What you'll learn
- Introduction to Machine Learning & Different types of Machine Learning
- Setting up the Python Environment
- Python Programming Basics – Data Structures, Working with Lists, Loops & List Comprehension
- Python Programming Basics – Beyond Lists: Tuples & Dictionaries, Introducing Functions
- Pandas – Working with Data
- Visualization with Seaborn
- ML Algorithms: Multiple Linear Regression , Implementing MLR
- ML Algorithms: Model Evaluation & Diagnostics
- ML Algorithms: Model Validation Strategies (K-Fold Cross Validation)
- ML Algorithms: Classification – Logistic Regression Theory
- ML Algorithms: Logistic Regression Implementation in Scikit-learn
- ML Algorithms: Model Evaluation & Diagnostics for Logistic Regression
- ML Algorithms: Model Validation Strategies (K-Fold Cross Validation) for Logistic Regression
- ML Algorithms: Classification & Regression Tree – Theory
- ML Algorithms: Model Evaluation & Diagnostics (Tree-Based Models)
- Machine Learning Pipeline & Process
- Bias vs. Variance
- Introduction to Feature Engineering
- Handling Imbalanced Data
- Ensemble Models for Classification – Random Forest, Gradient Boosting
- Unsupervised Model Clustering
What is this course about?
Kick start or accelerate your career with Practical Data Science & ML: Essentials for Career Growth – a hands-on, video-based course designed for real-world impact. Whether you’re a student exploring data science or a working professional aiming to up skill, this course provides a practical, job-ready foundation in data science and machine learning. Learn how to work with real datasets, apply machine learning algorithms, build predictive models, and gain actionable insights – all using industry-standard tools like Python, Pandas, Scikit-learn, and Jupyter Notebooks. This course should be chosen for the key reasons:
- Practical Focus: Learn by doing, not just theory.
- Beginner-Friendly: No prior coding experience needed.
- Expert-Led Training: Taught by data science professionals and practitioners in collaboration with leading academicians.
- Optimized functional and technical skills.
- The diverse case studies and real life problems helps aspirants imbibe deep sectorial knowledge with the cutting edge techniques to work on real life business solutions.
Who is this course for?
- Students looking to build job-ready skills in the area of Data Science & Machine Learning
- Working professionals aiming to up skill or switch careers leveraging Data Science & Machine Learning.
- Entrepreneurs and business owners seeking practical insights by adopting Data Science & Machine Learning.
- Anyone interested in lifelong learning in the area of Data Science & Machine Learning
- Academicians, professors or facilitators wanting to have the practical application of Data Science & Machine Learning to pass on to the students.
- Researchers who needs to leverage cutting edge techniques in Machine Learning and Data Science for converting their knowledge and novelty into a paper for publications in research journals.
What are the prerequisites for this course?
- Basic knowledge of statistics and probability
- Familiarity with SAS software
- Basic understanding of data preprocessing and exploratory data analysis (not mandatory, but some knowledge may help).
Neumuuv is an experiential learntech platform offering hands-on courses in technology, business, and soft skills. Unlike traditional edtechs, our trainers are industry professionals from top MNCs who bring real-world expertise into every session. We bridge the gap between academia and industry by preparing learners with practical, career-ready skills.
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