What will you learn in Machine Learning Course
Implement end-to-end machine learning workflows using Python, Spark, and popular ML libraries.
Design, train, and evaluate models for regression, classification, clustering, and recommendation systems.
Build deep learning solutions with TensorFlow/Keras for NLP, computer vision, and sequence learning.
Apply advanced AI techniques—ensemble methods, reinforcement learning, and graphical models—to real-world problems.
Deploy scalable ML pipelines on cloud platforms, and solidify your expertise through capstone projects.
Program Overview
Module 1: Python & Statistics for Data Science
⏳ 20 hours
Topics: Python essentials, NumPy/Pandas, descriptive statistics, probability distributions
Hands-on: Clean and analyze a real dataset; perform statistical hypothesis tests
Module 2: Python Certification Training
⏳ 24 hours
Topics: Advanced Python constructs, OOP, file I/O, exception handling, modules
Hands-on: Develop automation scripts for data ingestion and preprocessing
Module 3: Python Machine Learning Certification
⏳ 30 hours
Topics: Scikit-learn APIs, supervised/unsupervised algorithms, model evaluation metrics
Hands-on: Build and fine-tune regression, classification, and clustering models
Module 4: Advanced Artificial Intelligence
⏳ 35 hours
Topics: Ensemble methods, advanced feature engineering, recommendation systems
Hands-on: Implement random forests, gradient boosting, and a simple recommender
Module 5: ChatGPT Complete Course
⏳ 8 hours
Topics: Large language models, prompt engineering, fine-tuning strategies
Hands-on: Build conversational agents and integrate them into simple applications
Module 6: PySpark Certification Training
⏳ 24 hours
Topics: RDD/DataFrame APIs, Spark SQL, MLlib pipelines, performance tuning
Hands-on: Process big data on Spark clusters and execute ML workflows at scale
Module 7: Reinforcement Learning
⏳ 12 hours
Topics: Markov decision processes, policy/value iteration, Q-learning, Deep RL basics
Hands-on: Train an agent on OpenAI Gym environments and visualize learning curves
Module 8: Graphical Models Certification
⏳ 12 hours
Topics: Probabilistic graphical models, Bayesian networks, inference algorithms
Hands-on: Build and query a Bayesian network for risk analysis scenarios
Module 9: Sequence Learning
⏳ 12 hours
Topics: RNNs, LSTMs, GRUs, sequence-to-sequence models, attention mechanisms
Hands-on: Develop an LSTM-based text generator and sentiment classifier
Module 10: Capstone Project & Portfolio
⏳ 20 hours
Topics: End-to-end pipeline design, cloud deployment, MLOps best practices
Hands-on: Deliver a complete ML solution—including data ingestion, model training, API deployment—and present your work
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Job Outlook
Machine Learning Engineers earn a median salary of $136,047 USD per year in the U.S., with 36% projected growth through 2033
Strong demand in tech, healthcare, finance, and e-commerce for scalable AI/ML solutions
Roles include ML Engineer, Data Scientist, NLP Engineer, and AI Research Scientist
Opportunities for freelance consulting in model development, MLOps, and AI strategy
Specification: Machine Learning Course
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