Complete Machine Learning 2024 A-Z™: 10 Real World Projects
Complete Machine Learning 2024 A-Z™: 10 Real World Projects, available at $69.99, has an average rating of 4.83, with 139 lectures, based on 470 reviews, and has 5725 subscribers.
You will learn about Python Machine Learning Statistics and Math Data Science Natural Language Processing Data Analysis Data Visualization This course is ideal for individuals who are Beginner or Intermediate or Advanced It is particularly useful for Beginner or Intermediate or Advanced.
Enroll now: Complete Machine Learning 2024 A-Z™: 10 Real World Projects
Summary
Title: Complete Machine Learning 2024 A-Z™: 10 Real World Projects
Price: $69.99
Average Rating: 4.83
Number of Lectures: 139
Number of Published Lectures: 137
Number of Curriculum Items: 139
Number of Published Curriculum Objects: 137
Original Price: $109.99
Quality Status: approved
Status: Live
What You Will Learn
- Python
- Machine Learning
- Statistics and Math
- Data Science
- Natural Language Processing
- Data Analysis
- Data Visualization
Who Should Attend
- Beginner
- Intermediate
- Advanced
Target Audiences
- Beginner
- Intermediate
- Advanced
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!
This course is designed for both beginners with some programming experience or experienced developers looking to make the jump to Data Science!
This course is made to give you all the required knowledge at the beginning of your journey, so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips and trick you would require to start your career.
It gives detailed guide on the Data science process involved and Machine Learning algorithms. All the algorithms are covered in detail so that the learner gains good understanding of the concepts. Although Machine Learning involves use of pre-developed algorithms one needs to have a clear understanding of what goes behind the scene to actually convert a good model to a great model.
Our exotic journey will include the concepts of:
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Comparison between Artificial intelligence, Machine Learning, Deep Learning and Neural Network.
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What is data science and its need.
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The need for machine Learning and introduction to NLP (Natural Language Processing).
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The different types of Machine Learning – Supervised and Unsupervised Learning.
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Hands-on learning of Python from beginner level so that even a non-programmer can begin the journey of Data science with ease.
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All the important libraries you would need to work on Machine learning lifecycle.
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Full-fledged course on Statistics so that you don’t have to take another course for statistics, we cover it all.
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Data cleaning and exploratory Data analysis with all the real life tips and tricks to give you an edge from someone who has just the introductory knowledge which is usually not provided in a beginner course.
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All the mathematics behind the complex Machine learning algorithms provided in a simple language to make it easy to understand and work on in future.
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Hands-on practice on more than 20 different Datasets to give you a quick start and learning advantage of working on different datasets and problems.
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More that 20 assignments and assessments allow you to evaluate and improve yourself on the go.
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Total 10 beginner to Advance level projects so that you can test your skills.
Course Curriculum
Chapter 1: Introduction to Data Science and Machine Learning
Lecture 1: Introduction to Data Science and ML
Lecture 2: ML Process and Types
Chapter 2: Python Basics, Decision Making and Loops
Lecture 1: Python_Installation
Lecture 2: Python Practice Guidelines
Lecture 3: Python_Numbers
Lecture 4: Practice Numbers
Lecture 5: String Operations
Lecture 6: String Slicing
Lecture 7: Practice Strings
Lecture 8: Practice String Functions
Lecture 9: Lists
Lecture 10: Boolean Operations
Lecture 11: If Else Conditions
Lecture 12: For and While Loops
Lecture 13: Functions
Chapter 3: Python Data Structures
Lecture 1: List comprehension
Lecture 2: Dictionaries
Lecture 3: Sets
Lecture 4: Tuples
Lecture 5: Dynamic Function Arguments
Lecture 6: Lambda functions, Map, Reduce, Filter
Chapter 4: Python Practice Questions
Lecture 1: Practice Sets and Dictionary
Lecture 2: List Comprehension Practice
Lecture 3: Functions Practice
Lecture 4: Functions Practice 2
Lecture 5: Practice String and List Comprehension
Lecture 6: Practice Functions
Chapter 5: OOPS
Lecture 1: Intro to OOPS
Lecture 2: OOPS : Without vs with OOPS
Lecture 3: OOPS: classes objects attributes
Lecture 4: OOPS: Methods
Lecture 5: OOPS: Inheritance
Lecture 6: OOPS: Polymorphism
Lecture 7: OOPS: Encapsulation
Lecture 8: Practice OOPS
Lecture 9: Python Assignment
Chapter 6: Descriptive Statistics
Lecture 1: Introduction to Statistics_Population & Sampling
Lecture 2: Measure Of Central Tendencies Mean Median Mode
Lecture 3: Measure Of Variability – Variance Standard Deviation IQR
Lecture 4: Data Diatributions Correlation & Covariance
Lecture 5: Descriptive statistics Practice questions
Chapter 7: Inferential Statistics: Intro, Central Limit Theorem,Z-Score,CI
Lecture 1: Intro to Inferential Statistics
Lecture 2: Variable Types
Lecture 3: Central_Limit_Theorem
Lecture 4: Z-Score
Lecture 5: Confidence Interval
Lecture 6: CI examples
Chapter 8: Hypothesis Testing
Lecture 1: Hypothesis Testing Introduction
Lecture 2: Hypothesis Testing Theory Explained
Lecture 3: Type of Errors and Significant Difference
Chapter 9: T-Test, chi-Square , AnOVa Test and more
Lecture 1: T-Tests
Lecture 2: Chi Square test of Goodness of Fit
Lecture 3: Chi Square test of Independance
Lecture 4: Anova
Lecture 5: Which test to pick
Lecture 6: Statistics Using Graphpad
Chapter 10: Case Study: Statistics on House Pricing Data Set
Lecture 1: Inferential Statistics Case Study
Chapter 11: Data Preparation : Numpy, Pandas, working with DataFrames
Lecture 1: Data Preparation Guidelines
Lecture 2: Data Preparation
Lecture 3: Numpy
Lecture 4: Reading and Writing to files
Lecture 5: Pandas introduction
Lecture 6: Pandas on Dataframe
Lecture 7: Pandas Sorting Merging
Lecture 8: Pandas: Stack unstack melt
Chapter 12: Numerical Analysis, and Data Visualization
Lecture 1: Data Preparation using Pandas
Lecture 2: Data Visualization using Matplotlib and Seaborn
Chapter 13: Case Study: Data Preparation Loans DataSet
Lecture 1: Numerical Summary
Chapter 14: Feature selection and Data Preparation (Structured and Text Data)
Lecture 1: Feature Selection
Lecture 2: Feature Selection Code
Lecture 3: NLP_Text Data preparation
Lecture 4: NLP_hands On
Lecture 5: Data Preparation Assignments and Solutions
Chapter 15: Modelling : Supervised Learning
Lecture 1: Supervised Learning
Lecture 2: Linear Regression Introduction
Lecture 3: Linear_Regression_Cost_Gradient_CV
Lecture 4: Linear Regression_Implementation
Lecture 5: Linear Regression_Regularization
Lecture 6: Logistic Regression: Introduction
Lecture 7: Logistic Regression: Mathematics
Lecture 8: Logistic Regression: Metrics
Lecture 9: Logistic Regression: Code
Lecture 10: Sklearn Metrics: Explained
Lecture 11: Decision Trees: Introduction and Rule generation
Lecture 12: Decision_Tree: Splitting
Instructors
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MG Analytics
Data Scientist and Professional Trainer
Rating Distribution
- 1 stars: 15 votes
- 2 stars: 13 votes
- 3 stars: 47 votes
- 4 stars: 127 votes
- 5 stars: 268 votes
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