.
Module 6 — Generative AI & Large Language Models
Weeks 13–14Learning Objectives:
- •Class 37 — Generative AI & LLM Foundations: How LLMs work, pre-training vs post-training, GPT, LLaMA and Claude model families, KV cache and context windows.
- •Class 38 — Prompt Engineering & LLM APIs: Zero and few-shot prompting, chain-of-thought, structured outputs with Pydantic and Instructor, Claude, OpenAI and Gemini APIs.
- •Class 39 — Fine-Tuning LLMs: Instruction datasets and chat templates, LoRA and QLoRA, supervised fine-tuning with Hugging Face TRL and Unsloth.
- •Class 40 — Alignment, Quantization & Serving: RLHF and DPO, GPTQ, AWQ and GGUF quantization, running models locally with Ollama and serving with vLLM.
- •Class 41 — RAG Systems: Chunking and embeddings, LangChain and LlamaIndex, hybrid search and reranking, RAG evaluation with RAGAS.
- •Class 42 — AI Agents & Speech AI: Tool calling, ReAct agents with LangGraph, MCP basics and speech-to-text with Whisper.
Module 7 — Computer Vision, Generative Vision & Capstone
Weeks 15–16Learning Objectives:
- •Class 43 — CNNs & Transfer Learning: Convolutions, pooling, ResNet and EfficientNet, data augmentation and image classification pipelines.
- •Class 44 — Vision Transformers & CLIP: ViT patch embeddings, CNN vs ViT, CLIP image-text models, vision APIs with Claude and Gemini.
- •Class 45 — Generative Vision: Diffusion models and Stable Diffusion, vision-language models (LLaVA, Qwen-VL), multimodal applications.
- •Class 46 — Deploying ML, DL & LLM Apps: FastAPI and Docker, Gradio and Streamlit demos, Hugging Face Spaces, monitoring cost and latency.
- •Class 47 — Capstone Build: Build and deploy a complete ML, deep learning or LLM solution for a real problem, with mentor reviews along the way.
- •Class 48 — Capstone Demo & Certification: Live demo, case study report and GitHub portfolio, practical assessment, Advance Machine Learning & Deep Learning Certificate award.
Learning Outcomes
Enroll Now — Online & In-Campus Batches Available! Build practical expertise in Python, machine learning, neural networks, PyTorch, NLP, Transformers, Generative AI and computer vision. Develop real-world AI projects, create a professional GitHub portfolio and take the next step toward a career in artificial intelligence.
Why Choose Nexskill?
Perfect For These Professionals
Students interested in artificial intelligence and machine learning
Beginners who want to build a career in AI and data science
Python developers looking to specialize in machine learning
Software engineers exploring deep learning and AI applications
Data analysts interested in predictive modeling and advanced analytics
Professionals looking to develop NLP and Generative AI skills
Freelancers exploring machine learning and AI development opportunities
Tech enthusiasts interested in computer vision and intelligent applications
Course Fee & Enrollment
Skills You'll Master
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Master Python programming and work confidently with NumPy, Pandas and data visualization.
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Understand the mathematics behind machine learning, including linear algebra, probability, statistics and calculus.
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Build, evaluate, tune and deploy classical machine learning models using Scikit-learn and XGBoost.
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Build and train neural networks and sequence models using PyTorch.
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Develop NLP systems using Transformers, Hugging Face and semantic search.
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Understand attention mechanisms and modern Transformer architectures.
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Prompt, fine-tune, quantize and serve Large Language Models.
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Build RAG systems, AI agents and speech AI applications.
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Develop computer vision and generative vision applications.
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Deploy AI solutions using FastAPI, Docker, Gradio and Streamlit.
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Complete a real-world AI capstone and earn the Advance Machine Learning & Deep Learning Certificate.
Ready to Become a Machine Learning & Deep Learning Expert?
Why Students Choose Nexskill
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Industry-focused curriculum covering machine learning, deep learning and Generative AI.
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Practical learning through real-world AI projects and hands-on exercises.
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Experienced instructors and mentor guidance throughout the program.
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Training with modern frameworks including PyTorch, Scikit-learn and Hugging Face.
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Hands-on experience with LLMs, RAG, AI agents and computer vision.
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Capstone development with mentor reviews and practical assessment.
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Professional GitHub portfolio and project documentation.
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Career guidance for AI, machine learning and data science roles.
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Flexible online and physical learning options.
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Freelancing and professional development guidance.
