Machine Learning Course for Beginners 2025

1. Data Scientists looking to deepen their skills in machine learning.
2. Machine Learning Engineers eager to master advanced techniques.
3. Data Analysts aiming to expand their knowledge of predictive modeling.
4. Software Developers are interested in exploring machine learning.
5. IT Professionals transitioning to data-focused careers.
6. Statisticians who want to apply machine learning to their work.
7. Business Analysts seeking to make data-driven decisions.
8. Research Scientists working on AI or data-related projects.
9. Students pursuing graduate programs in computer science or data science
10. AI Enthusiasts are passionate about exploring machine learning.
11. Product Managers looking to incorporate machine learning into product strategies.
12. Technical Managers leading teams on data-driven projects.

This course is tailored for anyone who wants to build a strong foundation in machine learning and apply it to real-world challenges.

Learning Objectives - What Will You Learn in the Machine Learning Speciality Course?

Course Overview: The Machine Learning Speciality course is designed to provide a complete understanding of data science and machine learning, starting from the basics to advanced concepts. It combines theory with real-world projects to ensure hands-on learning.

What You Will Learn:

1. Understand why data science and machine learning are essential for solving problems and making better decisions.
2. Discover the distinctions between descriptive, predictive, and prescriptive analytics and how each is used.
3. Master the data science project workflow, from planning to final deployment.
4. Gain key skills needed for a data scientist role, such as statistics and programming.
5. Learn about various types of machine learning, such as supervised, unsupervised, and reinforcement learning.
6. Learn Python and its tools for data analysis, visualization, and building machine learning models.
7. Perform exploratory data analysis (EDA) and data preprocessing, like handling missing or categorical data.
8. Build and test machine learning models for regression and classification, and understand concepts like overfitting and model selection.
9. Apply feature selection and dimensionality reduction techniques to enhance the performance of your model.
10. Use cross-validation and hyperparameter tuning methods to improve model performance.
11. Get introduced to deep learning, build neural networks with Keras and TensorFlow, and understand key concepts like activation functions.
12. Learn clustering methods to group data and use tools like the Elbow method to find the right number of clusters.

Basic Course- 

1. Week 1: Introduction, Definitions, and Core Principles of Machine Learning
2. Week 2: Probability Density Estimation

1. Explore the key concepts you'll master in Intermediate Machine Learning!
2. Supervised Learning II: Advanced Regressors and Classifiers
3. Discover supervised learning algorithms such as Naive Bayes, Support Vector Machines, and Linear Discriminant Analysis.

1. Module Introduction to Data Science & Machine Learning
2. Module Python for Data Analysis & Pre-processing
3. Data Pre-Processing