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An Introductory Guide to Data Science and Machine Learning
Gain insights into data science and machine learning, explore essential concepts and libraries, and work on real-time projects to become a confident data scientist.
4.3
93 lessons
2 AI mock interviews
6h
Created by Jamshaid Sohail
What's included
- 93 lessons across 11 modules
- 2 AI mock interviews with scoring rubrics
- AI Tutor on every lesson
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What You'll Learn
- Differentiate data science, data analysis, and data engineering roles, tools, and responsibilities
- Apply the data science life cycle from problem framing to model monitoring in real-world workflows
- Acquire web data by scraping pages and HTML tables using Beautiful Soup and Scrapy
- Manipulate and analyze data with NumPy and pandas, including statistics, sorting, merging, and pivots
- Build visualizations in seaborn to interpret relationships, distributions, trends, and outliers
- Train and evaluate ML models for regression, classification, and clustering using scikit-learn metrics and CV
- Implement deep learning basics including backpropagation, CNNs, RNNs, and LSTMs for image/sequence tasks
Learning Roadmap
1.
What Is Data Science ?
What Is Data Science ?
Get familiar with the core concepts and distinctions of data science and its life cycle.
2.
Applications of Data Science
Applications of Data Science
Get started with data science in health care, recommender systems, and image analysis applications.
3.
Overview of Libraries
Overview of Libraries
13 Lessons
13 Lessons
Master essential data science libraries: Beautiful Soup for web scraping, NumPy for array operations, pandas for data analysis, and SpaCy for NLP.
4.
Probability and Statistics
Probability and Statistics
13 Lessons
13 Lessons
Grasp the fundamentals of probability, statistics, distributions, skewness, sampling, and hypothesis testing.
5.
Machine Learning Part-1
Machine Learning Part-1
21 Lessons
21 Lessons
Deepen your knowledge of machine learning basics, types, regression techniques, feature engineering, and model evaluation measures.
6.
Machine Learning Part-2
Machine Learning Part-2
15 Lessons
15 Lessons
Follow the process of exploring classification algorithms and techniques to evaluate model performance.
7.
Machine Learning Part-3
Machine Learning Part-3
7 Lessons
7 Lessons
Piece together the parts of unsupervised learning, clustering techniques, and semi-supervised learning applications.
8.
Deep Learning
Deep Learning
8 Lessons
8 Lessons
Sharpen your skills in deep learning, neural networks, CNNs, RNNs, and LSTM networks.
9.
Machine Learning Tools and Libraries
Machine Learning Tools and Libraries
3 Lessons
3 Lessons
Get started with essential tools and libraries for efficient machine learning.
10.
Big Data Tools and Technologies
Big Data Tools and Technologies
4 Lessons
4 Lessons
Understand big data, Hadoop, MapReduce, Apache Spark, and their applications.
11.
Where to go next ?
Where to go next ?
3 Lessons
3 Lessons
Apply your skills through Kaggle competitions, Educative courses, and essential references.
Certificate of Completion
Showcase your accomplishment by sharing your certificate of completion.
Complete more lessons to unlock your certificate
About this course
WHY I WROTE THIS COURSE
There is a lot of dispersed and somewhat conflicting information on the internet when it comes to data science, making it tough to know where to start. Don't worry.
This course will get you familiar with the state of data science and the related fields such as machine learning and big data. You'll learn the fundamental concepts and libraries that are essential to solve any problem in this field.
You will work on real-time projects from Kaggle while also honing your mathematical skills, which will be used extensively in most problems you face. You'll go through a systematic approach to learning about data acquisition to data wrangling and everything in between. This is your all-in-one guide to becoming a confident data scientist.
ABOUT THE AUTHOR
Jamshaid Sohail
I am a Data Scientist holding an industrial experience of 4 years. I have completed over 67 online courses and currently pursuing my Masters in Data Science from RWTH Aachen University. I have authored 4 courses related to Data science.
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