Data Science & Machine Learning(Theory+Projects)A-Z 90 HOURS

Data Science Python-Learn Statistics for Data Science, Machine Learning for Data Science, Deep Learning for Data Science

4.25 (652 reviews)
Udemy
platform
English
language
Data Science
category
instructor
5,797
students
94 hours
content
Mar 2024
last update
$69.99
regular price

What you will learn

Key data science and machine learning concepts right from the beginning with a complete unfolding with examples in Python.

Essential Concepts and Algorithms in Machine Learning

Python for Data Science and Data Analysis

Data Understanding and Data Visualization with Python

Probability and Statistics in Python

Feature Engineering and Dimensionality Reduction with Python

Artificial Neural Networks with Python

Convolutional Neural Networks with Python

Recurrent Neural Networks with Python

Detailed Explanation and Live Coding with Python

Building your own AI applications.

Description


Comprehensive Course Description:

Electrification was, without a doubt, the greatest engineering marvel of the 20th century. The electric motor was invented way back in 1821, and the electrical circuit was mathematically analyzed in 1827. But factory electrification, household electrification, and railway electrification all started slowly several decades later.

Fast forward to today. It’s the same story with Artificial Intelligence (AI). The field of AI was formally founded in 1956. But it’s only now—more than six decades later—that AI is expected to revolutionize the way humanity will live and work in the coming decades.

Data science is a large field of study that covers data systems and processes. These systems and processes are aimed at maintaining data sets as well as getting meaning out of them. Machine Learning (ML), a branch of AI, is the concept that systems can automatically learn and adapt from experience without human intervention. ML, essentially, aims to equip machines with independent learning techniques.

Data Science & Machine Learning Full Course in 90 Hours is exhaustive and covers various topics in both these fields in great detail.

Data science specialists use a combination of algorithms, applications, principles, and tools to gain a real sense of random data clusters. You are probably aware that organizations worldwide are generating exponential amounts of data. So, monitoring and storing all this data becomes very difficult. This is where data science plays a vital role by focusing on data modeling and data warehousing.

Both AI and ML are important to data scientists because they can work equally well in both with ease. The expertise of these skilled professionals allows them to switch roles quickly, too. And in the life cycle of a data science project, this can be a critical factor.


What makes this Data Science and Machine Learning course unique?

This learning by doing course provides you with not only a solid theoretical foundation but also practical hands-on training in data science and machine learning. At the end of this course, you will be equipped with the knowledge of all the essential concepts you need to excel as a Data Science professional.

When you take a quick look at the different sections of this all-inclusive course, you may think of these sections as being independent. But that’s not the case. These sections are interlinked and almost sequential. While it’s true that the course is divided into multiple sections, it’s also true that each section is an independent concept, or you can view it as a course on its own.

We have deliberately arranged these sections in a sequence. The reason for this is each subsequent section builds upon the sections you have completed. This framework enables you to explore more independent concepts easily.

Data Science & Machine Learning Full Course in 90 HOURS is crafted to teach you the most in-demand skills in the real world. This course aims to help you understand all the data science and machine learning concepts and methodologies with regards to Python. The course is:

· Comfortably paced.

· Easy to understand.

· Descriptive and expressive.

· Exhaustive.

· Practical with live coding.

· Rich with the most advanced and recently discovered models and breakthroughs by the champions in the AI universe.

This course is designed for beginners, but we will explore complex concepts gradually.

You will find this course interesting, and you will move ahead easily, as it is a compilation of all the basics. You will make quick progress and experience more than what you have learned. At the end of every subsection, you are assigned Home Work/exercises/activities to assess / further strengthen your learning. All this assessment is based on the previous concepts and methods you have learned. Several of these assessment tasks will be coding based, as the main aim is to get you up and proceed to implementations.

Data Science is doubtless a rewarding career. You get to resolve some of the most interesting data issues and earn a handsome salary package for your efforts. After you finish Data Science & Machine Learning Full Course in 90 HOURS, you will be able to easily tackle real-world problems and ensure steady career growth.

Unlike other courses, this comprehensive course is not expensive. In fact, you can learn all the concepts and methodologies of Data Science and Machine Learning at a fraction of the cost. Our tutorials are divided into 700+ brief HD videos along with detailed code notebooks.

Enroll in this course and start your learning journey in Data Science and Machine Learning. This course really simplifies all the complex concepts for you. You will not find an easier course that inspires you as much along your learning journey.


Teaching is our passion:

We work meticulously to create online tutorials with instructors who are willing to share their expertise and help you in understanding all the concepts. The aim is to create a strong basic understanding for you before you move onward to the advanced version. Detailed course notes, high-quality video content, learning assessment questions, meaningful course material, and subject-related handouts are some of the perks of this course. You are also assured of the support of a dedicated instructor every step of the way. You can approach our team in case of any queries.

Course content:

1. Python for Data Science and Data Analysis

a. You start with problem-solving and finish with fancy indexing and plots in Matplotlib.

b. No prior knowledge in any computer science language is assumed.

c. Great fun with Python language.

d. Reasonable treatment of data science packages (NumPy, Pandas, Matplotlib, Seaborn, and Sklearn).

e. After this course, you will be a competent Python programmer as well as a reasonable expert of data science packages (NumPy, Pandas, Matplotlib).

f. This section is designed to teach you programming in general also. Therefore, shifting from this language to any other language after this section is not difficult.

2. Data Understanding and Data Visualization with Python

a. This section deals with the in-depth treatment of data science packages both for data manipulation as well as data visualization.

b. While Section 1 focuses more on Python language, this section focuses completely on data science packages and their efficient use.

c. The packages covered in this section include NumPy, Pandas, Matplotlib, Seaborn, Bokeh, Plotly, and Folium.

d. As far as we know, this is the most comprehensive section on data understanding and visualization among the available ones.

e. Further, this section is designed to reduce the dependency on core Python language to be treated independently, as well.

f. 2D and 3D visualizations, interactive visualizations, and geographic maps are also covered.

g. Proceeding in data science with being able to effectively play with the data using famous packages makes progress much worse, and this section addresses this concern.

3. Mastering Probability and Statistics in Python

a. Obviously, concepts in data science are not new. In fact, it is also believed that data science is merely a renamed version of Probability and Statistics. Well, without being biased to that extent, we will say that the practical nature of applications was uncovered earlier even though the theory traces back to Probability and Statistics.

b. One way or the other, knowing Probability and Statistics makes a significant theoretical as well as practical difference.

c. Most of the courses on Probability and Statistics, however, fail to link the data science practices and theory by merely focusing on the axiomatic treatment of the subject.

d. We build this section by keeping the practical needs of data science in mind as well as the importance of theory.

e. Wherever important, we deliberately explain and show the relationships by derivations and even through Python Code.

f. This section builds a very sound basis for understanding the classical concepts in data science as well as its more recent generalizations.

g. We start with the very basics of Probability, go through inference and estimations, link famous machine learning techniques with conditional probability, and finally, show that Deep Neural Networks indeed learn a probability function eventually.

4. Machine Learning Crash Course

a. Although several concepts, or even all, fall under the umbrella of Probability and Statistics, it turns out that most of the concepts have made their own practical place, mostly derived through engineering, with the name of Machine Learning. For example, the term “overfitting” is now referring to the area of machine learning.

b. Machine Learning brings its own set of practices to reach the demands of automation. Hence, mastering these concepts becomes inevitable.

c. This section is actually a quick walkthrough of the concepts in Machine Learning and focuses on all the theoretical as well as practical concepts.

d. We mostly cover applications using the Sklearn Python package and build machine learning pipelines in this section.

e. We also elaborate on more advanced areas of machine learning, which we later present as separate sections.

5. Feature Engineering and Dimensionality Reduction with Python

a. Knowing the sections you have covered thus far certainly brings you a huge clarity of the field. But there is still one thing that brings the improvements in the results with a reasonable margin, and that is data preprocessing or data preparation.

b. Most of the data science today relies on preparing the data suitable for machine learning models. An effective way of data preparation, most of the time, becomes a game-changer.

c. This section focuses on data preparation for machine learning models.

d. We build this section to provide an understanding of why selecting features and transforming features are important.

e. We also discuss practical issues with real data, like missing values and non-numeric data.

f. We discuss the performance improvements both in terms of execution time as well as the accuracy of the models.

g. We explain the required mathematical background in a simple way.

h. Finally, all the concepts are made more easily understandable by coding relevant examples in Python.

6. Artificial Neural Networks with Python

a. With the availability of a huge quantity of data as well as computation power, a relatively old machine learning model, Artificial Neural Network turns out to be the game-changer in data science.

b. Artificial Neural Network can approximate almost any pattern in the data. Further, it has a much greater data utilization capacity as compared to the more classical methods.

c. With the recent rise of ANNs, a lot of practical techniques are also discovered, particularly for ANNs.

d. Also, working with a large amount of data brings its own challenges for learning algorithms.

e. In this section, we address all these concerns and cover ANNs in depth.

f. We also introduce another framework, “TensorFlow,” for working in ANNs.

g. With this section in hand, you can now target much larger machine learning problems.

7. Convolutional Neural Networks with Python

a. ANNs, in its most basic form, is not that suitable for image data and for the problems in computer vision.

b. Convolutional Neural Networks (CNNs) are considered a game-changer in the field of computer vision. CNNs are not limited to images only. You’ll find them everywhere now, from audio processing to more advanced reinforcement learning (i.e., Resnets in AlphaZero). So, the understanding of CNNs becomes inevitable in all the fields of data science. Even most of the Recurrent Neural Networks (RNNs) rely on CNNs nowadays.

c. In this section, you will to learn about:

i. The significance of CNNs in data science.

ii. The reasons to shift to CNNs from hand engineering (classical computer vision).

iii. The major concepts from the absolute beginning with complete unfolding with examples in Python.

iv. Practical explanation and live coding with Python.

v. Evolution of CNNs — LeNet (1990s) to MobileNets (2020s).

vi. Intricate details of CNNs including examples of training CNNs.

vii. TensorFlow (Google’s deep learning framework).

viii. The use and applications of CNNs (with implementations in framework TensorFlow) that are more recent and advanced in terms of accuracy and efficiency.

ix. The use and applications of pre-trained CNNs (with implementations in framework TensorFlow) for transfer learning on your own dataset.

x. Building your own applications for Human Face-Verification and Neural Style Transfer.



After completing this course successfully, you will be able to:

  • · Relate the concepts, principles, and theories in Data Science & Machine Learning.

  • · Understand the methodology of Data Science & Machine Learning using real datasets.


Who this course is for:

  • · People who want to become perfect in their data speak.

  • · People who want to learn Data Science & Machine Learning with real datasets in Data Science.

  • · People from a non-engineering background who want to enter the Data Science field.

  • · People who want to enter the Machine Learning field.

  • · Individuals who are passionate about numbers and programming.

  • · People who want to learn Data Science & Machine Learning along with its implementation in realistic projects.

  • · Data Scientists.

  • · Business Analysts.

Content

Introduction to the Course

Introduction to Courses and Instructor
Introduction to the Course: Feedbacks and Review
Link to Github to get the Python Notebooks

Basics for Data Science: Python for Data Science and Data Analysis

Introduction to the Course: Focus of the Course-Part 1
Introduction to the Course: Focus of the Course-Part 2
Basics of Programming: Understanding the Algorithm
Basics of Programming: FlowCharts and Pseudocodes
Basics of Programming: Example of Algorithms- Making Tea Problem
Basics of Programming: Example of Algorithms-Searching Minimun
Basics of Programming: Example of Algorithms-Sorting Problem
Basics of Programming: Sorting Problem in Python
Why Python and Jupyter Notebook: Why Python
Why Python and Jupyter Notebook: Why Jupyter Notebooks
Installation of Anaconda and IPython Shell: Installing Python and Jupyter Anacon
Installation of Anaconda and IPython Shell: Your First Python Code- Hello World
Installation of Anaconda and IPython Shell: Coding in IPython Shell
Variable and Operator: Variables
Variable and Operator: Operators
Variable and Operator: Variable Name Quiz
Variable and Operator: Bool Data Type in Python
Variable and Operator: Comparison in Python
Variable and Operator: Combining Comparisons in Python
Variable and Operator: Combining Comparisons Quiz
Python Useful function: Python Function- Round
Python Useful function: Python Function- Divmod
Python Useful function: Python Function- Is instance and PowFunctions
Python Useful function: Python Function- Input
Control Flow in Python: If Python Condition
Control Flow in Python: if Elif Else Python Conditions
Control Flow in Python: More on if Elif Else Python Conditions
Control Flow in Python: Indentations
Control Flow in Python: Comments and Problem Solving Practice With If
Control Flow in Python: While Loop
Control Flow in Python: While Loop break Continue
Control Flow in Python: For Loop
Control Flow in Python: Else In For Loop
Control Flow in Python: Loops Practice-Sorting Problem
Function and Module in Python: Functions in Python
Function and Module in Python: DocString
Function and Module in Python: Input Arguments
Function and Module in Python: Multiple Input Arguments
Function and Module in Python: Ordering Multiple Input Arguments
Function and Module in Python: Output Arguments and Return Statement
Function and Module in Python: Function Practice-Output Arguments and Return Statement
Function and Module in Python: Variable Number of Input Arguments
Function and Module in Python: Variable Number of Input Arguments as Dictionary
Function and Module in Python: Default Values in Python
Function and Module in Python: Modules in Python
Function and Module in Python: Making Modules in Python
Function and Module in Python: Function Practice-Sorting List in Python
String in Python: Strings
String in Python: Multi Line Strings
String in Python: Indexing Strings
String in Python: String Methods
String in Python: String Escape Sequences
Data Structure (List, Tuple, Set, Dictionary): Introduction to Data Structure
Data Structure (List, Tuple, Set, Dictionary): Defining and Indexing
Data Structure (List, Tuple, Set, Dictionary): Insertion and Deletion
Data Structure (List, Tuple, Set, Dictionary): Python Practice-Insertion and Deletion
Data Structure (List, Tuple, Set, Dictionary): Deep Copy or Reference Slicing
Data Structure (List, Tuple, Set, Dictionary): Exploring Methods Using TAB Completion
Data Structure (List, Tuple, Set, Dictionary): Data Structure Abstract Ways
Data Structure (List, Tuple, Set, Dictionary): Data Structure Practice
NumPy for Numerical Data Processing: Introduction to NumPy
NumPy for Numerical Data Processing: NumPy Dimensions
NumPy for Numerical Data Processing: NumPy Shape, Size and Bytes
NumPy for Numerical Data Processing: Arange, Random and Reshape-Part 1
NumPy for Numerical Data Processing: Arange, Random and Reshape-Part 2
NumPy for Numerical Data Processing: Slicing-Part 1
NumPy for Numerical Data Processing: Slicing-Part 2
NumPy for Numerical Data Processing: NumPy Masking
NumPy for Numerical Data Processing: NumPy BroadCasting and Concatination
NumPy for Numerical Data Processing: NumPy ufuncs Speed Test
Pandas for Data Manipulation: Introduction to Pandas
Pandas for Data Manipulation: Pandas Series
Pandas for Data Manipulation: Pandas Data Frame
Pandas for Data Manipulation: Pandas Missing Values
Pandas for Data Manipulation: Pandas .loc and .iloc
Pandas for Data Manipulation: Pandas Practice-Using COVID19 Data -Part 1
Pandas for Data Manipulation: Pandas Practice-Using COVID19 Data -Part 2
Matplotlib, Seaborn, and Bokeh for Data Visualization: Introduction to Matplotlib
Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Vs. Matplotlib Style
Matplotlib, Seaborn, and Bokeh for Data Visualization: Histograms Kdeplot
Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Pairplot and Jointplot
Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Pairplot using Iris Data
Matplotlib, Seaborn, and Bokeh for Data Visualization: Introduction to Bokeh
Matplotlib, Seaborn, and Bokeh for Data Visualization: Bokeh Gridplot
Scikit-Learn for Machine Learning: Introduction to Scikit-Learn
Scikit-Learn for Machine Learning: Scikit-Learn for Linear Regression
Scikit-Learn for Machine Learning: Scikit-Learn for SVM and Random Forests
Scikit-Learn for Machine Learning: ScikitLearn- Trend Analysis COVID19
Scikit-Learn for Machine Learning: THANK YOU Bonus Video

Basics for Data Science: Data Understanding and Data Visualization with Python

Introduction to the Course: Focus of the Course
Introduction to the Course: Content of the Course
Introduction to the Course: Request for Your Honest Review
NumPy for Numerical Data Processing: Ufuncs Add, Sum and Plus Operators
NumPy for Numerical Data Processing: Ufuncs Subtract Power Mod
NumPy for Numerical Data Processing: Ufuncs Comparisons Logical Operators
NumPy for Numerical Data Processing: Ufuncs Output Argument
NumPy for Numerical Data Processing: NumPy Playing with Images
NumPy for Numerical Data Processing: NumPy KNN Classifier fromScratch
NumPy for Numerical Data Processing: NumPy Structured Arrays
Pandas for Data Manipulation and Understanding: Introduction to Pandas
Pandas for Data Manipulation and Understanding: Pandas Series
Pandas for Data Manipulation and Understanding: Pandas DataFrame
Pandas for Data Manipulation and Understanding: Pandas Missing Values
Pandas for Data Manipulation and Understanding: Pandas Loc Iloc
Pandas for Data Manipulation and Understanding: Pandas in Practice
Pandas for Data Manipulation and Understanding: Pandas Group by
Pandas for Data Manipulation and Understanding: Hierarchical Indexing
Pandas for Data Manipulation and Understanding: Pandas Rolling
Pandas for Data Manipulation and Understanding: Pandas Where
Pandas for Data Manipulation and Understanding: Pandas Clip
Pandas for Data Manipulation and Understanding: Pandas Merge
Pandas for Data Manipulation and Understanding: Pandas Pivot Table
Pandas for Data Manipulation and Understanding: Pandas Strings
Pandas for Data Manipulation and Understanding: Pandas DateTime
Pandas for Data Manipulation and Understanding: Pandas Hands On COVID19 Data
Pandas for Data Manipulation and Understanding: Pandas Hands On COVID19 Data Bug
Matplotlib for Data Visualization: Introduction to Matplotlib
Matplotlib for Data Visualization: Matplotlib Multiple Plots
Matplotlib for Data Visualization: Matplotlib Colors and Styles
Matplotlib for Data Visualization: Matplotlib Colors and Styles Shortcuts
Matplotlib for Data Visualization: Matplotlib Axis Limits
Matplotlib for Data Visualization: Matplotlib Legends Labels
Matplotlib for Data Visualization: Matplotlib Set Function
Matplotlib for Data Visualization: Matplotlib Markers
Matplotlib for Data Visualization: Matplotlib Markers Randomplots
Matplotlib for Data Visualization: Matplotlib Scatter Plot
Matplotlib for Data Visualization: Matplotlib Contour Plot
Matplotlib for Data Visualization: Matplotlib Histograms
Matplotlib for Data Visualization: Matplotlib Subplots
Matplotlib for Data Visualization: Matplotlib 3D Introduction
Matplotlib for Data Visualization: Matplotlib 3D Scatter Plots
Matplotlib for Data Visualization: Matplotlib 3D Surface Plots
Seaborn for Data Visualization: Introduction to Seaborn
Seaborn for Data Visualization: Seaborn Relplot
Seaborn for Data Visualization: Seaborn Relplot Kind Line
Seaborn for Data Visualization: Seaborn Relplot Facets
Seaborn for Data Visualization: Seaborn Catplot
Seaborn for Data Visualization: Seaborn Heatmaps
Bokeh for Interactive Plotting: Introduction to Bokeh
Bokeh for Interactive Plotting: Bokeh Multiplots Markers
Bokeh for Interactive Plotting: Bokeh Multiplots Grid Plot
Plotly for 3D Interactive Plotting: Plotly 3D Interactive Scatter Plot
Plotly for 3D Interactive Plotting: Plotly 3D Interactive Surface Plot
Geographic Maps with Folium: Geographic Maps with Folium using COVID-19 Data
Pandas for Plotting: Pandas for Plotting
Pandas for Plotting: THANK YOU Bonus Video

Basics for Data Science: Mastering Probability and Statistics in Python

Introduction to Course: Focus of the Course
Introduction to Course: Request for Your Honest Review
Probability vs Statistics: Probability vs Statistics
Sets: Definition of Set
Sets: Cardinality of a Set
Sets: Subsets PowerSet UniversalSet
Sets: Python Practice Subsets
Sets: PowerSets Solution
Sets: Operations
Sets: Python Practice Operations
Sets: VennDiagrams Operations
Sets: Homework
Experiment: Random Experiment
Experiment: Outcome and Sample Space
Experiment: Event
Experiment: Recap and Homework
Probability Model: Probability Model
Probability Model: Probability Axioms
Probability Model: Probability Axioms Derivations
Probability Model: Probablility Models Example
Probability Model: Probablility Models More Examples
Probability Model: Probablility Models Continous
Probability Model: Conditional Probability
Probability Model: Conditional Probability Example
Probability Model: Conditional Probability Formula
Probability Model: Conditional Probability in Machine Learning
Probability Model: Conditional Probability Total Probability Theorem
Probability Model: Probablility Models Independence
Probability Model: Probablility Models Conditional Independence
Probability Model: Probablility Models BayesRule
Probability Model: Probablility Models towards Random Variables
Probability Model: HomeWork
Random Variables: Introduction
Random Variables: Random Variables Examples
Random Variables: Bernulli Random Variables
Random Variables: Bernulli Trail Python Practice
Random Variables: Geometric Random Variable
Random Variables: Geometric Random Variable Normalization Proof Optional
Random Variables: Geometric Random Variable Python Practice
Random Variables: Binomial Random Variables
Random Variables: Binomial Python Practice
Random Variables: Random Variables in Real DataSets
Random Variables: Homework
Continous Random Variables: Zero Probability to Individual Values
Continous Random Variables: Probability Density Functions
Continous Random Variables: Uniform Distribution
Continous Random Variables: Uniform Distribution Python
Continous Random Variables: Exponential
Continous Random Variables: Exponential Python
Continous Random Variables: Gaussian Random Variables
Continous Random Variables: Gaussian Python
Continous Random Variables: Transformation of Random Variables
Continous Random Variables: Homework
Expectations: Definition
Expectations: Sample Mean
Expectations: Law of Large Numbers
Expectations: Law of Large Numbers Famous Distributions
Expectations: Law of Large Numbers Famous Distributions Python
Expectations: Variance
Expectations: Homework
Project Bayes Classifier: Project Bayes Classifier From Scratch
Multiple Random Variables: Joint Distributions
Multiple Random Variables: Multivariate Gaussian
Multiple Random Variables: Conditioning Independence
Multiple Random Variables: Classification
Multiple Random Variables: Naive Bayes Classification
Multiple Random Variables: Regression
Multiple Random Variables: Curse of Dimensionality
Multiple Random Variables: Homework
Optional Estimation: Parametric Distributions
Optional Estimation: MLE
Optional Estimation: LogLiklihood
Optional Estimation: MAP
Optional Estimation: Logistic Regression
Optional Estimation: Ridge Regression
Optional Estimation: DNN
Mathematical Derivations for Math Lovers (Optional): Permutations
Mathematical Derivations for Math Lovers (Optional): Combinations
Mathematical Derivations for Math Lovers (Optional): Binomial Random Variable
Mathematical Derivations for Math Lovers (Optional): Logistic Regression Formulation
Mathematical Derivations for Math Lovers (Optional): Logistic Regression Derivation
Mathematical Derivations for Math Lovers (Optional): THANK YOU Bonus Video

Machine Learning: Machine Learning Crash Course

Introduction to the Course: Focus of the Course
Introduction to the Course: Python Practical of the Course
Introduction to the Course: Your Feedback and Review
Why Machine Learning: Machine Learning Applications-Part 1
Why Machine Learning: Machine Learning Applications-Part 2
Why Machine Learning: Why Machine Learning is Trending Now
Process of Learning from Data: Supervised Learning
Process of Learning from Data: UnSupervised Learning and Reinforcement Learning
Machine Learning Methods: Features
Machine Learning Methods: Features Practice with Python
Machine Learning Methods: Regression
Machine Learning Methods: Regression Practice with Python
Machine Learning Methods: Classsification
Machine Learning Methods: Classification Practice with Python
Machine Learning Methods: Clustering
Machine Learning Methods: Clustering Practice with Python
Data Preparation and Preprocessing: Handling Image Data
Data Preparation and Preprocessing: Handling Video and Audio Data
Data Preparation and Preprocessing: Handling Text Data
Data Preparation and Preprocessing: One Hot Encoding
Data Preparation and Preprocessing: Data Standardization
Machine Learning Models and Optimization: Machine Learning Model 1
Machine Learning Models and Optimization: Machine Learning Model 2
Machine Learning Models and Optimization: Machine Learning Model 3
Machine Learning Models and Optimization: Training Process, Error, Cost and Loss
Machine Learning Models and Optimization: Optimization
Building Machine Learning Model from Scratch: Linear Regression from Scratch- Part 1
Building Machine Learning Model from Scratch: Linear Regression from Scratch- Part 2
Building Machine Learning Model from Scratch: Minimun-to-mean Distance Classifier from Scratch- Part 1
Building Machine Learning Model from Scratch: Minimun-to-mean Distance Classifier from Scratch- Part 2
Building Machine Learning Model from Scratch: K-means Clustering from Scratch- Part 1
Building Machine Learning Model from Scratch: K-means Clustering from Scratch- Part 2
Overfitting, Underfitting and Generalization: Overfitting Introduction
Overfitting, Underfitting and Generalization: Overfitting example on Python
Overfitting, Underfitting and Generalization: Regularization
Overfitting, Underfitting and Generalization: Generalization
Overfitting, Underfitting and Generalization: Data Snooping and the Test Set
Overfitting, Underfitting and Generalization: Cross-validation
Machine Learning Model Performance Metrics: The Accuracy
Machine Learning Model Performance Metrics: The Confusion Matrix
Dimensionality Reduction: The Curse of Dimensionality
Dimensionality Reduction: The Principal Component Analysis (PCA)
Deep Learning Overview: Introduction to Deep Neural Networks (DNN)
Deep Learning Overview: Introduction to Convolutional Neural Networks (CNN)
Deep Learning Overview: Introduction to Recurrent Neural Networks (CNN)
Hands-on Machine Learning Project Using Scikit-Learn: Principal Component Analysis (PCA) with Python
Hands-on Machine Learning Project Using Scikit-Learn: Pipeline in Scikit-Learn for Machine Learning Project
Hands-on Machine Learning Project Using Scikit-Learn: Cross-validation with Python
Hands-on Machine Learning Project Using Scikit-Learn: Face Recognition Project with Python
Hands-on Machine Learning Project Using Scikit-Learn: THANK YOU Bonus Video
OPTIONAL Section- Mathematics Wrap-up: Mathematical Wrap-up on Machine Learning

Machine Learning: Feature Engineering and Dimensionality Reduction with Python

Introduction: Focus of the Course
Introduction: Request for Your Honest Review
Features in Data Science: Introduction to Feature in Data Science
Features in Data Science: Marking Facial Features
Features in Data Science: Feature Space
Features in Data Science: Features Dimensions
Features in Data Science: Features Dimensions Activity
Features in Data Science: Why Dimensionality Reduction
Features in Data Science: Activity-Dimensionality Reduction
Features in Data Science: Feature Dimensionality Reduction Methods
Feature Selection: Why Feature Selection
Feature Selection: Feature Selection Methods
Feature Selection: Filter Methods
Feature Selection: Wrapper Methods
Feature Selection: Embedded Methods
Feature Selection: Search Strategy
Feature Selection: Search Strategy Activity
Feature Selection: Statistical Based Methods
Feature Selection: Information Theoratic Methods
Feature Selection: Similarity Based Methods Introduction
Feature Selection: Similarity Based Methods Criteria
Feature Selection: Activity- Feature Selection in Python
Feature Selection: Activity- Feature Selection
Mathematical Foundation: Introduction to Mathematical Foundation of Feature Selection
Mathematical Foundation: Closure Of A Set
Mathematical Foundation: Linear Combinations
Mathematical Foundation: Linear Independence
Mathematical Foundation: Vector Space
Mathematical Foundation: Basis and Dimensions
Mathematical Foundation: Coordinates vs Dimensions
Mathematical Foundation: SubSpace
Mathematical Foundation: Orthonormal Basis
Mathematical Foundation: Matrix Product
Mathematical Foundation: Least Squares
Mathematical Foundation: Rank
Mathematical Foundation: Eigen Space
Mathematical Foundation: Positive Semi Definite Matrix
Mathematical Foundation: Singular Value Decomposition SVD
Mathematical Foundation: Lagrange Multipliers
Mathematical Foundation: Vector Derivatives
Mathematical Foundation: Linear Algebra Module Python
Mathematical Foundation: Activity-Linear Algebra Module Python
Feature Extraction: Feature Extraction Introduction
Feature Extraction: PCA Introduction
Feature Extraction: PCA Criteria
Feature Extraction: PCA Properties
Feature Extraction: PCA Max Variance Formulation
Feature Extraction: PCA Derivation
Feature Extraction: PCA Implementation
Feature Extraction: PCA For Small Sample Size Problems(DualPCA)
Feature Extraction: PCA vs SVD
Feature Extraction: Kernel PCA
Feature Extraction: Kernel PCA vs ISOMAP
Feature Extraction: Kernel PCA vs The Rest
Feature Extraction: Encoder Decoder Networks For Dimensionality Reduction vs kernel PCA
Feature Extraction: Supervised PCA and Fishers Linear Discriminant Analysis
Feature Extraction: Supervised PCA and Fishers Linear Discriminant Analysis Activity
Feature Extraction: Dimensionality Reduction Pipelines Python Project
Feature Engineering: Categorical Features
Feature Engineering: Categorical Features Python
Feature Engineering: Text Features
Feature Engineering: Image Features
Feature Engineering: Derived Features
Feature Engineering: Derived Features Histogram Of Gradients Local Binary Patterns
Feature Engineering: Feature Scaling
Feature Engineering: Activity-Feature Scaling
Feature Engineering: THANK YOU Bonus Video

Deep learning: Artificial Neural Networks with Python

Introduction to the Course: Why Deep learning Networks (DNN)
Introduction to the Course: Feedbacks and Review
Deep Neural Networks and Deep Learning Basics: Introduction to Artificial Neural Networks
Deep Neural Networks and Deep Learning Basics: Neuron and Perceptron
Deep Neural Networks and Deep Learning Basics: Deep Neural Network Architecture
Deep Neural Networks and Deep Learning Basics: FeedForward fully Connected MLP
Deep Neural Networks and Deep Learning Basics: Calculating Number of weights of DNN
Deep Neural Networks and Deep Learning Basics: Number Of Neurons Vs Number Of Layers
Deep Neural Networks and Deep Learning Basics: Discriminative Vs Generative Learning
Deep Neural Networks and Deep Learning Basics: Universal Approximation Theorem
Deep Neural Networks and Deep Learning Basics: Why Depth
Deep Neural Networks and Deep Learning Basics: Decision Boundary in DNN
Deep Neural Networks and Deep Learning Basics: Bias Term
Deep Neural Networks and Deep Learning Basics: The Activation Function
Deep Neural Networks and Deep Learning Basics: DNN Training Parameters
Deep Neural Networks and Deep Learning Basics: Gradient Descent
Deep Neural Networks and Deep Learning Basics: Backpropagation
Deep Neural Networks and Deep Learning Basics: Training DNN Animantion
Deep Neural Networks and Deep Learning Basics: Weigth Initialization
Deep Neural Networks and Deep Learning Basics: Batch MiniBatch Stocastic
Deep Neural Networks and Deep Learning Basics: Batch Normalization
Deep Neural Networks and Deep Learning Basics: Rprop Momentum
Deep Neural Networks and Deep Learning Basics: convergence Animation
Deep Neural Networks and Deep Learning Basics: Drop Out Early Stopping Hyperparameters
Python for Data Science: Python Packages for Data Science
Python for Data Science: NumPy Pandas and Matplotlib (Part 1)
Python for Data Science: NumPy Pandas and Matplotlib (Part 2)
Python for Data Science: NumPy Pandas and Matplotlib (Part 3)
Python for Data Science: NumPy Pandas and Matplotlib (Part 4)
Python for Data Science: NumPy Pandas and Matplotlib (Part 5)
Python for Data Science: NumPy Pandas and Matplotlib (Part 6)
Python for Data Science: DataSet Preprocessing
Python for Data Science: TensorFlow for classification
Implementation of DNN for COVID 19 Analysis: COVID19 Data Analysis
Implementation of DNN for COVID 19 Analysis: COVID19 Regression with TensorFlow
Implementation of DNN for COVID 19 Analysis: THANK YOU Bonus Video

Deep learning: Convolutional Neural Networks with Python

Introduction: Why CNN
Introduction: Focus of the Course
Introduction: Request for Your Honest Review
Image Processing: Gray Scale Images
Image Processing: RGB Images
Image Processing: Reading and Showing Images in Python
Image Processing: Converting an Image to Grayscale in Python
Image Processing: Image Formation
Image Processing: Image Blurring 1
Image Processing: Image Blurring 2
Image Processing: General Image Filtering
Image Processing: Convolution
Image Processing: Edge Detection
Image Processing: Image Sharpening
Image Processing: Implementation of Image Blurring Edge Detection Image Sharpening in Python
Image Processing: Parameteric Shape Detection
Image Processing: Image Processing Activity
Object Detection: Introduction to Object Detection
Object Detection: Classification PipleLine
Object Detection: Sliding Window Implementation
Object Detection: Shift Scale Rotation Invariance
Object Detection: Person Detection
Object Detection: HOG Features
Object Detection: Hand Engineering vs CNNs
Object Detection: Object Detection Activity
Deep Neural Network Architecture: Convolution Revisited
Deep Neural Network Architecture: Implementing Convolution in Python Revisited
Deep Neural Network Architecture: Why Convolution
Deep Neural Network Architecture: Filters Padding Strides
Deep Neural Network Architecture: Pooling Tensors
Deep Neural Network Architecture: CNN Example
Deep Neural Network Architecture: Convolution and Pooling Details
Deep Neural Network Architecture: NonVectorized Implementations of Conv2d and Pool2d
Deep Neural Network Architecture: Deep Neural Network Architecture Activity
Gradient Descent in CNNs: Example Setup
Gradient Descent in CNNs: Why Derivaties
Gradient Descent in CNNs: What is Chain Rule
Gradient Descent in CNNs: Applying Chain Rule
Gradient Descent in CNNs: Gradients of Convolutional Layer
Gradient Descent in CNNs: Extending To Multiple Filters
Gradient Descent in CNNs: Gradients of MaxPooling Layer
Gradient Descent in CNNs: Extending to Multiple Layers
Gradient Descent in CNNs: Implementation in Numpy ForwardPass.mp4.
Gradient Descent in CNNs: Implementation in Numpy BackwardPass 1
Gradient Descent in CNNs: Implementation in Numpy BackwardPass 2
Gradient Descent in CNNs: Implementation in Numpy BackwardPass 3
Gradient Descent in CNNs: Implementation in Numpy BackwardPass 4
Gradient Descent in CNNs: Implementation in Numpy BackwardPass 5
Gradient Descent in CNNs: Gradient Descent in CNNs Activity
Introduction to TensorFlow: Introduction
Introduction to TensorFlow: FashionMNIST Example Plan Neural Network
Introduction to TensorFlow: FashionMNIST Example CNN
Introduction to TensorFlow: Introduction to TensorFlow Activity
Classical CNNs: LeNet
Classical CNNs: AlexNet
Classical CNNs: VGG
Classical CNNs: InceptionNet
Classical CNNs: GoogLeNet
Classical CNNs: Resnet
Classical CNNs: Classical CNNs Activity
Transfer Learning: What is Transfer learning
Transfer Learning: Why Transfer Learning
Transfer Learning: ImageNet Challenge
Transfer Learning: Practical Tips
Transfer Learning: Project in TensorFlow
Transfer Learning: Transfer Learning Activity
Yolo: Image Classfication Revisited
Yolo: Sliding Window Object Localization
Yolo: Sliding Window Efficient Implementation
Yolo: Yolo Introduction
Yolo: Yolo Training Data Generation
Yolo: Yolo Anchor Boxes
Yolo: Yolo Algorithm
Yolo: Yolo Non Maxima Supression
Yolo: RCNN
Yolo: Yolo Activity
Face Verification: Problem Setup
Face Verification: Project Implementation
Face Verification: Face Verification Activity
Neural Style Transfer: Problem Setup
Neural Style Transfer: Implementation Tensorflow Hub
Neural Style Transfer: THANK YOU Bonus Video

Deep learning: Recurrent Neural Networks with Python

Introduction to Course: Focus of the Course
Introduction to Course: Request for Your Honest Review
Applications of RNN (Motivation): Human Activity Recognition
Applications of RNN (Motivation): Image Captioning
Applications of RNN (Motivation): Machine Translation
Applications of RNN (Motivation): Speech Recognition
Applications of RNN (Motivation): Stock Price Predictions
Applications of RNN (Motivation): When to Model RNN
Applications of RNN (Motivation): Activity
RNN Architecture: Introduction to Module
RNN Architecture: Fixed Length Memory Model
RNN Architecture: Infinite Memory Architecture
RNN Architecture: Weight Sharing
RNN Architecture: Notations
RNN Architecture: ManyToMany Model
RNN Architecture: OneToMany Model
RNN Architecture: ManyToOne Model
RNN Architecture: Activity Many to One
RNN Architecture: ManyToMany Different Sizes Model
RNN Architecture: Activity Many to Many Nmt
RNN Architecture: Models Summary
RNN Architecture: Deep RNNs
Gradient Decsent in RNN: Introduction to Gradient Descent Module
Gradient Decsent in RNN: Example Setup
Gradient Decsent in RNN: Equations
Gradient Decsent in RNN: Loss Function
Gradient Decsent in RNN: Why Gradients
Gradient Decsent in RNN: Chain Rule
Gradient Decsent in RNN: Chain Rule in Action
Gradient Decsent in RNN: BackPropagation Through Time
Gradient Decsent in RNN: Activity
Vanishing Gradients in RNN: Introduction to Better RNNs Module
Vanishing Gradients in RNN: Introduction Vanishing Gradients in RNN
Vanishing Gradients in RNN: GRU
Vanishing Gradients in RNN: GRU Optional
Vanishing Gradients in RNN: LSTM
Vanishing Gradients in RNN: LSTM Optional
Vanishing Gradients in RNN: Bidirectional RNN
Vanishing Gradients in RNN: Attention Model
Vanishing Gradients in RNN: Attention Model Optional
TensorFlow: Introduction to TensorFlow
TensorFlow: TensorFlow Text Classification Example using RNN
Project I_ Book Writer: Introduction
Project I_ Book Writer: Data Mapping
Project I_ Book Writer: Modling RNN Architecture
Project I_ Book Writer: Modling RNN Model in TensorFlow
Project I_ Book Writer: Modling RNN Model Training
Project I_ Book Writer: Modling RNN Model Text Generation
Project I_ Book Writer: Activity
Project II_ Stock Price Prediction: Problem Statement
Project II_ Stock Price Prediction: Data Set
Project II_ Stock Price Prediction: Data Prepration
Project II_ Stock Price Prediction: RNN Model Training and Evaluation
Project II_ Stock Price Prediction: Activity
Further Readings and Resourses: Further Readings and Resourses 1
Bonus Lecture: THANK YOU Bonus Video

Reinforcement Learning

Motivation Reinforcement Learning: What is Reinforcement Learning
Motivation Reinforcement Learning: What is Reinforcement Learning Hiders and Seekers by OpenAI
Motivation Reinforcement Learning: RL vs Other ML Frameworks
Motivation Reinforcement Learning: Why Reinforcement Learning
Motivation Reinforcement Learning: Examples of Reinforcement Learning
Motivation Reinforcement Learning: Limitations of Reinforcement Learning
Motivation Reinforcement Learning: Exercises
Terminology of Reinforcement Learning: What is Environment
Terminology of Reinforcement Learning: What is Environment_2
Terminology of Reinforcement Learning: What is Agent
Terminology of Reinforcement Learning: What is State
Terminology of Reinforcement Learning: State Belongs to Environment and not to Agent
Terminology of Reinforcement Learning: What is Action
Terminology of Reinforcement Learning: What is Reward
Terminology of Reinforcement Learning: Goal
Terminology of Reinforcement Learning: Policy
Terminology of Reinforcement Learning: Summary
GridWorld Example: Setup 1
GridWorld Example: Setup 2
GridWorld Example: Setup 3
GridWorld Example: Policy Comparison
GridWorld Example: Deterministic Environment
GridWorld Example: Stochastic Environment
GridWorld Example: Stochastic Environment 2
GridWorld Example: Stochastic Environment 3
GridWorld Example: Non Stationary Environment
GridWorld Example: GridWorld Summary
GridWorld Example: Activity
Markov Decision Process Prerequisites: Probability
Markov Decision Process Prerequisites: Probability 2
Markov Decision Process Prerequisites: Probability 3
Markov Decision Process Prerequisites: Conditional Probability
Markov Decision Process Prerequisites: Conditional Probability Fun Example
Markov Decision Process Prerequisites: Joint Probability
Markov Decision Process Prerequisites: Joint probability 2
Markov Decision Process Prerequisites: Joint Probability 3
Markov Decision Process Prerequisites: Expected Value
Markov Decision Process Prerequisites: Conditional Expectation
Markov Decision Process Prerequisites: Modeling Uncertainity of Environment
Markov Decision Process Prerequisites: Modeling Uncertainity of Environment 2
Markov Decision Process Prerequisites: Modeling Uncertainity of Environment 3
Markov Decision Process Prerequisites: Modeling Uncertainity of Environment Stochastic Policy
Markov Decision Process Prerequisites: Modeling Uncertainity of Environment Stochastic Policy 2
Markov Decision Process Prerequisites: Modeling Uncertainity of Environment Value Functions
Markov Decision Process Prerequisites: Running Averages
Markov Decision Process Prerequisites: Running Averages 2
Markov Decision Process Prerequisites: Running Averages as Temporal Difference
Markov Decision Process Prerequisites: Activity
Elements of Markov Decision Process: Markov Property
Elements of Markov Decision Process: State Space
Elements of Markov Decision Process: Action Space
Elements of Markov Decision Process: Transition Probabilities
Elements of Markov Decision Process: Reward Function
Elements of Markov Decision Process: Discount Factor
Elements of Markov Decision Process: Summary
Elements of Markov Decision Process: Activity
More on Reword: MOR Quiz 1
More on Reword: MOR Quiz Solution 1
More on Reword: MOR Quiz 2
More on Reword: MOR Quiz Solution 2
More on Reword: MOR Reward Scaling
More on Reword: MOR Infinite Horizons
More on Reword: MOR Quiz 3
More on Reword: MOR Quiz Solution 3
Solving MDP: MDP Recap
Solving MDP: Value Functions
Solving MDP: Optimal Value Function
Solving MDP: Optimal Policy
Solving MDP: Balman Equation
Solving MDP: Value Iteration
Solving MDP: Value Iteration Quiz
Solving MDP: Value Iteration Quiz Gamma Missing
Solving MDP: Value Iteration Solution
Solving MDP: Problems of Value Iteration
Solving MDP: Policy Evaluation
Solving MDP: Policy Evaluation 2
Solving MDP: Policy Evaluation 3
Solving MDP: Policy Evaluation Closed Form Solution
Solving MDP: Policy Iteration
Solving MDP: State Action Values
Solving MDP: V and Q Comparisons
Value Approximation: What does it mean that MDP is Unknown
Value Approximation: Why Transition Probabilities are Important
Value Approximation: Model Based Solutions
Value Approximation: Model Free Solutions
Value Approximation: Monte-Carlo Learning
Value Approximation: Monte-Carlo Learning Example
Value Approximation: Monte-Carlo Learning Limitations
Temporal Differencing-Q Learning: Running Average
Temporal Differencing-Q Learning: Learning Rate
Temporal Differencing-Q Learning: Learning Equation
Temporal Differencing-Q Learning: TD Algorithm
Temporal Differencing-Q Learning: Exploration vs Exploitation
Temporal Differencing-Q Learning: Epsilon Greedy Policy
Temporal Differencing-Q Learning: SARSA
Temporal Differencing-Q Learning: Q-Learning
Temporal Differencing-Q Learning: Q-Learning Implementation for MAPROVER Clipped
TD Lambda: N Step Look a Head
TD Lambda: Formulation
TD Lambda: Values
TD Lambda: TD Eligibility Trace
TD Lambda: TD Q-Learning TD Lambda
Project Frozenlake (Open AI Gym): Frozenlake 1
Project Frozenlake (Open AI Gym): Frozenlake Implementation
Conclusion

Screenshots

Data Science & Machine Learning(Theory+Projects)A-Z 90 HOURS - Screenshot_01Data Science & Machine Learning(Theory+Projects)A-Z 90 HOURS - Screenshot_02Data Science & Machine Learning(Theory+Projects)A-Z 90 HOURS - Screenshot_03Data Science & Machine Learning(Theory+Projects)A-Z 90 HOURS - Screenshot_04

Reviews

Alperen
September 12, 2023
Some of the codes as well as some of the math need revising. However, concepts are ease to understand and explained fairly well.
Hitesh
August 25, 2023
The Overall course is good but Instructor use too many filler words and repeat sentences too often that caught my concentration. This is not a work of person who has 15 years teaching experience. Sometime the Instructor stuck with the problem and it takes 5-10 minutes . Also the Instructor can explain little more in detail. I am Disappointed that It didn't match my expectations and now I am struggling to understand the concept.
Abd
July 22, 2023
excellent explaining but you need to make a part concerning transformers and a project on them with more about chatgpt
Shivratan
April 29, 2023
100+ hour course , to much mathematical information this create headache , it is good to with sorter course which focuses on main fuctions
Andriy
October 25, 2022
Too much useless blah-blah-blah, no useful info. This course wastes my time on reviewing obvious things that have no values for anybody, even beginners.
Wiktor
September 13, 2022
Good course if you dont tried programing its all you need but if you know something about python the start can be really boring and slow
Jean
April 24, 2022
C'est bonne mise en relief de ce qu'on fera dans le cours, ça fait rêver ! J'espère retrouver tout ce qui a été dit en sommaire! Merci beaucoup !
Hicham
December 30, 2021
Compared to the videos that I watched it is a boring course, the voice is not clear, a music at the beginning and each end of the video, weak explanation and less programming. I do not recommend at all
VINAYAK
November 18, 2021
Explanation is not clear and arrangements of topics is poor . easy topics are being made Hard.. I think its for beginners
Jesús
November 7, 2021
A mi parecer el contenido es bastante útil y el curso se desarrolla de una forma que permite comenzar de forma simple y comprensible pero escalando en nivel hasta llega a temas mas complejos.
Abubakar
September 26, 2021
Pretty disappointing. They have got preview from someone else and someone else is delivering the lectures. There are too too many filler words and gaps that throw the attention away. Can't concentrate enough in even single lecture. If the filler words and repetitions are removed the course will reduce to 30 hours. Expected a lot but can't concentrate enough. Gaps and filler words suck
Dipanjan
September 17, 2021
The structure of this course is really well thought. I am sure as days progress, this course will be in the top 5 of the best data science courses in udemy.
Rahmon
September 4, 2021
I didn't enjoy the Machine Learning Module much because the course was taught abstractly. Real life data should have been used rather than synthetic data. It didn't meet my expectation.
Jaime
August 27, 2021
I really love this course. A perfect balance of math, programming and technics. The perfect umbrella for ML is probability and it is covered in the best way in this course. I think that this is the point that I more appreciate.
David
July 8, 2021
Excellent course. I had to search Kaggle to find the covid-19 dataset, but I found it. I am only through the first 94 lessons, but it is excellent so far. I am looking forward to the rest. Lecture 108 was as clear as mud.

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3582994
udemy ID
10/21/2020
course created date
12/8/2020
course indexed date
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course submited by