Machine Learning, Data Science and Deep Learning with Python
4.8 /5- Offered byUDEMY
Machine Learning, Data Science and Deep Learning with Python at UDEMY Overview
Duration | 14 hours |
Total fee | ₹649 |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Go to Website  |
Credential | Certificate |
Machine Learning, Data Science and Deep Learning with Python at UDEMY Highlights
- Earn a Certificate of completion from Udemy
- Learn from 1 downloadable resources & 6 article
- Get full lifetime access of the course material
- Comes with 30 days money back guarantee
Machine Learning, Data Science and Deep Learning with Python at UDEMY Course details
- For Software developers or programmers who want to transition into the lucrative data science and machine learning career path will learn a lot from this course
- For Technologists curious about how deep learning really works
- For Data analyst
- Build artificial neural networks with Tensorflow and Keras
- Classify 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
- This comprehensive machine learning tutorial includes over 100 lectures spanning 15 hours of video, and most topics include hands-on Python code examples you can use for reference and for practice
- Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon
- The topics in this course come from an analysis of real requirements in data scientist job listings from the biggest tech employers
- We'll cover the A-Z of machine learning, AI, and data mining techniques real employers are looking for
Machine Learning, Data Science and Deep Learning with Python at UDEMY Curriculum
Getting Started
Introduction
Udemy 101: Getting the Most From This Course
[Activity] Getting What You Need
Installation: Getting Started
[Activity] WINDOWS: Installing and Using Anaconda & Course Materials
[Activity] MAC: Installing and Using Anaconda & Course Materials
[Activity] LINUX: Installing and Using Anaconda & Course Materials
[Activity] Installing Enthought Canopy
Python Basics, Part 1 [Optional]
[Activity] Python Basics, Part 2 [Optional]
[Activity] Python Basics, Part 3 [Optional]
[Activity] Python Basics, Part 4 [Optional]
Running Python Scripts [Optional]
Introducing the Pandas Library [Optional]
Statistics and Probability Refresher, and Python Practice
Types of Data
Mean, Median, Mode
[Activity] Using mean, median, and mode in Python
[Activity] Variation and Standard Deviation
Probability Density Function; Probability Mass Function
Common Data Distributions
[Activity] Percentiles and Moments
[Activity] A Crash Course in matplotlib
[Activity] Advanced Visualization with Seaborn
[Activity] Covariance and Correlation
[Exercise] Conditional Probability
Exercise Solution: Conditional Probability of Purchase by Age
Bayes' Theorem
Predictive Models
[Activity] Linear Regression
[Activity] Linear Regression
[Activity] Polynomial Regression
[Activity] Multiple Regression, and Predicting Car Prices
Multi-Level Models
Machine Learning with Python
Supervised vs. Unsupervised Learning, and Train/Test
[Activity] Using Train/Test to Prevent Overfitting a Polynomial Regression
Bayesian Methods: Concepts
[Activity] Implementing a Spam Classifier with Naive Bayes
K-Means Clustering
[Activity] Clustering people based on income and age
Measuring Entropy
[Activity] Install GraphViz
[Activity] WINDOWS: Installing Graphviz
[Activity] MAC: Installing Graphviz
[Activity] LINUX: Installing Graphviz
Decision Trees: Concepts
[Activity] Decision Trees: Predicting Hiring Decisions
Ensemble Learning
[Activity] XGBoost
Support Vector Machines (SVM) Overview
[Activity] Using SVM to cluster people using scikit-learn
Recommender Systems
User-Based Collaborative Filtering
Item-Based Collaborative Filtering
[Activity] Finding Movie Similarities
[Activity] Improving the Results of Movie Similarities
[Activity] Making Movie Recommendations to People
[Exercise] Improve the recommender's results
More Data Mining and Machine Learning Techniques
K-Nearest-Neighbors: Concepts
[Activity] Using KNN to predict a rating for a movie
Dimensionality Reduction; Principal Component Analysis
[Activity] PCA Example with the Iris data set
Data Warehousing Overview: ETL and ELT
Reinforcement Learning
[Activity] Reinforcement Learning & Q-Learning with Gym
Understanding a Confusion Matrix
Measuring Classifiers (Precision, Recall, F1, ROC, AUC)
Dealing with Real-World Data
Bias/Variance Tradeoff
[Activity] K-Fold Cross-Validation to avoid overfitting
Data Cleaning and Normalization
[Activity] Cleaning web log data
Normalizing numerical data
[Activity] Detecting outliers
Feature Engineering and the Curse of Dimensionality
Imputation Techniques for Missing Data
Handling Unbalanced Data: Oversampling, Undersampling, and SMOTE
Binning, Transforming, Encoding, Scaling, and Shuffling
Apache Spark: Machine Learning on Big Data
Warning about Java 11 and Spark 3!
Spark installation notes for MacOS and Linux users
[Activity] Installing Spark - Part 1
[Activity] Installing Spark - Part 2
Spark Introduction
Spark and the Resilient Distributed Dataset (RDD)
Introducing MLLib
Introduction to Decision Trees in Spark
[Activity] K-Means Clustering in Spark
TF / IDF
[Activity] Searching Wikipedia with Spark
[Activity] Using the Spark 2.0 DataFrame API for MLLib
Experimental Design / ML in the Real World
Deploying Models to Real-Time Systems
A/B Testing Concepts
T-Tests and P-Values
[Activity] Hands-on With T-Tests
Determining How Long to Run an Experiment
A/B Test Gotchas
Deep Learning and Neural Networks
Deep Learning Pre-Requisites
The History of Artificial Neural Networks
[Activity] Deep Learning in the Tensorflow Playground
Deep Learning Details
Introducing Tensorflow
Important note about Tensorflow 2
[Activity] Using Tensorflow, Part 1
[Activity] Using Tensorflow, Part 2
[Activity] Introducing Keras
[Activity] Using Keras to Predict Political Affiliations
Convolutional Neural Networks (CNN's)
[Activity] Using CNN's for handwriting recognition
Recurrent Neural Networks (RNN's)
[Activity] Using a RNN for sentiment analysis
[Activity] Transfer Learning
Tuning Neural Networks: Learning Rate and Batch Size Hyperparameters
Deep Learning Regularization with Dropout and Early Stopping
The Ethics of Deep Learning
Learning More about Deep Learning
Final Project
Your final project assignment
Final project review
You made it!
More to Explore
Don't Forget to Leave a Rating!
Bonus Lecture: Discounts on my Spark and MapReduce courses!
Bonus Lecture: More courses to explore!
Machine Learning, Data Science and Deep Learning with Python at UDEMY Faculty details
Machine Learning, Data Science and Deep Learning with Python at UDEMY Entry Requirements
- Prerequisite: Some prior coding or scripting experience is required
- At least high school level math skills will be required
- Not mentioned
Other courses offered by UDEMY
Student Forum
Anything you would want to ask experts?
Data ScienceMachine LearningMachine Learning and Pattern RecognitionMachine Learning, Data Science and Deep Learning with Python