Machine Learning A-Z™: Hands-On Python & R In Data Science

Machine Learning A-Z™: Hands-On Python & R In Data Science

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Course Description

Are you ready to start your path to becoming a Data Scientist! This comprehensive course will be your guide to learning how to use the power of Python to analyze data, create beautiful visualizations, and use powerful machine learning algorithms! Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world's most interesting problems! This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science! This comprehensive course is comparable to other Data Science bootcamps that usually cost thousands of dollars, but now you can learn all that information at a fraction of the cost! With over 100 HD video lectures and detailed code notebooks for every lecture this is one of the most comprehensive course for data science an

d machine learning on Udemy! We'll teach you how to program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning: Programming with Python NumPy with Python Using pandas Data Frames to solve complex tasks Use pandas to handle Excel Files Web scraping with python Connect Python to SQL Use matplotlib and seaborn for data visualizations Use plotly for interactive visualizations Machine Learning with SciKit Learn, including: Linear Regression K Nearest Neighbors K Means Clustering Decision Trees Random Forests Natural Language Processing Neural Nets and Deep Learning Support Vector Machines and much, much more! Enroll in the course and become a data scientist today! Who is the target audience? This course is meant for people with at least some programming experience

What you will learn

Use Python for Data Science and Machine Learning Use Spark for Big Data Analysis Implement Machine Learning Algorithms Learn to use NumPy for Numerical Data Learn to use Pandas for Data Analysis Learn to use Matplotlib for Python Plotting Learn to use Seaborn for statistical plots Use Plotly for interactive dynamic visualizations Use SciKit-Learn for Machine Learning Tasks K-Means Clustering Logistic Regression Linear Regression Random Forest and Decision Trees Natural Language Processing and Spam Filters Neural Networks Support Vector Machines


Section 1: Welcome to the course!

Section 2: -------------------- Part 1: Data Preprocessing --------------------

Section 3: Simple Linear Regression

Section 4: Multiple Linear Regression

Section 5: Polynomial Regression

Section 6: Support Vector Regression (SVR)

Section 7: Decision Tree Regression

Section 8: Random Forest Regression

Section 9: Evaluating Regression Models Performance

Section 10: Logistic Regression

Section 11: K-Nearest Neighbors (K-NN)

Section 12: Support Vector Machine (SVM)

Section 13: Kernel SVM

Section 14: Naive Bayes

Section 15: Decision Tree Classification

Section 16: Random Forest Classification

Section 17: Evaluating Classification Models Performance

Section 18: K-Means Clustering

Section 19: Hierarchical Clustering

Section 20: Apriori

Section 21: Eclat

Section 22: Upper Confidence Bound (UCB)

Section 23: Thompson Sampling

Section 24: -------------------- Part 7: Natural Language Processing --------------------

Section 25: -------------------- Part 8: Deep Learning --------------------

Section 26: Artificial Neural Networks

Section 27: Convolutional Neural Networks

Section 28: Principal Component Analysis (PCA)

Section 29: Linear Discriminant Analysis (LDA)

Section 30: Kernel PCA

Section 31: Model Selection

Section 32: XGBoost