3.96 out of 5
3.96
657 reviews on Udemy

Machine Learning and Data Science Hands-on with Python and R

Machine Learning, Statistics, Python, AI, Tensorflow, AWS, Deep Learning, R Programming, NLP, Bayesian, BI and much more
Instructor:
EDU CBA
38,988 students enrolled
English [Auto-generated]
Learn the use of Python for Data Science and Machine Learning
Master Machine Learning on Python & R
Master Machine Learning on Tensorflow
Learn Statistics, Python, Artificial Intelligence AI, Tensorflow, AWS.
Learn Deep Learning, R Programming, NLP, Bayesian Methods, A/B Testing, Business Intelligence BI, Regression.
Learn Hypothesis Testing, Algebra, Adaboost Regressor, Gaussian, Heuristic.
Learn Numpy, Pandas, Metplotlit, Seaborn.
Learn Forecasting, Distribution, Normalization, Trend Analysis, Predictive Modeling, Fraud Detection.
Learn Neural Network, Sequential Model, Data Visualization, Data Analysis, Data Manipulation, KNN Algorithm.
Learn Decision Tree, Random Forests, Kmeans Clustering, Vector Machine, Time Series Analysis, Market Basket Analysis

Learn from well designed, well-crafted study materials on Machine Learning ML, Statistics, Python, Artificial Intelligence AI, Tensorflow, AWS, Deep Learning, R Programming, NLP, Bayesian Methods, A/B Testing, Face Detection, Business Intelligence BI, Regression, Hypothesis Testing, Algebra, Adaboost Regressor, Gaussian, Heuristic, Numpy, Pandas, Metplotlit, Seaborn, Forecasting, Distribution, Normalization, Trend Analysis, Predictive Modeling, Fraud Detection, Neural Network, Sequential Model, Data Visualization, Data Analysis, Data Manipulation, KNN Algorithm, Decision Tree, Random Forests, Kmeans Clustering, Vector Machine, Time Series Analysis, Market Basket Analysis. Learn by doing. Full Lifetime Access.

Get the skills to work with implementations and develop capabilities that you can use to deliver results in a machine learning project. This program will help you build the foundation for a solid career in Machine learning Tools. Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions.

Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. Artificial intelligence is the simulation of human intelligence through machines and mostly through computer systems. Artificial intelligence is a sub field of computer. It enables computers to do things which are normally done by human beings. This program is a comprehensive understanding of AI concepts and its application using Python and iPython.

Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making.

Machine learning is a subfield of computer science stemming from research into artificial intelligence. It has strong ties to statistics and mathematical optimization, which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR), search engines and computer vision. Machine learning is sometimes conflated with data mining,] although that focuses more on exploratory data analysis. Machine learning and pattern recognition “can be viewed as two facets of the same field.

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.

Machine learning has proven to be a fruitful area of research, spawning a number of different problems and algorithms for their solution. This algorithm vary in their goals,in the available training data, and in the learning strategies. The ability to learn must be part of any system that would claim to possess general intelligence.

Machine Learning - Statistics Essentials

1
Machine Learning Introduction
2
Introduction to Machine Learning with Python
3
Analytics in Machine Learning
4
Big Data Machine Learning
5
Emerging Trends Machine Learning
6
Data Mining
7
Data Mining Continues
8
Supervised and Unsupervised
9
Sampling Method in Machine Learning
10
Technical Terminology
11
Error of Observation and Non Observation
12
Systematic Sampling
13
Cluster Sampling
14
Statistics Data Types
15
Qualitative Data and Visualization
16
Machine Learning
17
Relative Frequency Probability
18
Joint Probability
19
Conditional Probability
20
Concept of Independence
21
Total Probability
22
Random Variable
23
Probability Distribution
24
Cumulative Probability Distribution
25
Bernoulli Distribution
26
Gaussian Distribution
27
Geometric Distribution
28
Continuous and Normal Distribution
29
Mathematical Expression and Computation
30
Transpose of Matrix
31
Properties of Matrix
32
Determinants
33
Error Types
34
Critical Value Approach
35
Right and Left Sided Critical Approach
36
P-Value Approach
37
P-Value Approach Continues
38
Hypothesis Testing
39
Left Tail Test
40
Two Tail Test
41
Confidence Interval
42
Example of Confidence Interval
43
Normal and Non Normal Distribution
44
Normality Test
45
Normality Test Continues
46
Determining the Transformation
47
T-Test
48
T-Test Continue
49
More on T-Test
50
Test of Independence
51
Example of Test of Independence
52
Goodness of Fit Test
53
Example of Goodness of Fit Test
54
Co-Variance
55
Co-Variance Continues

Machine Learning with Tensorflow for Beginners

1
Introduction to Machine Learning with Tensorflow
2
Understanding Machine Learning
3
How do Machines Learns
4
Uses of Machine Learning
5
Examples with tensorflow by Google
6
Setting up the Workstation
7
Understanding program languages
8
Understanding and Functions of Jupyter
9
Learning of Jupyter installation
10
Understanding what Anaconda cloud is
11
Installation of Anaconda for Windows
12
Installation of Anaconda in Linux
13
Using the Jupyter notebook
14
Getting started with Anaconda
15
Determining options for Cloudberry
16
Introduction to Third Party Libraries
17
Numpy-Array
18
Numpy-Array Continue
19
Arrays
20
Arrays Continue
21
Indexing
22
Indexing Continue
23
Universal Functions
24
Introoduction to Pandas
25
Pandas Series
26
Pandas Series Continue
27
Import Randin
28
Import Randin Continue
29
Paratmeters
30
Indexing and Database
31
Missing Data
32
Missing Data-Groupby
33
Missing Data-Groupby Continue
34
Concat-Merge-Join
35
Operations
36
Import-Export
37
Python Visualisation
38
Mat Plotting
39
Multiple Plot Subsections
40
API Functionality
41
Title of the Plot
42
Change Size of Articles
43
Two Different Crops
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