Machine Learning: The Ultimate Guide To Discover The Mathematics Of Computer Science
- Length: 118 pages
- Edition: 1
- Language: English
- Publication Date: 2021-10-17
- ISBN-10: B09JP6YMC6
- Sales Rank: #432944 (See Top 100 Books)
Do you want to learn more about the world of Machine Learning and its applications? Would you like to improve and refine your Python programming skills? Would you like to become computer science savvy? If the answer to these questions is yes, then keep reading…
Machine Learning is rapidly changing our entire life, and thousands of companies all around the world are desperately searching for experts who can guide their transition into this fascinating field.
Whether you want to start from scratch or extend your programming knowledge, this book will ignite your passion for computer science and open tons of doors in both your personal and professional life.
WARNING: Do not read this book if you’re looking for a boring textbook containing a lot of dry math and programming lingo.
Jason Callaway has condensed everything you need in a simple and clear way, with practical examples, detailed explanations, tips and tricks from his experience. His intuitive yet rigorous approach will speed up your learning, providing actionable insights on the key fundamentals of ML with Python programming. You will gain a broad picture of the ML ecosystem and will be well versed in the best practices of applying advanced techniques to analyze and solve any kind of problem in an extremely short time, even if you are a complete beginner.
Here is just a tiny fraction of what you will learn:
- The basics of Python programming: variables, data types, basic and advanced operations
- Essential Python libraries such as NumPy, Pandas, Matplotlib
- The most up-to-date computational methods and visualization techniques for data science
- Real-world applications of machine learning and artificial intelligence
- How to build statistical and machine learning models
- Neural networks and predictive modeling
And much more
This book won’t make you an expert programmer (this requires time and a lot of practice, and anyone who wants to sell you a magic pill to become a computer engineer in 3 days is simply a liar), but you will get an exciting first look at programming and a foundation in the fundamental concepts of computer science and machine learning. Moreover, it will remain your go-to guide during your entire professional life.
INTRODUCTION MACHINE LEARNING & ITS BASIC CONCEPT How Do Machine Learning and Traditional Programming Differ? Why We Need Machine Learning How Does Machine Learning Work? Machine Learning Past to Present Machine Learning Basic Concept APPLICATIONS OF THE MACHINE LEARNING TECHNOLOGY Virtual Personal Assistants Predictions While Driving Video Surveillance Social Media Email Spam and Malware Filtering Online Customer Service Refinement of Search Engine Results Product Recommendations Online Fraud Detection TOOLS & LIBRARIES Pandas NumPy Scikit-learn Matplotlib Seaborn TensorFlow QUIZ SOLUTIONS PYTHON FOR MACHINE LEARNING Why Use Python for Machine Learning? How to Get started with Python? DATA ANALYSIS TECHNIQUES How Is Data Analysis Performed? Text Analytics Business Intelligence Data Visualization DATA SCRUBBING TECHNIQUES What is Data Scrubbing? Removing Variables One-Hot Encoding Drop Missing Values CLUSTERING K-Means Clustering Centroid Initialization Methods Determine the Number of Clusters K Means Clustering with Scikit-Learn The Clustering Dataset Figure 4: Scatter plot for the clustering dataset DATA VISUALIZATION TECHNIQUES Importing and Using Matplotlib Supervised Learning Unsupervised Learning Approaching Problems DECISION TREES An Overview on Decision Trees Classification and Regression Trees The Overfitting Problem DEEP LEARNING WITH PYTHON How to Get Deep Learning to Work Machine Learning Vs. Deep Learning HOW TO BUILD THE FIRST MACHINE LEARN MODEL? Regression Linear Regression Model Decision Tree Regression Model TIPS TO MAKE MACHINE LEARNING WORK FOR YOU Tip #1 Remember the Logistics Tip #2 Mind the Data Tip #3 Algorithms Are Not Always Right Tip #4 Pick Out a Diverse Toolset Tip #5 Try Out Some Hybrid Learning EXAMPLES AND EXERCISES Task 1 - Import the libraries and load the dataset Task 2 - Basic data exploration Task 3 - Check for invalid values Task 4 - Remove invalid rows Task 5 - Split data into training and test subsets Task 6 - Check for correlation among features Task 7 - Plot scatter plots Task 8 - Data preprocessing Task 9 - Train our model Task 10 - Evaluate the model Task 11 - Use a column transformer and pipeline to simplify our workflow Task 12 - Train the model using the pipeline Task 13 - Evaluate the model Task 14 - Do cross-validation using the pipeline CONCLUSION
Donate to keep this site alive
1. Disable the AdBlock plugin. Otherwise, you may not get any links.
2. Solve the CAPTCHA.
3. Click download link.
4. Lead to download server to download.