Machine Learning and artificial intelligence (AI) are revolutionizing industries everywhere. If you're curious about how companies like Google, Amazon, and even Udemy extract insights from massive data sets, this data science course equips you with the foundational knowledge needed. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. But it's not just about the money—this field offers engaging and intellectually stimulating work!
Course Overview
This comprehensive course covers the essential aspects of machine learning, providing a strong foundation and practical applications of these concepts.
Foundations
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Introduction to Machine Learning
- Understanding machine learning and its applications across various fields.
- The advantages of using Python libraries for machine learning.
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Python for AI & ML
- Python Basics: Functions, packages, and routines.
- Data Structures: Arrays, vectors, and data frames with practical examples.
- Installation and function of Jupyter Notebook.
- Key Libraries: Pandas, NumPy, Matplotlib, Seaborn.
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Applied Statistics
- Descriptive statistics, probability, and conditional probability.
- Hypothesis Testing and Inferential Statistics.
- Understanding different probability distributions: Binomial, Poisson, and Normal.
Machine Learning Techniques
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Supervised Learning
- Regression Models: From simple to multiple linear regression, including evaluation metrics.
- Classification Techniques: Naïve Bayes, K-NN classification, Support Vector Machines.
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Unsupervised Learning
- Clustering Techniques: K-means and hierarchical clustering, high-dimensional clustering.
- Dimension Reduction: PCA.
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Classification
- Introduction to various classification methods, including decision trees and logistic regression.
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Ensemble Techniques
- Advanced methods like Decision Trees, Bagging, Random Forests, and Boosting.
Advanced Topics
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Featurization, Model Selection & Tuning
- Feature engineering and model performance.
- ML pipeline, Grid search CV, and K-fold cross-validation.
- Model tuning and techniques such as regularization and bootstrapping.
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Recommendation Systems
- Exploration of different models: Popularity-based, Content-based, and Collaborative filtering.
Additional Modules
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Exploratory Data Analysis (EDA)
- Utilizing the Pandas-profiling library for effective analysis.
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Time Series Forecasting
- The ARIMA approach for effective prediction.
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Model Deployment
- Using Kubernetes for deploying machine learning models.
Capstone Project
If you have some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry, preparing you for this lucrative career path. Concepts are introduced in plain English before being demonstrated with Python code, which you can experiment with and reference in the future. This course focuses on practical understanding and application over academic or deeply mathematical explorations of algorithms. You'll conclude with a capstone project to solidify your learning.
Student Testimonials
Our Learner's Review: "Excellent course. Precise and well-organized presentation. The complete course is filled with a lot of learning not only theoretical but also practical examples. Mr. Risabh shares his practical experiences and actual problems faced by data scientists/ML engineers. The topic of 'The ethics of deep learning' is really a gold nugget that everyone must follow. Thank you, 1stMentor and SelfCode Academy for this wonderful course."
