The Complete Data Science and Image Recognition Course with Python
Learn everything you need to become a data scientist. Jump into Pandas, PyPlot, MNIST, Keras and more popular libraries.
The complete guide to TensorFlow, data science, data analysis, image recognition and everything else you NEED to know
Absolutely no experience necessary. Start with a complete introduction to Python that is perfect for absolute beginners and can also be used a review.
Jump into using the most popular libraries and frameworks for working with Python. You'll learn everything you need to become a data scientist. This includes:
1. Data Science with NumPy
Build projects with NumPy, the #1 Python library for data science providing arrays and matrices.
2. Data Analysis with Pandas
Build projects with pandas, a software library written for the Python programming language for data manipulation and analysis.
2. Data Visualization with PyPlot
Build projects with pyplot, a MATLABlike plotting framework enabling you to create a figure, create a plotting area in a figure, plot lines in a plotting area, decorate the plot with labels and much more. Learn it all in this massive course.
3. Machine Learning Theory
Machine learning is in high demand and is quickly becoming a requirement on every software engineer's resume. Learn how to solve problems with machine learning before diving into practical examples.
4. Introduction to TensorFlow
Build projects with TensorFlow, the most popular plaform enabling ML developers to build and deploy machine learning applications such as neural networks. Build your first linear regression model with TensorFlow. Learn how to build a dataset, model, train and test!
5. Build a Convolutional Neural Network
Build a convolutional neural network (CNN.) Learn how to use Keras with machine learning models.
Keras is a neuralnetwork library written in Python capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, and PlaidML. You'll be able to enable fast experimentation with deep neural networks with Keras.
Machine learning is quickly becoming a required skill for every software developer.
Enroll now to learn everything you need to know to get up to speed, whether you're a developer or aspiring data scientist. This is the course for you.
Your complete Python course for image recognition, data analysis, data visualization and more.
Reviews On Our Python Courses:
 "I know enough Python to be dangerous. Most of the ML classes are so abstract and theoretical that no learning happens. This is the first class where we use concrete examples that I can relate to and allow me to learn. Absolutely love this course!"  Mary T.
 "Yes, this is an amazing start. For someone new in python this is a very simple boot course. I am able to relate to my earlier programming experience with ease!"  Gajendran C.
 "Clear and concise information"  Paul B.
 "Easy to understand and very clear explanations. So far so good!!!"  Alejandro M.
Don't miss the biggest Python course of the year. This is a once in a lifetime chance to enroll in a massive course.
All source code is included for each project.
If you buy one course this year, this is it. Sign up while spots are open.
Your Instructor
Nimish Narang is Mammoth Interactive's lead developer specializing in Python, iOS and Android. Primarily a coder, he also is an avid trader.
Mammoth Interactive is a leading online course provider in everything from learning to code to becoming a YouTube star. Mammoth Interactive courses have been featured on Harvardโs edX, Business Insider and more.
Over 11 years, Mammoth Interactive has built a global student community with 1.1 million courses sold. Mammoth Interactive has released over 250 courses and 2,500 hours of video content.
Founder and CEO John Bura has been programming since 1997 and teaching
since 2002. John has created topselling applications for iOS, Xbox and
more. John also runs SaaS company Devonian Apps, building
efficiencyminded software for technology workers like you.
Course Curriculum

Preview00. Introduction (4:47)

Start01. Intro To Python (5:46)

Start02. Variables (19:34)

Start03. Type Conversion Examples (10:21)

Start04. Operators (7:21)

Start05. Operators Examples (22:09)

Start06. Collections (8:39)

Start07. Lists (11:55)

Start08. Multidimensional List Examples (8:22)

Start09. Tuples Examples (8:51)

Start10. Dictionaries Examples (14:41)

Start11. Ranges Examples (8:46)

Start12. Conditionals (6:58)

Start13. If Statement Examples (10:32)

Start14. If Statement Variants Examples (11:35)

Start15. Loops (7:17)

Start16. While Loops Examples (11:47)

Start17. For Loops Examples (11:35)

Start18. Functions (8:04)

Start19. Functions Examples (9:33)

Start20. Parameters And Return Values Examples (14:08)

Start21. Classes and Objects (11:30)

Start22. Classes Example (13:28)

Start23. Objects Examples (10:10)

Start24. Inheritance Examples (17:43)

Start25. Static Members Example (11:20)

Start26. Summary and Outro (4:23)

StartIntro to Python Slides

StartPython_Language_Basics Code

Preview00. Course Intro (5:11)

Start01. Intro to Numpy (6:20)

Start02. Installing Numpy (3:59)

Start03. Creating Numpy Arrays (16:55)

Start04. Creating Numpy Matrices (11:57)

Start05. Getting and Setting Numpy Elements (16:59)

Start06. Arithmetic Operations on Numpy Arrays (11:56)

Start07. Numpy Functions Part 1 (19:13)

Start08. Numpy Functions Part 2 (12:36)

Start09. Summary and Outro (3:01)

StartNumpy_Code

StartNumpy Slides

Preview00. Panda Course Introduction (5:43)

Start01. Intro to Pandas (7:55)

Start02. Installing Pandas (5:28)

Start03. Creating Pandas Series (20:34)

Start04. Date Ranges (11:29)

Start05. Getting Elements from Series (19:20)

Start06. Getting Properties of Series (13:04)

Start07. Modifying Series (19:01)

Start08. Operations on Series (11:48)

Start09. Creating Pandas DataFrames (22:57)

Start10. Getting Elements from DataFrames (25:12)

Start11. Getting Properties from DataFrames (17:44)

Start12. Dataframe Modification (36:24)

Start13. DataFrame Operations (20:09)

Start14 DataFrame Comparisons and Iteration (15:35)

Start15. Reading CSVs (12:00)

Start16.Summary and Outro (4:14)

StartpandasPracticeCSV

StartPandas_Code

StartPandas Slides