THE DATA SCIENCE COURSE 2019:COMPLETE BOOTCAMP
A Complete Data Science Training: Mathematics, Statistics, Python, Advanced Statistics in Python, Machine & Deep Learning. Come and visit now for advance learning.
What you will learn..?
This course provides the entire toolbox you need to become a data scientist.
Impress interviewers by showing an understanding of the data science field.
Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!).
Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data.
Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performance.
And lot MORE...
FOR MORE DETAILSPython for Data Science and Machine Learning Bootcamp
Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!
What you will learn..?
Use Python for Data Science and Machine LearningUse 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
.FOR MORE DETAILS
Statistics for Data Science and Business Analysis
Statistics you need in the office: Descriptive & Inferential statistics, Hypothesis testing, Regression analysis
Description
Is statistics a driving force in the industry you want to enter? Do you want to work as a Marketing Analyst, a Business Intelligence Analyst, a Data Analyst, or a Data Scientist? Well then, you’ve come to the right place!
What you will learn..?
Understand the fundamentals of statisticsLearn how to work with different types of data How to plot different types of data Calculate the measures of central tendency, asymmetry, and variability Calculate correlation and covariance Distinguish and work with different types of distributions Estimate confidence intervals Perform hypothesis testing Make data driven decisions Understand the mechanics of regression analysis Carry out regression analysis Use and understand dummy variables Understand the concepts needed for data science even with Python and R! .FOR MORE DETAILS
Complete Python Bootcamp: Go from zero to hero in Python 3
Learn Python like a Professional! Start from the basics and go all the way to creating your own applications and games!
Description
Become a Python Programmer and learn one of the employer's most requested skills of 2018! This is the most comprehensive, yet straight-forward, course for the Python programming language on Udemy! Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course, we will teach you Python 3. (Note, we also provide older Python 2 notes in case you need them) With over 100 lectures and more than 20 hours of video, this comprehensive course leaves no stone unturned! This course includes quizzes, tests, and homework assignments as well as 3 major projects to create a Python project portfolio! This course will teach you Python in a practical manner, with every lecture comes to a full coding screencast and a corresponding code notebook! Learn in whatever manner is best for you! We will start by helping you get Python installed on your computer, regardless of your operating system, whether its Linux, MacOS, or Windows, we've got you covered!
What you will learn..?
Learn to use Python professionally, learning both Python 2 and Python 3!Create games with Python, like Tic Tac Toe and Blackjack!
Learn advanced Python features, like the collections module and how to work with timestamps!
Learn to use Object Oriented Programming with classes!
Understand complex topics, like decorators.
Understand how to use both the Jupyter Notebook and create .py files
Get an understanding of how to create GUIs in the Jupyter Notebook system!
Build a complete understanding of Python from the ground up!
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Tensorflow 2.0: Deep Learning and Artificial Intelligence
Computer Vision, Time Series Forecasting, and More!
Description
Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning. In other words, if you want to do deep learning, you gotta know Tensorflow. This course is for beginner-level students all the way up to expert-level students. How can this be? If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts. Along the way, you will learn about all of the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks (image processing), and Recurrent Neural Networks (sequence data). Even if you've taken all of my previous courses already, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, and there are all-new and never-before-seen projects in this course such as time series forecasting and how to do stock predictions. This course is designed for students who want to learn fast, but there are also "in-depth" sections in case you want to dig a little deeper into the theory (like what is a loss function, and what are the different types of gradient descent approaches).
What you will learn..?
Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)Image Recognition
Predict Stock Returns
Time Series Forecasting
Computer Vision
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Use Tensorflow Serving to serve your model using a RESTful API
Use Tensorflow's Distribution Strategies to parallelize learning
Low-level Tensorflow, gradient tape, and how to build your own custom models
Demonstrate Moore's Law using Code
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Machine Learning, Data Science and Deep Learning with Python
Description
Machine Learning and artificial intelligence (AI) is everywhere; if you want to know how companies like Google, Amazon, and even Udemy extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This comprehensive machine learning tutorial includes over 100 lectures spanning 13 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice. I’ll draw on my 9 years of experience at Amazon and IMDb to guide you through what matters, and what doesn’t. Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. At the end, you'll be given a final project to apply what you've learned!
What you will learn..?
Build artificial neural networks with Tensorflow and KerasClassify images, data, and sentiments using deep learning
Make predictions using linear regression, polynomial regression, and multivariate regression
Data Visualization with MatPlotLib and Seaborn
Implement machine learning at massive scale with Apache Spark's MLLib
Understand reinforcement learning - and how to build a Pac-Man bot
Classify data using K-Means clustering, Support Vector Machines (SVM), KNN, Decision Trees, Naive Bayes, and PCA
Use train/test and K-Fold cross validation to choose and tune your models
Build a movie recommender system using item-based and user-based collaborative filtering
Clean your input data to remove outliers
Design and evaluate A/B tests using T-Tests and P-Values FOR MORE DETAILS
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