Are you thinking about learning data science? Do you know how to code in Python and want to take your learning further? Then you’ve come to the right place.
Data is more available today than it ever has been and in much larger quantities than ever before. And it’s only set to increase. Because of that, we need to evolve in how we process data, and that’s where data science comes in.
But let’s not forget machine learning, a subset of data science that offers us ways of examining and analyzing data to draw meaningful insights. Machine learning and data science are our future, and if you choose not to go down the route of learning it to an expert level, you should at least understand how it all works.
Here’s what you will:
• Learn what data science and machine learning are and their limitations
• Be introduced to NumPy and working with NumPy arrays
• Be introduced to Pandas and data manipulation
• Be introduced to Matplotlib and Seaborn and data visualization
• Discover an in-depth introduction to machine learning
• Master popular machine learning algorithms
• Learn how to implement classification and regression with Python
Python is the most popular computer programming language and by far the best for data science and machine learning. An intuitive language, it offers all the tools needed to analyze data, manipulate it, produce visualizations, and so much more.
Don’t slip behind. Data science is the future so embark on a journey you will never regret.
Introduction Part One – An Introduction to Data Science and Machine Learning What Is Data Science? How Important Is Data Science? Data Science Limitations What Is Machine Learning? How Important Is Machine Learning? Machine Learning Limitations Data Science vs. Machine Learning Part Two – Introducing NumPy What Is NumPy Library? How to Create a NumPy Array Shaping and Reshaping a NumPy array Index and Slice a NumPy Array Stack and Concatenate NumPy Arrays Broadcasting in NumPy Arrays NumPy Ufuncs Doing Math with NumPy Arrays NumPy Arrays and Images Part Three – Data Manipulation with Pandas Question One: How Do I Create a Pandas DataFrame? Question Two – How Do I Select a Column or Index from a DataFrame? Question Three: How Do I Add a Row, Column, or Index to a DataFrame? Question Four: How Do I Delete Indices, Rows, or Columns From a Data Frame? Question Five: How Do I Rename the Columns or Index of a DataFrame? Question Six: How Do I Format the DataFrame Data? Question Seven: How Do I Create an Empty DataFrame? Question Eight: When I Import Data, Will Pandas Recognize Dates? Question Nine: When Should a DataFrame Be Reshaped? Why and How? Question Ten: How Do I Iterate Over a DataFrame? Question Eleven: How Do I Write a DataFrame to a File? Part Four – Data Visualization with Matplotlib and Seaborn Using Matplotlib to Generate Histograms Using Matplotlib to Generate Scatter Plots Using Matplotlib to Generate Bar Charts Using Matplotlib to Generate Pie Charts Visualizing Data with Seaborn Using Seaborn to Generate Histograms Using Seaborn to Generate Scatter Plots Using Seaborn to Generate Heatmaps Using Seaborn to Generate Pairs Plot Part Five – An In-Depth Guide to Machine Learning Machine Learning Past to Present Machine Learning Features Different Types of Machine Learning Common Machine Learning Algorithms Gaussian Naive Bayes classifier K-Nearest Neighbors Support Vector Machine Learning Algorithm Fitting Support Vector Machines Linear Regression Machine Learning Algorithm Logistic Regression Machine Learning Algorithm A Logistic Regression Model Decision Tree Machine Learning Algorithm Random Forest Machine Learning Algorithm Artificial Neural Networks Machine Learning Algorithm Machine Learning Steps Evaluating a Machine Learning Model Model Evaluation Metrics Regression Metrics Implementing Machine Learning Algorithms with Python Advantages and Disadvantages of Machine Learning Conclusion ReferencesIntroduction
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