Machine Learning Foundations Build Expert - Level AI Models

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Free Download Machine Learning Foundations Build Expert-Level AI Models
Published 12/2025
Created by Meritshot Academy
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All | Genre: eLearning | Language: English | Duration: 140 Lectures ( 17h 28m ) | Size: 13.6 GB

Master advanced ML concepts through clear, practical lessons designed for learners of all backgrounds.
What you'll learn
Understand and apply Python programming for data and ML tasks.
Grasp essential statistical concepts used in real-world analysis.
Perform hypothesis testing to draw meaningful data-driven conclusions.
Clean, transform, and prepare raw datasets for modeling.
Analyze and interpret data using modern analytical techniques.
Create insightful data visualizations using popular Python libraries.
Build, train, and evaluate machine learning models from scratch.
Apply ML techniques to real-world projects and make accurate predictions.
Requirements
No prior programming or machine learning experience required
Basic computer skills and the ability to install software
A laptop or computer with internet access
Willingness to learn and practice through hands-on exercises
Curiosity about data, AI, and how machine learning works
Description
Machine Learning Foundations: Build Expert-Level AI Models is a comprehensive, beginner-friendly program designed to take you from fundamental concepts to advanced machine learning techniques. Whether you're new to programming or looking to strengthen your AI skillset, this course provides a complete, structured path to mastering data-driven decision-making and machine learning modeling.You'll start by learning Python programming, the essential language for modern AI development. Next, you'll build a strong mathematical foundation through statistics and hypothesis testing, giving you the analytical mindset required to interpret data with confidence. As you progress, you'll gain hands-on experience in data analysis, data visualization, and data cleaning-key skills every ML professional relies on to prepare and understand real-world datasets.What You Will Learn:Write clean, efficient Python code for data and ML tasksUnderstand core statistical concepts for data analysisPerform hypothesis testing to validate business decisionsAnalyze datasets using modern analytical techniquesVisualize data using industry-standard librariesClean and prepare messy, real-world dataBuild, evaluate, and deploy machine learning modelsFinally, you'll dive deep into machine learning, where you'll learn how to design, train, evaluate, and optimize models used in various industries today. Every module is practical, engaging, and designed for learners of all backgrounds.Take your first step into the exciting world of data science today. Enroll now and unlock your potential!
Who this course is for
Beginners who want to enter the field of data science or machine learning
Students and professionals looking to build a strong ML foundation
Programmers who want to expand their skills into AI and data analysis
Business analysts and decision-makers seeking data-driven insights
Job seekers preparing for roles in data science, AI, or analytics
Anyone curious about how machine learning works and wants hands-on experience
Entrepreneurs and innovators who want to use ML in real-world projects
Individuals looking to build practical, industry-ready AI skills
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14.41 GB | 19min 3s | mp4 | 1920X1080 | 16:9
Genre:eLearning |Language:English​

Files Included :

Spoiler

1 Real world use cases of Python.mp4 (53.2 MB)
2 Installation of Anaconda for Windows and macOS.mp4 (97.94 MB)
3 Introduction to Variables.mp4 (128.33 MB)
4 Introduction to Data Types and Type Casting.mp4 (74.31 MB)
5 Scope of Variables.mp4 (165.09 MB)
6 Introduction to Operators.mp4 (414.24 MB)
1 Introduction to Lists and Tuples.mp4 (149.84 MB)
2 Introduction to Sets and Dictionaries.mp4 (209.8 MB)
3 Introduction to Stacks and Queues.mp4 (38.16 MB)
4 Introduction to Space and Time Complexity.mp4 (215.44 MB)
5 Introduction to Sorting Algorithms.mp4 (144.26 MB)
6 Introduction to Searching Algorithms.mp4 (143.52 MB)
1 Introduction to Parameters and Arguments.mp4 (249.7 MB)
2 Introduction to Python Modules.mp4 (97.88 MB)
3 Introduction to Filter, Map, and Zip Functions.mp4 (228.69 MB)
4 Introduction to List, Set and Dictionary Comprehensions.mp4 (173.12 MB)
5 Introduction to Lambda Functions.mp4 (115.18 MB)
6 Introduction to Analytical and Aggregate Functions.mp4 (137.68 MB)
1 Introduction to Strings.mp4 (95.24 MB)
2 Introduction to Important String Functions.mp4 (179.55 MB)
3 Introduction to String Formatting and User Input.mp4 (138.92 MB)
4 Introduction to Meta Characters.mp4 (126.12 MB)
5 Introduction to Built-in Functions for Regular Expressions.mp4 (228.12 MB)
6 Special Characters and Sets for Regular Expressions.mp4 (124.42 MB)
1 Introduction to Conditional Statements.mp4 (69.07 MB)
2 Introduction to For Loops.mp4 (38.87 MB)
3 Introduction to While Loops.mp4 (55.87 MB)
4 Introduction to Break and Continue.mp4 (30.86 MB)
5 Using Conditional Statements in Loops.mp4 (62.7 MB)
6 Nested Loops and Conditional Statements.mp4 (86.09 MB)
1 Introduction to OOPs Concept.mp4 (93.17 MB)
2 Introduction to Inheritance.mp4 (34.89 MB)
3 Introduction to Encapsulation.mp4 (59.16 MB)
4 Introduction to Polymorphism.mp4 (88.22 MB)
5 Introduction to Date and Time Class.mp4 (129.25 MB)
6 Introduction to TimeDelta Class.mp4 (88.97 MB)
1 Introduction to Statistics and its importance.mp4 (166.86 MB)
2 Explain the role of statistics in data analysis.mp4 (40.08 MB)
3 Introduction to Python for Statistical Analysis.mp4 (42.17 MB)
1 Types of Data.mp4 (134.31 MB)
2 Measures of Central Tendency.mp4 (62.69 MB)
3 Measures of Spread.mp4 (79 MB)
4 Measures of Dependence.mp4 (45.1 MB)
5 Measures of Shape and Position.mp4 (158.73 MB)
6 Measures of Standard Scores.mp4 (83.91 MB)
1 Introduction to Basic Probability.mp4 (189.72 MB)
2 Introduction to Set Theory.mp4 (63.26 MB)
3 Introduction to Conditional Probability.mp4 (82.09 MB)
4 Introduction to Bayes Theorem.mp4 (41.36 MB)
5 Introduction to Permutations and Combinations.mp4 (52.04 MB)
6 Introduction to Random Variables.mp4 (87.76 MB)
7 Introduction to Probability Distribution Functions.mp4 (71.91 MB)
1 Introduction to Normal Distribution.mp4 (125.7 MB)
2 Introduction to Skewness and Kurtosis.mp4 (153.01 MB)
3 Introduction to Statistical Transformations.mp4 (226.77 MB)
4 Introduction to Sample and Population Mean.mp4 (78.19 MB)
5 Introduction to Central Limit Theorem.mp4 (24.9 MB)
6 Introduction to Bias and Variance.mp4 (36.68 MB)
7 Introduction to Maximum Likelihood Estimation.mp4 (58.91 MB)
8 Introduction to Confidence Intervals.mp4 (34.89 MB)
9 Introduction to Correlations.mp4 (124.12 MB)
10 Introduction to Sampling Methods.mp4 (116.87 MB)
1 Fundamentals of Hypothesis Testing.mp4 (36.12 MB)
2 Introduction to T Tests.mp4 (213.03 MB)
3 Introduction to Z Tests.mp4 (93.87 MB)
4 Introduction to Chi Squared Tests.mp4 (134.63 MB)
5 Introduction to Anova Tests.mp4 (59.13 MB)
1 Introduction to Numpy Arrays.mp4 (134.42 MB)
2 Introduction to Numpy Operations.mp4 (209.09 MB)
3 Introduction to Pandas.mp4 (42.03 MB)
4 Introduction to Series and DataFrames.mp4 (185.68 MB)
5 Reading CSV and JSON Data using Pandas.mp4 (67.29 MB)
6 Analyzing the Data using Pandas.mp4 (57.89 MB)
1 Indexing, slicing, and Filtering Data.mp4 (269.83 MB)
2 Merging and Concatenation using Pandas.mp4 (132.87 MB)
3 Correlation and Plotting using Pandas.mp4 (243.02 MB)
4 Introduction to Lambda, Map and Apply Functions.mp4 (90.01 MB)
5 Introduction to Grouping Operations using Pandas.mp4 (166.49 MB)
6 Introduction to Cross Tabulation using Pandas.mp4 (65.02 MB)
7 Introduction to Filtering Operations using Pandas.mp4 (83.17 MB)
8 Interactive Grouping and Filtering Operations.mp4 (176.34 MB)
1 Factors for good Data Visualization.mp4 (94.17 MB)
2 Introduction to Univariate Data Visualizations.mp4 (93.26 MB)
3 Introduction to Bivariate Data Visualizations.mp4 (25.14 MB)
4 Plotting two Categorical Variables.mp4 (73.41 MB)
5 Introduction to Multivariate Data Visualizations.mp4 (76.1 MB)
6 Introduction to Heatmaps and Pairplots.mp4 (143.87 MB)
8 Colorscales, Facet Grids, and Sub plots.mp4 (348.12 MB)
9 Introduction to 3D Data Visualization.mp4 (190.23 MB)
10 Introduction to Interactive Data Visualization.mp4 (141.65 MB)
11 Introduction to Maps using Plotly.mp4 (156.97 MB)
12 Introduction to Funnel and Gantt Charts using Plotly.mp4 (146.52 MB)
13 Introduction to Animated Data Visualizations using Plotly.mp4 (205.19 MB)
1 Causes and Impact of Missing Values.mp4 (48.01 MB)
2 Types of Missing Values.mp4 (89.18 MB)
3 When to delete the Missing Values from Data.mp4 (257.15 MB)
4 Imputing Missing Values with Statistical Values.mp4 (145 MB)
5 Imputing Missing Values with Business Logic.mp4 (26.68 MB)
6 Impact of Outliers on ML Models.mp4 (44.84 MB)
7 Dealing with Outliers in an dataset.mp4 (219.33 MB)
1 Introduction to Label and Ordinal Encoding.mp4 (307.33 MB)
2 Introduction to Binary and BaseN Encoding.mp4 (217.94 MB)
3 Introduction to Target Encoding.mp4 (157.51 MB)
1 Introduction to reindex, set index, reset index, and sort index Functions.mp4 (56.49 MB)
2 Introduction to Replace and Droplevel Functions.mp4 (21.04 MB)
3 Introduction to Split and Strip Functions.mp4 (256.98 MB)
4 Introduction to Stack and Unstack Functions.mp4 (76.44 MB)
5 Introduction to Melt, Explode, and Squeeze Functions.mp4 (56.7 MB)
6 Introduction to at time and between time Functions.mp4 (59.11 MB)
7 Introduction to nlargest and nsmallest Functions.mp4 (49.21 MB)
1 Determining how to drop unnecessary columns.mp4 (98.18 MB)
2 Decomposing the Date and Time Features.mp4 (15.85 MB)
3 Decomposing the Categorical Features.mp4 (35.45 MB)
4 Binning the Numerical Features.mp4 (27.44 MB)
5 Aggregation of Features.mp4 (57.48 MB)
1 How Industries are using Machine Learning.mp4 (20.54 MB)
2 Classification vs Regression.mp4 (21.38 MB)
1 Data Transformation with Linear regression.mp4 (74.13 MB)
2 K folds cross validation.mp4 (105.15 MB)
3 Analyzing the Performance of Regression model.mp4 (36.31 MB)
4 R2 and Adjusted R2 intuition.mp4 (30.37 MB)
5 MAE , RMSE , R2 and Adj R2 in code.mp4 (43.34 MB)
6 Industry relevance of Linear regression.mp4 (15.27 MB)
1 What is Regularization and why is it important.mp4 (36.46 MB)
2 Getting the intuition of Lasso, Ridge and Elastic Net.mp4 (60.55 MB)
3 Applying Lasso, Ridge and Elastic Net in sklearn.mp4 (76.14 MB)
1 Introduction to Logistic Regression.mp4 (59.49 MB)
2 Implementing Logistic Regression using Sklearn.mp4 (167.53 MB)
3 Feature selection using RFECV.mp4 (50.52 MB)
4 Hyperparameter tuning using Grid search.mp4 (260.68 MB)
5 Applying Cross validation.mp4 (35.42 MB)
6 Using accuracy score to analyze the performance of model.mp4 (49.55 MB)
7 Industry Relevance of Logistic Regression.mp4 (13.78 MB)
1 The Kernel Tricks for Support Vector Machines.mp4 (23.67 MB)
2 Implementing Support Vector Machines using Sklearn.mp4 (89.29 MB)
3 Introduction to K Nearest Neighbors.mp4 (19.29 MB)
4 Implementing K Nearest Neighbors using Sklearn.mp4 (22.87 MB)
5 Introduction to Naive and Gaussian Naive Bayes algorithm.mp4 (46.72 MB)
6 Implementing Naive Bayes using Sklearn.mp4 (34.43 MB)
7 When should we apply SVM, KNN, Naive Bayes algorithm.mp4 (18.78 MB)

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