Machine Learning with Scikit-Learn and TensorFlow Certification
Master Scikit-Learn & TensorFlow to build, train, and deploy powerful ML models through hands-on projects and real-world AI applications.
Instructor
Cognovera
Category
- Description
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The Machine Learning with Scikit-Learn & TensorFlow Certification program is an intensive, hands-on course designed to equip learners with the skills to design, build, and deploy advanced machine learning models. Over 8 weeks, you will start from core ML principles, progress through Scikit-Learn for classical ML algorithms, and master TensorFlow for deep learning applications including CNNs, RNNs, NLP, and time series forecasting. This program emphasizes practical, real-world projects so you can confidently implement AI-driven solutions across industries.
Week 1: Introduction to Machine Learning
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1
Day 1: Overview of Machine Learning & AI Ecosystem -
2
Day 2: Types of Machine Learning – Supervised, Unsupervised, Reinforcement
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3
Day 3: Understanding Data – Features, Labels, and Datasets
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4
Day 4: Python Libraries for ML – NumPy, Pandas, Matplotlib, Seaborn
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5
Day 5: Hands-on – Data Exploration & Preprocessing Basics
Week 2: Data Preparation & Feature Engineering
Week 3: Supervised Learning with Scikit-Learn
Week 4: Unsupervised Learning & Model Tuning
Week 5: Introduction to TensorFlow & Neural Networks
Week 6: Advanced Neural Networks & CNNs
Week 7: RNNs, NLP & Time Series Forecasting
Week 8: End-to-End Machine Learning Project
1. Do I need deep learning experience before joining?
No, the course starts from ML basics and progresses to deep learning, so prior deep learning experience is not required.
2. Will I work on real datasets?
Yes, you’ll use public datasets from domains like healthcare, finance, e-commerce, and NLP to build deployable models.
3. What tools and libraries will I learn?
You’ll work extensively with Scikit-Learn, TensorFlow, Keras, NumPy, Pandas, Matplotlib, and Seaborn.
4. Is there a final project?
Yes, you’ll complete a capstone project involving an end-to-end ML pipeline from data preprocessing to model deployment.
5. Will I get job-ready skills from this course?
Absolutely. The curriculum focuses on practical, industry-aligned skills, enabling you to work on real-world AI/ML problems.
Skills You Will Learn
- Data preprocessing, cleaning, and feature engineering
- Building supervised and unsupervised ML models with Scikit-Learn
- Implementing deep learning models using TensorFlow & Keras
- Designing CNNs for image processing and RNNs for sequential data
- Applying NLP techniques for text classification and sentiment analysis
- Performing hyperparameter tuning and model optimization
- Deploying ML models using Flask or Streamlit
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Course details
Lectures
40
Level
Intermediate
Basic info
- Programme Name: Machine Learning with Scikit-Learn & TensorFlow Certification
- Duration: 8 Weeks (5 Days/Week)
- Delivery Mode: Online / Blended Learning
- Level: Intermediate to Advanced
- Assessment: Weekly projects, quizzes, and a capstone project
- Certification: Professional Completion Certificate
Course requirements
- Proficiency in Python programming (variables, loops, functions, OOP concepts)
- Basic knowledge of statistics and linear algebra
- Familiarity with Jupyter Notebook and data analysis libraries (NumPy, Pandas, Matplotlib)
- A computer with Python 3.x installed and internet access
Intended audience
- Data Analysts and Data Engineers transitioning to Machine Learning roles
- Software Developers aiming to enter AI/ML development
- Researchers and students seeking applied ML skills for academic or industry projects
- Professionals in IT, finance, healthcare, marketing, or manufacturing leveraging ML in their fields
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