Data Science & Machine Learning(Theory+Projects)A-Z 90 HOURS
Data Science & Machine Learning(Theory+Projects)A-Z 90 HOURS, available at $69.99, has an average rating of 4.43, with 735 lectures, based on 794 reviews, and has 6659 subscribers.
You will learn about 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. This course is ideal for individuals who are • People who want to enter the Machine Learning field. or • People who want to become perfect in their data speak. or • People who want to learn Data Science & Machine Learning along with its implementation in realistic projects. or • People who want to learn Data Science & Machine Learning with real datasets in Data Science. or • People from a non-engineering background who want to enter the Data Science field. or • Individuals who are passionate about numbers and programming. or • Beginners in Data Science field. or • Business Analysts. It is particularly useful for • People who want to enter the Machine Learning field. or • People who want to become perfect in their data speak. or • People who want to learn Data Science & Machine Learning along with its implementation in realistic projects. or • People who want to learn Data Science & Machine Learning with real datasets in Data Science. or • People from a non-engineering background who want to enter the Data Science field. or • Individuals who are passionate about numbers and programming. or • Beginners in Data Science field. or • Business Analysts.
Enroll now: Data Science & Machine Learning(Theory+Projects)A-Z 90 HOURS
Summary
Title: Data Science & Machine Learning(Theory+Projects)A-Z 90 HOURS
Price: $69.99
Average Rating: 4.43
Number of Lectures: 735
Number of Published Lectures: 735
Number of Curriculum Items: 735
Number of Published Curriculum Objects: 735
Original Price: $199.99
Quality Status: approved
Status: Live
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.
Who Should Attend
- • People who want to enter the Machine Learning field.
- • People who want to become perfect in their data speak.
- • People who want to learn Data Science & Machine Learning along with its implementation in realistic projects.
- • 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.
- • Individuals who are passionate about numbers and programming.
- • Beginners in Data Science field.
- • Business Analysts.
Target Audiences
- • People who want to enter the Machine Learning field.
- • People who want to become perfect in their data speak.
- • People who want to learn Data Science & Machine Learning along with its implementation in realistic projects.
- • 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.
- • Individuals who are passionate about numbers and programming.
- • Beginners in Data Science field.
- • Business Analysts.
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 Learningat 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 Learningusing 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 Learningwith 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.
Course Curriculum
Chapter 1: Introduction to the Course
Lecture 1: Introduction to Courses and Instructor
Lecture 2: Introduction to Instructor
Lecture 3: Introduction to the Course: Feedbacks and Review
Lecture 4: Links for the Course's Materials and Codes
Chapter 2: Basics for Data Science: Python for Data Science and Data Analysis
Lecture 1: Links for the Course's Materials and Codes
Lecture 2: Introduction to the Course: Focus of the Course-Part 1
Lecture 3: Introduction to the Course: Focus of the Course-Part 2
Lecture 4: Basics of Programming: Understanding the Algorithm
Lecture 5: Basics of Programming: FlowCharts and Pseudocodes
Lecture 6: Basics of Programming: Example of Algorithms- Making Tea Problem
Lecture 7: Basics of Programming: Example of Algorithms-Searching Minimun
Lecture 8: Basics of Programming: Example of Algorithms-Sorting Problem
Lecture 9: Basics of Programming: Sorting Problem in Python
Lecture 10: Why Python and Jupyter Notebook: Why Python
Lecture 11: Why Python and Jupyter Notebook: Why Jupyter Notebooks
Lecture 12: Installation of Anaconda and IPython Shell: Installing Python and Jupyter Anacon
Lecture 13: Installation of Anaconda and IPython Shell: Your First Python Code- Hello World
Lecture 14: Installation of Anaconda and IPython Shell: Coding in IPython Shell
Lecture 15: Variable and Operator: Variables
Lecture 16: Variable and Operator: Operators
Lecture 17: Variable and Operator: Variable Name Quiz
Lecture 18: Variable and Operator: Bool Data Type in Python
Lecture 19: Variable and Operator: Comparison in Python
Lecture 20: Variable and Operator: Combining Comparisons in Python
Lecture 21: Variable and Operator: Combining Comparisons Quiz
Lecture 22: Python Useful function: Python Function- Round
Lecture 23: Python Useful function: Python Function- Divmod
Lecture 24: Python Useful function: Python Function- Is instance and PowFunctions
Lecture 25: Python Useful function: Python Function- Input
Lecture 26: Control Flow in Python: If Python Condition
Lecture 27: Control Flow in Python: if Elif Else Python Conditions
Lecture 28: Control Flow in Python: More on if Elif Else Python Conditions
Lecture 29: Control Flow in Python: Indentations
Lecture 30: Control Flow in Python: Comments and Problem Solving Practice With If
Lecture 31: Control Flow in Python: While Loop
Lecture 32: Control Flow in Python: While Loop break Continue
Lecture 33: Control Flow in Python: For Loop
Lecture 34: Control Flow in Python: Else In For Loop
Lecture 35: Control Flow in Python: Loops Practice-Sorting Problem
Lecture 36: Function and Module in Python: Functions in Python
Lecture 37: Function and Module in Python: DocString
Lecture 38: Function and Module in Python: Input Arguments
Lecture 39: Function and Module in Python: Multiple Input Arguments
Lecture 40: Function and Module in Python: Ordering Multiple Input Arguments
Lecture 41: Function and Module in Python: Output Arguments and Return Statement
Lecture 42: Function and Module in Python: Function Practice-Output Arguments and Return Statement
Lecture 43: Function and Module in Python: Variable Number of Input Arguments
Lecture 44: Function and Module in Python: Variable Number of Input Arguments as Dictionary
Lecture 45: Function and Module in Python: Default Values in Python
Lecture 46: Function and Module in Python: Modules in Python
Lecture 47: Function and Module in Python: Making Modules in Python
Lecture 48: Function and Module in Python: Function Practice-Sorting List in Python
Lecture 49: String in Python: Strings
Lecture 50: String in Python: Multi Line Strings
Lecture 51: String in Python: Indexing Strings
Lecture 52: String in Python: String Methods
Lecture 53: String in Python: String Escape Sequences
Lecture 54: Data Structure (List, Tuple, Set, Dictionary): Introduction to Data Structure
Lecture 55: Data Structure (List, Tuple, Set, Dictionary): Defining and Indexing
Lecture 56: Data Structure (List, Tuple, Set, Dictionary): Insertion and Deletion
Lecture 57: Data Structure (List, Tuple, Set, Dictionary): Python Practice-Insertion and Deletion
Lecture 58: Data Structure (List, Tuple, Set, Dictionary): Deep Copy or Reference Slicing
Lecture 59: Data Structure (List, Tuple, Set, Dictionary): Exploring Methods Using TAB Completion
Lecture 60: Data Structure (List, Tuple, Set, Dictionary): Data Structure Abstract Ways
Lecture 61: Data Structure (List, Tuple, Set, Dictionary): Data Structure Practice
Lecture 62: NumPy for Numerical Data Processing: Introduction to NumPy
Lecture 63: NumPy for Numerical Data Processing: NumPy Dimensions
Lecture 64: NumPy for Numerical Data Processing: NumPy Shape, Size and Bytes
Lecture 65: NumPy for Numerical Data Processing: Arange, Random and Reshape-Part 1
Lecture 66: NumPy for Numerical Data Processing: Arange, Random and Reshape-Part 2
Lecture 67: NumPy for Numerical Data Processing: Slicing-Part 1
Lecture 68: NumPy for Numerical Data Processing: Slicing-Part 2
Lecture 69: NumPy for Numerical Data Processing: NumPy Masking
Lecture 70: NumPy for Numerical Data Processing: NumPy BroadCasting and Concatination
Lecture 71: NumPy for Numerical Data Processing: NumPy ufuncs Speed Test
Lecture 72: Pandas for Data Manipulation: Introduction to Pandas
Lecture 73: Pandas for Data Manipulation: Pandas Series
Lecture 74: Pandas for Data Manipulation: Pandas Data Frame
Lecture 75: Pandas for Data Manipulation: Pandas Missing Values
Lecture 76: Pandas for Data Manipulation: Pandas .loc and .iloc
Lecture 77: Pandas for Data Manipulation: Pandas Practice-Using COVID19 Data -Part 1
Lecture 78: Pandas for Data Manipulation: Pandas Practice-Using COVID19 Data -Part 2
Lecture 79: Matplotlib, Seaborn, and Bokeh for Data Visualization: Introduction to Matplotlib
Lecture 80: Matplotlib, Seaborn, and Bokeh for Data Visualization:Trend Analysis COVID19
Lecture 81: Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Vs. Matplotlib Style
Lecture 82: Matplotlib, Seaborn, and Bokeh for Data Visualization: Histograms Kdeplot
Lecture 83: Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Pairplot and Jointplot
Lecture 84: Matplotlib, Seaborn, and Bokeh for Data Visualization: Seaborn Pairplot using Iris Data
Lecture 85: Matplotlib, Seaborn, and Bokeh for Data Visualization: Introduction to Bokeh
Lecture 86: Matplotlib, Seaborn, and Bokeh for Data Visualization: Bokeh Gridplot
Lecture 87: Scikit-Learn for Machine Learning: Introduction to Scikit-Learn
Lecture 88: Scikit-Learn for Machine Learning: Scikit-Learn for Linear Regression
Lecture 89: Scikit-Learn for Machine Learning: Scikit-Learn for SVM and Random Forests
Lecture 90: Scikit-Learn for Machine Learning: ScikitLearn- Trend Analysis COVID19
Lecture 91: Scikit-Learn for Machine Learning: THANK YOU Bonus Video
Chapter 3: Basics for Data Science: Data Understanding and Data Visualization with Python
Lecture 1: Links for the Course's Materials and Codes
Lecture 2: Introduction to the Course: Focus of the Course
Instructors
-
AI Sciences
AI Experts & Data Scientists |4+ Rated | 168+ Countries -
AI Sciences Team
Support Team AI Sciences
Rating Distribution
- 1 stars: 27 votes
- 2 stars: 21 votes
- 3 stars: 75 votes
- 4 stars: 256 votes
- 5 stars: 415 votes
Frequently Asked Questions
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